diff --git a/.github/.idea/.gitignore b/.github/.idea/.gitignore index 26d3352..eaf91e2 100644 --- a/.github/.idea/.gitignore +++ b/.github/.idea/.gitignore @@ -1,3 +1,3 @@ -# Default ignored files -/shelf/ -/workspace.xml +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.github/.idea/Pneumonia AI Dev.iml b/.github/.idea/Pneumonia AI Dev.iml index 58a6887..114c2a9 100644 --- a/.github/.idea/Pneumonia AI Dev.iml +++ b/.github/.idea/Pneumonia AI Dev.iml @@ -1,15 +1,15 @@ - - - - - - - - - - - - + + + + + + + + + + + + \ No newline at end of file diff --git a/.github/.idea/inspectionProfiles/Project_Default.xml b/.github/.idea/inspectionProfiles/Project_Default.xml index f6b8fe4..8fce6a1 100644 --- a/.github/.idea/inspectionProfiles/Project_Default.xml +++ b/.github/.idea/inspectionProfiles/Project_Default.xml @@ -1,15 +1,15 @@ - - - + + + \ No newline at end of file diff --git a/.github/.idea/inspectionProfiles/profiles_settings.xml b/.github/.idea/inspectionProfiles/profiles_settings.xml index 105ce2d..20fc29e 100644 --- a/.github/.idea/inspectionProfiles/profiles_settings.xml +++ b/.github/.idea/inspectionProfiles/profiles_settings.xml @@ -1,6 +1,6 @@ - - - + + + \ No newline at end of file diff --git a/.github/.idea/misc.xml b/.github/.idea/misc.xml index 2ce26da..a41c242 100644 --- a/.github/.idea/misc.xml +++ b/.github/.idea/misc.xml @@ -1,7 +1,7 @@ - - - - - + + + + + \ No newline at end of file diff --git a/.github/.idea/modules.xml b/.github/.idea/modules.xml index bc7318f..29389fc 100644 --- a/.github/.idea/modules.xml +++ b/.github/.idea/modules.xml @@ -1,8 +1,8 @@ - - - - - - - + + + + + + + \ No newline at end of file diff --git a/.github/.idea/vcs.xml b/.github/.idea/vcs.xml index 35eb1dd..c8397c9 100644 --- a/.github/.idea/vcs.xml +++ b/.github/.idea/vcs.xml @@ -1,6 +1,6 @@ - - - - - + + + + + \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index dd84ea7..6867cf8 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,38 +1,38 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: '' -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Desktop (please complete the following information):** - - OS: [e.g. iOS] - - Browser [e.g. chrome, safari] - - Version [e.g. 22] - -**Smartphone (please complete the following information):** - - Device: [e.g. iPhone6] - - OS: [e.g. iOS8.1] - - Browser [e.g. stock browser, safari] - - Version [e.g. 22] - -**Additional context** -Add any other context about the problem here. +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index bbcbbe7..72718d5 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,20 +1,20 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: '' -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 61a7f93..3883126 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -1,81 +1,81 @@ -# For most projects, this workflow file will not need changing; you simply need -# to commit it to your repository. -# -# You may wish to alter this file to override the set of languages analyzed, -# or to provide custom queries or build logic. -# -# ******** NOTE ******** -# We have attempted to detect the languages in your repository. Please check -# the `language` matrix defined below to confirm you have the correct set of -# supported CodeQL languages. -# -name: "CodeQL" - -on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] - schedule: - - cron: '24 13 * * 2' - -jobs: - analyze: - name: Analyze - # Runner size impacts CodeQL analysis time. To learn more, please see: - # - https://gh.io/recommended-hardware-resources-for-running-codeql - # - https://gh.io/supported-runners-and-hardware-resources - # - https://gh.io/using-larger-runners - # Consider using larger runners for possible analysis time improvements. - runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} - timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }} - permissions: - actions: read - contents: read - security-events: write - - strategy: - fail-fast: false - matrix: - language: [ 'python' ] - # CodeQL supports [ 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift' ] - # Use only 'java-kotlin' to analyze code written in Java, Kotlin or both - # Use only 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both - # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - # Initializes the CodeQL tools for scanning. - - name: Initialize CodeQL - uses: github/codeql-action/init@v3 - with: - languages: ${{ matrix.language }} - # If you wish to specify custom queries, you can do so here or in a config file. - # By default, queries listed here will override any specified in a config file. - # Prefix the list here with "+" to use these queries and those in the config file. - - # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs - # queries: security-extended,security-and-quality - - - # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift). - # If this step fails, then you should remove it and run the build manually (see below) - - name: Autobuild - uses: github/codeql-action/autobuild@v3 - - # ℹ️ Command-line programs to run using the OS shell. - # πŸ“š See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun - - # If the Autobuild fails above, remove it and uncomment the following three lines. - # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. - - # - run: | - # echo "Run, Build Application using script" - # ./location_of_script_within_repo/buildscript.sh - - - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v3 - with: - category: "/language:${{matrix.language}}" +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL" + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + schedule: + - cron: '24 13 * * 2' + +jobs: + analyze: + name: Analyze + # Runner size impacts CodeQL analysis time. To learn more, please see: + # - https://gh.io/recommended-hardware-resources-for-running-codeql + # - https://gh.io/supported-runners-and-hardware-resources + # - https://gh.io/using-larger-runners + # Consider using larger runners for possible analysis time improvements. + runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} + timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }} + permissions: + actions: read + contents: read + security-events: write + + strategy: + fail-fast: false + matrix: + language: [ 'python' ] + # CodeQL supports [ 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift' ] + # Use only 'java-kotlin' to analyze code written in Java, Kotlin or both + # Use only 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both + # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + + # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs + # queries: security-extended,security-and-quality + + + # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v3 + + # ℹ️ Command-line programs to run using the OS shell. + # πŸ“š See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + + # If the Autobuild fails above, remove it and uncomment the following three lines. + # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. + + # - run: | + # echo "Run, Build Application using script" + # ./location_of_script_within_repo/buildscript.sh + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/dependency-review.yml b/.github/workflows/dependency-review.yml index bdc93c3..7673349 100644 --- a/.github/workflows/dependency-review.yml +++ b/.github/workflows/dependency-review.yml @@ -1,20 +1,20 @@ -# Dependency Review Action -# -# This Action will scan dependency manifest files that change as part of a Pull Request, surfacing known-vulnerable versions of the packages declared or updated in the PR. Once installed, if the workflow run is marked as required, PRs introducing known-vulnerable packages will be blocked from merging. -# -# Source repository: https://github.com/actions/dependency-review-action -# Public documentation: https://docs.github.com/en/code-security/supply-chain-security/understanding-your-software-supply-chain/about-dependency-review#dependency-review-enforcement -name: 'Dependency Review' -on: pull_request - -permissions: - contents: read - -jobs: - dependency-review: - runs-on: ubuntu-latest - steps: - - name: 'Checkout Repository' - uses: actions/checkout@v3 - - name: 'Dependency Review' - uses: actions/dependency-review-action@v3 +# Dependency Review Action +# +# This Action will scan dependency manifest files that change as part of a Pull Request, surfacing known-vulnerable versions of the packages declared or updated in the PR. Once installed, if the workflow run is marked as required, PRs introducing known-vulnerable packages will be blocked from merging. +# +# Source repository: https://github.com/actions/dependency-review-action +# Public documentation: https://docs.github.com/en/code-security/supply-chain-security/understanding-your-software-supply-chain/about-dependency-review#dependency-review-enforcement +name: 'Dependency Review' +on: pull_request + +permissions: + contents: read + +jobs: + dependency-review: + runs-on: ubuntu-latest + steps: + - name: 'Checkout Repository' + uses: actions/checkout@v3 + - name: 'Dependency Review' + uses: actions/dependency-review-action@v3 diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 4677434..8daa395 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -1,16 +1,16 @@ -name: Greetings - -on: [pull_request_target, issues] - -jobs: - greeting: - runs-on: ubuntu-latest - permissions: - issues: write - pull-requests: write - steps: - - uses: actions/first-interaction@v1 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - issue-message: "Message that will be displayed on users' first issue" - pr-message: "Message that will be displayed on users' first pull request" +name: Greetings + +on: [pull_request_target, issues] + +jobs: + greeting: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + issue-message: "Message that will be displayed on users' first issue" + pr-message: "Message that will be displayed on users' first pull request" diff --git a/.github/workflows/powershell.yml b/.github/workflows/powershell.yml index dfe5b5c..558017f 100644 --- a/.github/workflows/powershell.yml +++ b/.github/workflows/powershell.yml @@ -1,49 +1,49 @@ -# This workflow uses actions that are not certified by GitHub. -# They are provided by a third-party and are governed by -# separate terms of service, privacy policy, and support -# documentation. -# -# https://github.com/microsoft/action-psscriptanalyzer -# For more information on PSScriptAnalyzer in general, see -# https://github.com/PowerShell/PSScriptAnalyzer - -name: PSScriptAnalyzer - -on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] - schedule: - - cron: '23 6 * * 5' - -permissions: - contents: read - -jobs: - build: - permissions: - contents: read # for actions/checkout to fetch code - security-events: write # for github/codeql-action/upload-sarif to upload SARIF results - actions: read # only required for a private repository by github/codeql-action/upload-sarif to get the Action run status - name: PSScriptAnalyzer - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - - name: Run PSScriptAnalyzer - uses: microsoft/psscriptanalyzer-action@6b2948b1944407914a58661c49941824d149734f - with: - # Check https://github.com/microsoft/action-psscriptanalyzer for more info about the options. - # The below set up runs PSScriptAnalyzer to your entire repository and runs some basic security rules. - path: .\ - recurse: true - # Include your own basic security rules. Removing this option will run all the rules - includeRule: '"PSAvoidGlobalAliases", "PSAvoidUsingConvertToSecureStringWithPlainText"' - output: results.sarif - - # Upload the SARIF file generated in the previous step - - name: Upload SARIF results file - uses: github/codeql-action/upload-sarif@v2 - with: - sarif_file: results.sarif +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. +# +# https://github.com/microsoft/action-psscriptanalyzer +# For more information on PSScriptAnalyzer in general, see +# https://github.com/PowerShell/PSScriptAnalyzer + +name: PSScriptAnalyzer + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + schedule: + - cron: '23 6 * * 5' + +permissions: + contents: read + +jobs: + build: + permissions: + contents: read # for actions/checkout to fetch code + security-events: write # for github/codeql-action/upload-sarif to upload SARIF results + actions: read # only required for a private repository by github/codeql-action/upload-sarif to get the Action run status + name: PSScriptAnalyzer + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Run PSScriptAnalyzer + uses: microsoft/psscriptanalyzer-action@6b2948b1944407914a58661c49941824d149734f + with: + # Check https://github.com/microsoft/action-psscriptanalyzer for more info about the options. + # The below set up runs PSScriptAnalyzer to your entire repository and runs some basic security rules. + path: .\ + recurse: true + # Include your own basic security rules. Removing this option will run all the rules + includeRule: '"PSAvoidGlobalAliases", "PSAvoidUsingConvertToSecureStringWithPlainText"' + output: results.sarif + + # Upload the SARIF file generated in the previous step + - name: Upload SARIF results file + uses: github/codeql-action/upload-sarif@v2 + with: + sarif_file: results.sarif diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index a012b39..5f8b404 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -1,44 +1,44 @@ -# This workflow will install Python dependencies, run tests and lint with a single version of Python -# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python - -name: Python Test [main] - -on: - push: - branches: [ "main" ] - pull_request: - branches: [ "main" ] - schedule: - - cron: "0 0 */2 * *" - - -jobs: - Test_Global: - permissions: - contents: read - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: "3.10" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then - grep -v 'gpu-control' requirements.txt | xargs pip install - fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - # - name: Main code Updated - # run: | - # python Check_MCUS.py - +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python Test [main] + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + schedule: + - cron: "0 0 */2 * *" + + +jobs: + Test_Global: + permissions: + contents: read + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then + grep -v 'gpu-control' requirements.txt | xargs pip install + fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + # - name: Main code Updated + # run: | + # python Check_MCUS.py + diff --git a/.github/workflows/python-app_Alpha-b.yml b/.github/workflows/python-app_Alpha-b.yml index 3056e93..5cb4236 100644 --- a/.github/workflows/python-app_Alpha-b.yml +++ b/.github/workflows/python-app_Alpha-b.yml @@ -1,44 +1,44 @@ -# This workflow will install Python dependencies, run tests and lint with a single version of Python -# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python - -name: Python Test [Alpha-b] - -on: - push: - branches: ["Alpha-b"] - pull_request: - branches: ["Alpha-b"] - schedule: - - cron: "0 0 */2 * *" - - -jobs: - Test_Global: - permissions: - contents: read - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: "3.10" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then - grep -v 'gpu-control' requirements.txt | xargs pip install - fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - # - name: Main code Updated - # run: | - # python Check_MCUS.py +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python Test [Alpha-b] + +on: + push: + branches: ["Alpha-b"] + pull_request: + branches: ["Alpha-b"] + schedule: + - cron: "0 0 */2 * *" + + +jobs: + Test_Global: + permissions: + contents: read + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then + grep -v 'gpu-control' requirements.txt | xargs pip install + fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + # - name: Main code Updated + # run: | + # python Check_MCUS.py \ No newline at end of file diff --git a/.github/workflows/python-app_Beta-b.yml b/.github/workflows/python-app_Beta-b.yml index 0665828..a4acfd6 100644 --- a/.github/workflows/python-app_Beta-b.yml +++ b/.github/workflows/python-app_Beta-b.yml @@ -1,43 +1,43 @@ -# This workflow will install Python dependencies, run tests and lint with a single version of Python -# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python - -name: Python Test [Beta-b] - -on: - push: - branches: [ "Beta-b" ] - pull_request: - branches: [ "Beta-b" ] - schedule: - - cron: "0 0 */2 * *" - - -jobs: - Test_Global: - permissions: - contents: read - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: "3.10" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest - if [ -f requirements.txt ]; then - grep -v 'gpu-control' requirements.txt | xargs pip install - fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - # - name: Main code Updated - # run: | - # python Check_MCUS.py +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python Test [Beta-b] + +on: + push: + branches: [ "Beta-b" ] + pull_request: + branches: [ "Beta-b" ] + schedule: + - cron: "0 0 */2 * *" + + +jobs: + Test_Global: + permissions: + contents: read + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then + grep -v 'gpu-control' requirements.txt | xargs pip install + fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + # - name: Main code Updated + # run: | + # python Check_MCUS.py diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 8360efa..07b06c3 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -1,27 +1,27 @@ -# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time. -# -# You can adjust the behavior by modifying this file. -# For more information, see: -# https://github.com/actions/stale -name: Mark stale issues and pull requests - -on: - schedule: - - cron: '40 0 * * *' - -jobs: - stale: - - runs-on: ubuntu-latest - permissions: - issues: write - pull-requests: write - - steps: - - uses: actions/stale@v5 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - stale-issue-message: 'Stale issue message' - stale-pr-message: 'Stale pull request message' - stale-issue-label: 'no-issue-activity' - stale-pr-label: 'no-pr-activity' +# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time. +# +# You can adjust the behavior by modifying this file. +# For more information, see: +# https://github.com/actions/stale +name: Mark stale issues and pull requests + +on: + schedule: + - cron: '40 0 * * *' + +jobs: + stale: + + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + + steps: + - uses: actions/stale@v5 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: 'Stale issue message' + stale-pr-message: 'Stale pull request message' + stale-issue-label: 'no-issue-activity' + stale-pr-label: 'no-pr-activity' diff --git a/.gitignore b/.gitignore index 6173e13..7478a08 100644 --- a/.gitignore +++ b/.gitignore @@ -1,85 +1,85 @@ -# Exclude Visual Studio Code project settings -/.vscode - -# Exclude Visual Studio project settings -/.vs - -# Exclude database files -/Database - -# Exclude downloaded files -/download - -# Exclude model files -/models - -# Exclude Python cache files -__pycache__/ - -# Ignore all files in the logs/fit directory -logs/fit/* - -# Ignore all files in the Samples directory -Samples/* - -# Do not ignore directories in logs/fit that end with _STR -!logs/fit/*_STR/ - -# Do not ignore directories in Samples that end with _STR -!Samples/*_STR/ - -# Do not ignore .gz files in directories in Samples that end with _STR -!Samples/*_STR.gz - -# Exclude specific model weight files -/PAI_model_weights.h5 -/PAI_model_weights_BL.h5 -/PAI_model_T.h5 -/PAI_model_T_BL.h5 - -# Exclude cache files -/cache - -# Exclude build artifacts -/Build - -# Exclude validation files -/validation - -# Exclude project file for Pneumonia AI -/Pneumonia AI.pyproj - -# Exclude specific model file used in the CLI interface -/Interface/CLI/Data/PAI_model.h5 - -# Exclude temporary files -/TEMP.txt -/freearc1.tmp - -# Exclude logs and dataset files in the CLI interface -/Interface/CLI/Data/logs -/Interface/CLI/Data/dataset.npy - -# Exclude temporary Python version file in the CLI interface -/Interface/CLI/Data/Python Ver.tmp - -# Exclude specific model file used in the GUI interface -/Interface/GUI/Data/PAI_model.h5 - -# Exclude temporary Python version file in the GUI interface -/Interface/GUI/Data/Python Ver.tmp - -# venv -/venv_2 -/venv - -# Python Embed -/Interface/CLI/Data/Python Embed 3.10.11 -/Interface/CLI/Data/Use_Python_Embed.tmp -/Interface/CLI/Python.Embed.3.10.11.exe - -# GUI DEV -/Interface/GUI/GUI_DEV.cmd - -# Exclude logs in GUI interface +# Exclude Visual Studio Code project settings +/.vscode + +# Exclude Visual Studio project settings +/.vs + +# Exclude database files +/Database + +# Exclude downloaded files +/download + +# Exclude model files +/models + +# Exclude Python cache files +__pycache__/ + +# Ignore all files in the logs/fit directory +logs/fit/* + +# Ignore all files in the Samples directory +Samples/* + +# Do not ignore directories in logs/fit that end with _STR +!logs/fit/*_STR/ + +# Do not ignore directories in Samples that end with _STR +!Samples/*_STR/ + +# Do not ignore .gz files in directories in Samples that end with _STR +!Samples/*_STR.gz + +# Exclude specific model weight files +/PAI_model_weights.h5 +/PAI_model_weights_BL.h5 +/PAI_model_T.h5 +/PAI_model_T_BL.h5 + +# Exclude cache files +/cache + +# Exclude build artifacts +/Build + +# Exclude validation files +/validation + +# Exclude project file for Pneumonia AI +/Pneumonia AI.pyproj + +# Exclude specific model file used in the CLI interface +/Interface/CLI/Data/PAI_model.h5 + +# Exclude temporary files +/TEMP.txt +/freearc1.tmp + +# Exclude logs and dataset files in the CLI interface +/Interface/CLI/Data/logs +/Interface/CLI/Data/dataset.npy + +# Exclude temporary Python version file in the CLI interface +/Interface/CLI/Data/Python Ver.tmp + +# Exclude specific model file used in the GUI interface +/Interface/GUI/Data/PAI_model.h5 + +# Exclude temporary Python version file in the GUI interface +/Interface/GUI/Data/Python Ver.tmp + +# venv +/venv_2 +/venv + +# Python Embed +/Interface/CLI/Data/Python Embed 3.10.11 +/Interface/CLI/Data/Use_Python_Embed.tmp +/Interface/CLI/Python.Embed.3.10.11.exe + +# GUI DEV +/Interface/GUI/GUI_DEV.cmd + +# Exclude logs in GUI interface /Interface/GUI/Data/logs \ No newline at end of file diff --git a/.idea/Pneumonia AI Dev.iml b/.idea/Pneumonia AI Dev.iml index 167e7f9..12a49d6 100644 --- a/.idea/Pneumonia AI Dev.iml +++ b/.idea/Pneumonia AI Dev.iml @@ -1,15 +1,15 @@ - - - - - - - - - - + + + + + + + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml index 2ce26da..a41c242 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,7 +1,7 @@ - - - - - + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml index bc7318f..29389fc 100644 --- a/.idea/modules.xml +++ b/.idea/modules.xml @@ -1,8 +1,8 @@ - - - - - - - + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml index 35eb1dd..c8397c9 100644 --- a/.idea/vcs.xml +++ b/.idea/vcs.xml @@ -1,6 +1,6 @@ - - - - - + + + + + \ No newline at end of file diff --git a/Archive/API/Python/Example.py b/Archive/API/Python/Example.py index e3f6a14..f94c09d 100644 --- a/Archive/API/Python/Example.py +++ b/Archive/API/Python/Example.py @@ -1,29 +1,29 @@ -# Import necessary libraries -import pprint -import numpy as np -from pdai import * -from PIL import Image -pp = pprint.PrettyPrinter(indent=4, width=10) - -# Instantiate the PneumoniaModel class -pdai_model = PneumoniaModel("models\Ready\V1\PAI_model.h5", verbose=0) - -# Load the model -pdai_model.load_model() - -# Load an image for prediction -img_path = 'API\\Python\\test sampels\\PNEUMONIA\\person1947_bacteria_4876.jpeg' -img = Image.open(img_path) -img = img.convert('RGB') # Convert grayscale to RGB -img = img.resize((280, 300)) -x = np.array(img) -x = np.expand_dims(x, axis=0) - -print('without CLAHE>>>') -# Make a prediction without CLAHE -result = pdai_model.predict(x) -pp.pprint(result) -print('with CLAHE>>>') -# Make a prediction with CLAHE -result = pdai_model.predict(x, clahe=True) -pp.pprint(result) +# Import necessary libraries +import pprint +import numpy as np +from pdai import * +from PIL import Image +pp = pprint.PrettyPrinter(indent=4, width=10) + +# Instantiate the PneumoniaModel class +pdai_model = PneumoniaModel("models\Ready\V1\PAI_model.h5", verbose=0) + +# Load the model +pdai_model.load_model() + +# Load an image for prediction +img_path = 'API\\Python\\test sampels\\PNEUMONIA\\person1947_bacteria_4876.jpeg' +img = Image.open(img_path) +img = img.convert('RGB') # Convert grayscale to RGB +img = img.resize((280, 300)) +x = np.array(img) +x = np.expand_dims(x, axis=0) + +print('without CLAHE>>>') +# Make a prediction without CLAHE +result = pdai_model.predict(x) +pp.pprint(result) +print('with CLAHE>>>') +# Make a prediction with CLAHE +result = pdai_model.predict(x, clahe=True) +pp.pprint(result) diff --git a/Archive/API/Python/pdai.py b/Archive/API/Python/pdai.py index b0b6fb1..05daac8 100644 --- a/Archive/API/Python/pdai.py +++ b/Archive/API/Python/pdai.py @@ -1,104 +1,104 @@ -import os -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -from keras.models import load_model -from typing import Union, Dict -import numpy as np -import cv2 - -class PneumoniaModel: - def __init__(self, model_path: str, verbose: int = 0): - """ - Initializes the PneumoniaModel with the given model path and verbosity level. - - Args: - model_path (str): Path to the saved model. - verbose (int, optional): Verbosity level. If 1, prints status messages during operations. Defaults to 0. - """ - self.model_path = model_path - self.model = None - self.verbose = verbose - - - def load_model(self) -> Dict[str, Union[str, None]]: - """ - Loads the model from the path specified during initialization. - - Returns: - dict: A dictionary with a "status" key. If the model is loaded successfully, "status" is "success". - If an error occurs, "status" is "error" and an additional "message" key contains the error message. - """ - try: - self.model = None - self.model = load_model(self.model_path) - if self.verbose == 1: - print("Model loaded successfully.") - except Exception as e: - if self.verbose == 1: - print(f"Error loading model: {str(e)}") - return {"status": "error", "message": str(e)} - - return {"status": "success"} - - - def predict(self, image: np.ndarray, clahe: bool = False) -> Dict[str, Union[str, float, None]]: - """ - Makes a prediction using the loaded model on the given image. - - Args: - image (np.ndarray): The image to make a prediction on. - clahe (bool, optional): Whether to apply CLAHE to the image before making a prediction. Defaults to False. - - Returns: - dict: A dictionary with a "status" key. If the prediction is made successfully, "status" is "success", - and additional "prediction" and "confidence" keys contain the prediction and confidence level. - If an error occurs, "status" is "error" and an additional "message" key contains the error message. - """ - if self.model is None: - if self.verbose == 1: - print("Model not loaded. Call load_model() first.") - return {"status": "error", "message": "Model not loaded. Call load_model() first."} - - if image.ndim != 4 or image.shape[3] != 3: - return {"status": "error", "message": f"Invalid image format. The image should have three color channels (RGB). Img shape = {image.shape}."} - - try: - if clahe: - # Create a CLAHE object - clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) - - b, g, r = cv2.split(image[0]) - - # Convert the channels to the appropriate format - b = cv2.convertScaleAbs(b) - g = cv2.convertScaleAbs(g) - r = cv2.convertScaleAbs(r) - - # Apply adaptive histogram equalization to each channel - equalized_b = clahe.apply(b) - equalized_g = clahe.apply(g) - equalized_r = clahe.apply(r) - - # Merge the equalized channels back into an image - equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) - - # Replace the original image with the equalized image in the array - image = equalized_image - - # Normalize the image - image = image / 255.0 - - if self.verbose == 1: - print("Making prediction...") - prediction = self.model.predict(image) - if np.argmax(prediction) == 0: - if self.verbose == 1: - print("Prediction: Normal") - return {"status": "success", "prediction": "Normal", "confidence": np.max(prediction)} - else: - if self.verbose == 1: - print("Prediction: Pneumonia") - return {"status": "success", "prediction": "Pneumonia", "confidence": np.max(prediction)} - except IndexError as e: - if self.verbose == 1: - print(f"Error making prediction: {str(e)}") - return {"status": "error", "message": str(e)} +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +from keras.models import load_model +from typing import Union, Dict +import numpy as np +import cv2 + +class PneumoniaModel: + def __init__(self, model_path: str, verbose: int = 0): + """ + Initializes the PneumoniaModel with the given model path and verbosity level. + + Args: + model_path (str): Path to the saved model. + verbose (int, optional): Verbosity level. If 1, prints status messages during operations. Defaults to 0. + """ + self.model_path = model_path + self.model = None + self.verbose = verbose + + + def load_model(self) -> Dict[str, Union[str, None]]: + """ + Loads the model from the path specified during initialization. + + Returns: + dict: A dictionary with a "status" key. If the model is loaded successfully, "status" is "success". + If an error occurs, "status" is "error" and an additional "message" key contains the error message. + """ + try: + self.model = None + self.model = load_model(self.model_path) + if self.verbose == 1: + print("Model loaded successfully.") + except Exception as e: + if self.verbose == 1: + print(f"Error loading model: {str(e)}") + return {"status": "error", "message": str(e)} + + return {"status": "success"} + + + def predict(self, image: np.ndarray, clahe: bool = False) -> Dict[str, Union[str, float, None]]: + """ + Makes a prediction using the loaded model on the given image. + + Args: + image (np.ndarray): The image to make a prediction on. + clahe (bool, optional): Whether to apply CLAHE to the image before making a prediction. Defaults to False. + + Returns: + dict: A dictionary with a "status" key. If the prediction is made successfully, "status" is "success", + and additional "prediction" and "confidence" keys contain the prediction and confidence level. + If an error occurs, "status" is "error" and an additional "message" key contains the error message. + """ + if self.model is None: + if self.verbose == 1: + print("Model not loaded. Call load_model() first.") + return {"status": "error", "message": "Model not loaded. Call load_model() first."} + + if image.ndim != 4 or image.shape[3] != 3: + return {"status": "error", "message": f"Invalid image format. The image should have three color channels (RGB). Img shape = {image.shape}."} + + try: + if clahe: + # Create a CLAHE object + clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) + + b, g, r = cv2.split(image[0]) + + # Convert the channels to the appropriate format + b = cv2.convertScaleAbs(b) + g = cv2.convertScaleAbs(g) + r = cv2.convertScaleAbs(r) + + # Apply adaptive histogram equalization to each channel + equalized_b = clahe.apply(b) + equalized_g = clahe.apply(g) + equalized_r = clahe.apply(r) + + # Merge the equalized channels back into an image + equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) + + # Replace the original image with the equalized image in the array + image = equalized_image + + # Normalize the image + image = image / 255.0 + + if self.verbose == 1: + print("Making prediction...") + prediction = self.model.predict(image) + if np.argmax(prediction) == 0: + if self.verbose == 1: + print("Prediction: Normal") + return {"status": "success", "prediction": "Normal", "confidence": np.max(prediction)} + else: + if self.verbose == 1: + print("Prediction: Pneumonia") + return {"status": "success", "prediction": "Pneumonia", "confidence": np.max(prediction)} + except IndexError as e: + if self.verbose == 1: + print(f"Error making prediction: {str(e)}") + return {"status": "error", "message": str(e)} diff --git a/Archive/keras_applications_mod/README.md b/Archive/keras_applications_mod/README.md index e7bf84f..43230c4 100644 --- a/Archive/keras_applications_mod/README.md +++ b/Archive/keras_applications_mod/README.md @@ -1,7 +1,7 @@ -# This is a modified clone of `keras-applications` -## The repo part https://github.com/keras-team/keras/tree/master/keras/applications link: -### https://github.com/keras-team/keras-applications/tree/master -## Changed: -- efficientnet.py - - Added `EfficientNet_CXL` model. +# This is a modified clone of `keras-applications` +## The repo part https://github.com/keras-team/keras/tree/master/keras/applications link: +### https://github.com/keras-team/keras-applications/tree/master +## Changed: +- efficientnet.py + - Added `EfficientNet_CXL` model. \ No newline at end of file diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb index e5bbdbc..3a896d1 100644 --- a/BETA_E_Model_T&T.ipynb +++ b/BETA_E_Model_T&T.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:44.939427800Z", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.128539500Z", @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.139048Z", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:48.287855100Z", @@ -209,15 +209,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "SAVE_TYPE = 'H5'\n", "Use_mixed_float16 = False\n", @@ -239,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.059139500Z", @@ -249,29 +241,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mUsing Def IDG...\u001b[0m\n", - "Found 23681 images belonging to 2 classes.\n", - "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", - "\u001b[0;33mMaking categorical data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mNormalizing image data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n", - "\u001b[0;33mSetting LNTS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", - "\u001b[0;33mshuffling data...\u001b[0m\n", - "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d26-h14_m43_s44\u001b[0m\n", - "\u001b[0;32mDone.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "#Z_SCORE_normalize\n", "def Z_SCORE_normalize(arr):\n", @@ -678,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -878,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-27T17:34:12.077394600Z", @@ -888,2164 +858,7 @@ "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n", - " 1[0][0]'] \n", - " \n", - " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n", - " 2[0][0]'] \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -3139,2471 +952,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total base_model1 layers: 806\n", - "Total base_model2 layers: 132\n", - "Total model layers: 15\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " efficientnet-b7 (Functional) (None, 7, 7, 2560) 64097680 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| stem_conv (Conv2D) (None, 112, 112, 64 1728 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_bn (BatchNormalization) (None, 112, 112, 64 256 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_activation (Activation) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1a_bn (BatchNormalization (None, 112, 112, 64 256 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_activation (Activation (None, 112, 112, 64 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_se_squeeze (GlobalAver (None, 64) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 [] Y |\n", - "| |\n", - "| block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 [] Y |\n", - "| |\n", - "| block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 [] Y |\n", - "| |\n", - "| block1a_se_excite (Multiply) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1b_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1b_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1c_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1c_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1c_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1d_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1d_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1d_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 [] Y |\n", - "| 2) |\n", - "| |\n", - "| block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 [] Y |\n", - "| ization) 2) |\n", - "| |\n", - "| block2a_expand_activation (Act (None, 112, 112, 19 0 [] Y |\n", - "| ivation) 2) |\n", - "| |\n", - "| block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2a_bn (BatchNormalization (None, 56, 56, 192) 768 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_activation (Activation (None, 56, 56, 192) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_se_squeeze (GlobalAver (None, 192) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 [] Y |\n", - "| |\n", - "| block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 [] Y |\n", - "| |\n", - "| block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 [] Y |\n", - "| |\n", - "| block2a_se_excite (Multiply) (None, 56, 56, 192) 0 [] Y |\n", - "| |\n", - "| block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 [] Y |\n", - "| |\n", - "| block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2b_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2b_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2b_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2c_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2c_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2c_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2d_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2d_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2d_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2e_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2e_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2e_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2f_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2f_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2f_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2g_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2g_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2g_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3a_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_activation (Activation (None, 28, 28, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block3a_se_excite (Multiply) (None, 28, 28, 288) 0 [] Y |\n", - "| |\n", - "| block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 [] Y |\n", - "| |\n", - "| block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3b_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3b_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3b_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3c_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3c_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3c_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3d_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3d_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3d_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3e_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3e_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3e_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3f_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3f_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3f_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3g_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3g_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3g_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4a_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_activation (Activation (None, 14, 14, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block4a_se_excite (Multiply) (None, 14, 14, 480) 0 [] Y |\n", - "| |\n", - "| block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 [] Y |\n", - "| |\n", - "| block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4b_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4b_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4b_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4c_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4c_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4c_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4d_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4d_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4d_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4e_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4e_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4e_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4f_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4f_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4f_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4g_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4g_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4g_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4h_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4h_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4h_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4i_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4i_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4i_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4j_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4j_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4j_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block5a_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block5a_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 [] Y |\n", - "| |\n", - "| block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5b_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5b_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5b_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5c_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5c_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5c_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5d_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5d_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5d_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5e_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5e_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5e_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5f_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5f_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5f_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5g_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5g_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5g_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5h_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5h_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5h_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5i_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5i_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5i_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5j_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5j_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5j_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block6a_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_activation (Activation (None, 7, 7, 1344) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 [] Y |\n", - "| |\n", - "| block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6b_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6b_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6c_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6c_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6d_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6d_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6e_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6e_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6f_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6f_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6g_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6g_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6h_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6h_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6i_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6i_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6j_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6j_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6k_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6k_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6l_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6l_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6m_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6m_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7a_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 [] Y |\n", - "| |\n", - "| block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7b_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7b_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7c_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7c_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7d_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7d_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| top_conv (Conv2D) (None, 7, 7, 2560) 1638400 [] Y |\n", - "| |\n", - "| top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 [] Y |\n", - "| |\n", - "| top_activation (Activation) (None, 7, 7, 2560) 0 [] Y |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " xception (Functional) (None, 7, 7, 2048) 20861480 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| block1_conv1 (Conv2D) (None, 111, 111, 32 864 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv1_bn (BatchNormaliz (None, 111, 111, 32 128 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv1_act (Activation) (None, 111, 111, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2 (Conv2D) (None, 109, 109, 64 18432 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2_bn (BatchNormaliz (None, 109, 109, 64 256 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv2_act (Activation) (None, 109, 109, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2_sepconv1 (SeparableConv (None, 109, 109, 12 8768 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv1_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| block2_sepconv2_act (Activatio (None, 109, 109, 12 0 [] Y |\n", - "| n) 8) |\n", - "| |\n", - "| block2_sepconv2 (SeparableConv (None, 109, 109, 12 17536 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv2_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| conv2d (Conv2D) (None, 55, 55, 128) 8192 [] Y |\n", - "| |\n", - "| block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| batch_normalization (BatchNorm (None, 55, 55, 128) 512 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add (Add) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| block3_sepconv1_act (Activatio (None, 55, 55, 128) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv1 (SeparableConv (None, 55, 55, 256) 33920 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv1_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3_sepconv2_act (Activatio (None, 55, 55, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv2 (SeparableConv (None, 55, 55, 256) 67840 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv2_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 [] Y |\n", - "| |\n", - "| block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| batch_normalization_1 (BatchNo (None, 28, 28, 256) 1024 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_1 (Add) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| block4_sepconv1_act (Activatio (None, 28, 28, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv1 (SeparableConv (None, 28, 28, 728) 188672 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv1_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4_sepconv2_act (Activatio (None, 28, 28, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv2 (SeparableConv (None, 28, 28, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv2_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 [] Y |\n", - "| |\n", - "| block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| batch_normalization_2 (BatchNo (None, 14, 14, 728) 2912 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_2 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block5_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_3 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block6_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_4 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block7_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_5 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block8_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_6 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block9_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_7 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block10_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_8 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block11_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_9 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block12_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_10 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block13_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block13_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block13_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv2 (SeparableCon (None, 14, 14, 1024 752024 [] Y |\n", - "| v2D) ) |\n", - "| |\n", - "| block13_sepconv2_bn (BatchNorm (None, 14, 14, 1024 4096 [] Y |\n", - "| alization) ) |\n", - "| |\n", - "| conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 [] Y |\n", - "| |\n", - "| block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_11 (Add) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 [] Y |\n", - "| on) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (Glob (None, 2560) 0 ['efficientnet-b7[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " global_average_pooling2d_1 (Gl (None, 2048) 0 ['xception[0][0]'] Y \n", - " obalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dense_1 (Dense) (None, 512) 1049088 ['global_average_pooling2d_1[0] Y \n", - " [0]'] \n", - " \n", - " concatenate (Concatenate) (None, 1024) 0 ['dense[0][0]', Y \n", - " 'dense_1[0][0]'] \n", - " \n", - " dense_2 (Dense) (None, 1024) 1049600 ['concatenate[0][0]'] Y \n", - " \n", - " dropout (Dropout) (None, 1024) 0 ['dense_2[0][0]'] Y \n", - " \n", - " batch_normalization_4 (BatchNo (None, 1024) 4096 ['dropout[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_3 (Dense) (None, 512) 524800 ['batch_normalization_4[0][0]'] Y \n", - " \n", - " batch_normalization_5 (BatchNo (None, 512) 2048 ['dense_3[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_4 (Dense) (None, 128) 65664 ['batch_normalization_5[0][0]'] Y \n", - " \n", - " dense_5 (Dense) (None, 2) 258 ['dense_4[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 88,965,946\n", - "Trainable params: 88,597,626\n", - "Non-trainable params: 368,320\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "from keras.applications.xception import Xception\n", @@ -5692,4150 +1043,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n", - " ) 'stack_0_block0_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n", - " ) 'stack_0_block1_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n", - " ) 'stack_0_block2_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n", - " ) 'stack_0_block3_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_1_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n", - " 'stack_1_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n", - " 'stack_1_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n", - " 'stack_1_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n", - " 'stack_1_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n", - " 'stack_1_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n", - " 'stack_1_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_2_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n", - " 'stack_2_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n", - " 'stack_2_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n", - " 'stack_2_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n", - " 'stack_2_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n", - " 'stack_2_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n", - " 'stack_2_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " a) [0]'] \n", - " \n", - " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_1[0][0]'] \n", - " \n", - " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_3[0][0]'] \n", - " \n", - " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_3_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_5[0][0]'] \n", - " \n", - " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n", - " 'stack_3_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_7[0][0]'] \n", - " \n", - " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n", - " 'stack_3_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_9[0][0]'] \n", - " \n", - " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n", - " 'stack_3_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_11[0][0]'] \n", - " \n", - " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n", - " 'stack_3_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_13[0][0]'] \n", - " \n", - " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n", - " 'stack_3_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_15[0][0]'] \n", - " \n", - " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n", - " 'stack_3_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_17[0][0]'] \n", - " \n", - " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n", - " 'stack_3_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_19[0][0]'] \n", - " \n", - " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n", - " 'stack_3_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_21[0][0]'] \n", - " \n", - " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n", - " 'stack_3_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_23[0][0]'] \n", - " \n", - " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n", - " 'stack_3_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_25[0][0]'] \n", - " \n", - " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n", - " 'stack_3_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_27[0][0]'] \n", - " \n", - " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n", - " 'stack_3_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_29[0][0]'] \n", - " \n", - " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n", - " 'stack_3_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_31[0][0]'] \n", - " \n", - " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n", - " 'stack_3_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_33[0][0]'] \n", - " \n", - " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " onv2D) ) '] \n", - " \n", - " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_35[0][0]'] \n", - " \n", - " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_4_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_37[0][0]'] \n", - " \n", - " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n", - " 'stack_4_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_39[0][0]'] \n", - " \n", - " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n", - " 'stack_4_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_41[0][0]'] \n", - " \n", - " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n", - " 'stack_4_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_43[0][0]'] \n", - " \n", - " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n", - " 'stack_4_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_45[0][0]'] \n", - " \n", - " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n", - " 'stack_4_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_47[0][0]'] \n", - " \n", - " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n", - " 'stack_4_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_49[0][0]'] \n", - " \n", - " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n", - " 'stack_4_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_51[0][0]'] \n", - " \n", - " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n", - " 'stack_4_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_53[0][0]'] \n", - " \n", - " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n", - " 'stack_4_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_55[0][0]'] \n", - " \n", - " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n", - " 'stack_4_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_57[0][0]'] \n", - " \n", - " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n", - " 'stack_4_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_59[0][0]'] \n", - " \n", - " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n", - " 'stack_4_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_61[0][0]'] \n", - " \n", - " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n", - " 'stack_4_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_63[0][0]'] \n", - " \n", - " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n", - " 'stack_4_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_65[0][0]'] \n", - " \n", - " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n", - " 'stack_4_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_67[0][0]'] \n", - " \n", - " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n", - " 'stack_4_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_69[0][0]'] \n", - " \n", - " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n", - " 'stack_4_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_71[0][0]'] \n", - " \n", - " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n", - " 'stack_4_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_73[0][0]'] \n", - " \n", - " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n", - " 'stack_4_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_75[0][0]'] \n", - " \n", - " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n", - " 'stack_4_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_77[0][0]'] \n", - " \n", - " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n", - " 'stack_4_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_79[0][0]'] \n", - " \n", - " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n", - " 'stack_4_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_81[0][0]'] \n", - " \n", - " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_83[0][0]'] \n", - " \n", - " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_5_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_85[0][0]'] \n", - " \n", - " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n", - " 'stack_5_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_87[0][0]'] \n", - " \n", - " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n", - " 'stack_5_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_89[0][0]'] \n", - " \n", - " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n", - " 'stack_5_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_91[0][0]'] \n", - " \n", - " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n", - " 'stack_5_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_93[0][0]'] \n", - " \n", - " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n", - " 'stack_5_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_95[0][0]'] \n", - " \n", - " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n", - " 'stack_5_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_97[0][0]'] \n", - " \n", - " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n", - " 'stack_5_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_99[0][0]'] \n", - " \n", - " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n", - " 'stack_5_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_101[0][0]'] \n", - " \n", - " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n", - " 'stack_5_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_103[0][0]'] \n", - " \n", - " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n", - " 'stack_5_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_105[0][0]'] \n", - " \n", - " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n", - " 'stack_5_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_107[0][0]'] \n", - " \n", - " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n", - " 'stack_5_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_109[0][0]'] \n", - " \n", - " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n", - " 'stack_5_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_111[0][0]'] \n", - " \n", - " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n", - " 'stack_5_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_113[0][0]'] \n", - " \n", - " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n", - " 'stack_5_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_115[0][0]'] \n", - " \n", - " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n", - " 'stack_5_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_117[0][0]'] \n", - " \n", - " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n", - " 'stack_5_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_119[0][0]'] \n", - " \n", - " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n", - " 'stack_5_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_121[0][0]'] \n", - " \n", - " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n", - " 'stack_5_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_123[0][0]'] \n", - " \n", - " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n", - " 'stack_5_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_125[0][0]'] \n", - " \n", - " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n", - " 'stack_5_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_127[0][0]'] \n", - " \n", - " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n", - " 'stack_5_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_129[0][0]'] \n", - " \n", - " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n", - " 'stack_5_block24_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_131[0][0]'] \n", - " \n", - " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n", - " 'stack_5_block25_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_133[0][0]'] \n", - " \n", - " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n", - " 'stack_5_block26_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_135[0][0]'] \n", - " \n", - " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n", - " 'stack_5_block27_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_137[0][0]'] \n", - " \n", - " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n", - " 'stack_5_block28_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_139[0][0]'] \n", - " \n", - " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n", - " 'stack_5_block29_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_141[0][0]'] \n", - " \n", - " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n", - " 'stack_5_block30_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_143[0][0]'] \n", - " \n", - " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n", - " 'stack_5_block31_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_145[0][0]'] \n", - " \n", - " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_147[0][0]'] \n", - " \n", - " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_6_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_149[0][0]'] \n", - " \n", - " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n", - " 'stack_6_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_151[0][0]'] \n", - " \n", - " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n", - " 'stack_6_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_153[0][0]'] \n", - " \n", - " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n", - " 'stack_6_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_155[0][0]'] \n", - " \n", - " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n", - " 'stack_6_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_157[0][0]'] \n", - " \n", - " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n", - " 'stack_6_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_159[0][0]'] \n", - " \n", - " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n", - " 'stack_6_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n", - " \n", - " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", - " \n", - " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", - " \n", - " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", - " 2D) \n", - " \n", - " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", - " \n", - " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 207,618,394\n", - "Trainable params: 206,841,370\n", - "Non-trainable params: 777,024\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras_efficientnet_v2 import EfficientNetV2XL\n", "\n", @@ -9872,1276 +1082,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total layers in the base model: 467\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 475\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1b_add[0][0]'] Y \n", - " 4) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 4) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 4) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 32) 4608 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 192) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 192) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 192) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 32) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 192) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 192) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 192) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 32) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 192) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 192) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 192) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 32) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2d_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 192) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 192) 4800 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 192) 768 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 192) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 192) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 192) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 56) 10752 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 336) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 336) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 336) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 56) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 336) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 336) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 336) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 56) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 336) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 336) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 336) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 56) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3d_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 336) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 336) 3024 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 336) 1344 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 336) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 336) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 336) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 112) 37632 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 672) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 672) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 672) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 112) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 672) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 672) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 672) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 112) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 672) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 672) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 672) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 112) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 672) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 672) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 672) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 112) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 672) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 672) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 672) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 112) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4f_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 672) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 672) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 672) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 160) 107520 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5a_project_bn[0][0]'] Y \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 960) 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 960) 0 ['block5b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 960) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5b_activation[0][0]', Y \n", - " 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 160) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5b_add[0][0]'] Y \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 960) 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 960) 0 ['block5c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 960) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5c_activation[0][0]', Y \n", - " 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 160) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5c_add[0][0]'] Y \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 960) 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 960) 0 ['block5d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 960) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5d_activation[0][0]', Y \n", - " 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 160) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5d_add[0][0]'] Y \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 960) 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 960) 0 ['block5e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 960) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5e_activation[0][0]', Y \n", - " 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 160) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5e_add[0][0]'] Y \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 960) 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 960) 0 ['block5f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 960) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5f_activation[0][0]', Y \n", - " 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 160) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5f_add[0][0]'] Y \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block6a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 960) 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 960) 24000 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 960) 3840 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 960) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 960) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 960) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 272) 261120 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 1632) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 1632) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 1632) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 272) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 1632) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 1632) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 1632) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 272) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 1632) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 1632) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 1632) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 272) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 1632) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 1632) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 1632) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 272) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 1632) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 1632) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 1632) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 272) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 1632) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 1632) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 1632) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 272) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 1632) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 1632) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 1632) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 272) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6h_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 1632) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 14688 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 1632) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 1632) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 448) 731136 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 2688) 1204224 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 2688) 10752 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 2688) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 2688) 24192 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 2688) 10752 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 2688) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 2688) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 2688) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 112) 301168 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 2688) 303744 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 2688) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 448) 1204224 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 448) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 448) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 1792) 802816 ['block7b_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 1792) 7168 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 1792) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 1792) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 918016 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 18,924,506\n", - "Trainable params: 18,797,258\n", - "Non-trainable params: 127,248\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB4 as KENB4\n", "# FUNC\n", @@ -11286,2169 +1229,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:32.994176700Z", "start_time": "2023-12-28T02:31:27.381088600Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L1 (BatchNorm (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-Pooling-L1[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L2 (BatchNorm (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-Pooling-L2[0][0]'] Y \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -13534,1145 +1322,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total model layers: 11\n", - "Model: \"model\"\n", - "____________________________________________________________________________\n", - " Layer (type) Output Shape Param # Trainable \n", - "============================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3)] 0 Y \n", - " \n", - " lambda (Lambda) (None, 224, 224, 3) 0 Y \n", - " \n", - " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n", - " ) 8 \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n", - "| |\n", - "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n", - "| malization (Normalization) |\n", - "| |\n", - "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n", - "| ntial) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n", - "|| Conv2D) ||\n", - "|| ||\n", - "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n", - "|| orm (LayerNormalization) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n", - "| ambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n", - "| g_block_0 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n", - "|| g_layernorm_0 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n", - "|| g_conv_0 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n", - "| g_block_1 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n", - "|| g_layernorm_1 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n", - "|| g_conv_1 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_3_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_3_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_3_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_3_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_3_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_3_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_3_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_4_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_4_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_4_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_4_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_4_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_4_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_4_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_5_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_5_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_5_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_5_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_5_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_5_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_5_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_6_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_6_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_6_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_6_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_6_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_6_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_6_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_7_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_7_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_7_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_7_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_7_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_7_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_7_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_8_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_8_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_8_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_8_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_8_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_8_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_8_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_9_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_9_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_9_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_9_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_9_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_9_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_9_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_10_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_10_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_10_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_10_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_10_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_10_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_10_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_11_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_11_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_11_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_11_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_11_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_11_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_11_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_12_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_12_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_12_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_12_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_12_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_12_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_12_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_13_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_13_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_13_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_13_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_13_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_13_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_13_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_14_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_14_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_14_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_14_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_14_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_14_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_14_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_15_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_15_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_15_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_15_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_15_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_15_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_15_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_16_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_16_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_16_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_16_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_16_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_16_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_16_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_17_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_17_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_17_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_17_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_17_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_17_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_17_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_18_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_18_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_18_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_18_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_18_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_18_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_18_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_19_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_19_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_19_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_19_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_19_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_19_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_19_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_20_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_20_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_20_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_20_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_20_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_20_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_20_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_21_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_21_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_21_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_21_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_21_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_21_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_21_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_22_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_22_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_22_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_22_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_22_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_22_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_22_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_23_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_23_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_23_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_23_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_23_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_23_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_23_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_24_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_24_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_24_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_24_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_24_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_24_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_24_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_25_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_25_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_25_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_25_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_25_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_25_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_25_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_26_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_26_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_26_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_26_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_26_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_26_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_26_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n", - "| g_block_2 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n", - "|| g_layernorm_2 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n", - "|| g_conv_2 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n", - "| ormalization) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (G (None, 2048) 0 Y \n", - " lobalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1049088 Y \n", - " \n", - " dropout (Dropout) (None, 512) 0 Y \n", - " \n", - " batch_normalization (BatchN (None, 512) 2048 Y \n", - " ormalization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 Y \n", - " \n", - " batch_normalization_1 (Batc (None, 512) 2048 Y \n", - " hNormalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 Y \n", - " \n", - "============================================================================\n", - "Total params: 349,529,730\n", - "Trainable params: 349,527,682\n", - "Non-trainable params: 2,048\n", - "____________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras.applications import ConvNeXtXLarge\n", "from keras.layers import Lambda\n", @@ -14718,1545 +1370,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 569\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 577\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 24 96 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 24 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 24) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 24 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 24 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 24 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1c_add[0][0]'] Y \n", - " 4) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 4) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 4) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 40) 5760 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 240) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 240) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 240) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 40) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 240) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 240) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 240) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 40) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 240) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 240) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 240) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 40) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 240) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 240) 2160 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 240) 960 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 240) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 240) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 240) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 40) 9600 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 40) 160 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 40) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 40) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 240) 9600 ['block2e_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 240) 960 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 240) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 240) 6000 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 240) 960 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 240) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 240) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 240) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 64) 15360 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 384) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 384) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 384) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 64) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 384) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 384) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 384) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 64) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 384) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 384) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 384) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 64) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 384) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 384) 9600 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 384) 1536 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 384) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 384) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 384) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 64) 24576 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 64) 256 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 64) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 64) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 384) 24576 ['block3e_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 384) 1536 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 384) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 384) 3456 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 384) 1536 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 384) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 384) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 384) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 16) 6160 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 384) 6528 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 384) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 128) 49152 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 768) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 768) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 768) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 128) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 768) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 768) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 768) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 128) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 768) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 768) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 768) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 128) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 768) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 768) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 768) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 128) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 768) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 768) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 768) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 128) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 768) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 768) 6912 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 768) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 768) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 768) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 128) 98304 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 128) 512 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 128) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 128) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 768) 98304 ['block4g_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 768) 3072 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 768) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 768) 19200 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 768) 3072 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 768) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 768) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 768) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 32) 24608 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 768) 25344 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 768) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 176) 135168 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1056 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1056 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1056) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 176) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1056 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1056 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1056) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 176) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1056 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1056 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1056) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 176) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1056 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1056 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1056) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 176) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1056 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1056 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1056) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 176) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1056 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1056 26400 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1056 4224 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1056 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1056) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1056 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 176) 185856 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 176) 704 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 176) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 176) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1056 185856 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1056 4224 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1056 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1056) 26400 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1056) 4224 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1056) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1056) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1056) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 44) 46508 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1056) 47520 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1056) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 304) 321024 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 1824) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 1824) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 1824) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 304) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 1824) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 1824) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 1824) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 304) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 1824) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 1824) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 1824) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 304) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 1824) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 1824) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 1824) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 304) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 1824) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 1824) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 1824) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 304) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 1824) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 1824) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 1824) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 304) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 1824) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 1824) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 1824) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 304) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 1824) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 45600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 1824) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 1824) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 304) 554496 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 304) 1216 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 304) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 304) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 1824) 554496 ['block6i_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 1824) 7296 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 1824) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1824) 16416 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 1824) 7296 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 1824) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 1824) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 1824) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 76) 138700 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 1824) 140448 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 1824) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 512) 933888 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3072) 1572864 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3072) 12288 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3072) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3072) 27648 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3072) 12288 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3072) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3072) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3072) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 128) 393344 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3072) 396288 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3072) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 512) 1572864 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 512) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 512) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3072) 1572864 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3072) 12288 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3072) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3072) 27648 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3072) 12288 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3072) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3072) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3072) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 128) 393344 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3072) 396288 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3072) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 512) 1572864 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 512) 2048 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 512) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 512) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2048) 1048576 ['block7c_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2048) 8192 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2048) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2048) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1049088 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n", - " 1[0][0]'] \n", - " \n", - " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n", - " 2[0][0]'] \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 29,895,282\n", - "Trainable params: 29,720,498\n", - "Non-trainable params: 174,784\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB5 as KENB5\n", "# FUNC\n", @@ -16388,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -16468,2162 +1584,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "import efficientnet.tfkeras\n", "# Configuration\n", @@ -18702,18 +1665,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "for layer in model.layers[-7:]:\n", " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", @@ -18756,2393 +1710,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", "start_time": "2023-12-28T02:31:32.468641900Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the model...\n", - "\u001b[0;33m\n", - "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d26-h14_m53_s22]\u001b[0m\u001b[0;36m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0;33mSetup Verbose END.\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d26-h14_m59_s09\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - "256/256 [==============================] - 64s 189ms/step - loss: 8.6530 - accuracy: 0.7173 - val_loss: 7.5201 - val_accuracy: 0.7356\n", - "Epoch 2/6\n", - "256/256 [==============================] - 45s 176ms/step - loss: 5.9175 - accuracy: 0.8672 - val_loss: 4.5553 - val_accuracy: 0.9119\n", - "Epoch 3/6\n", - "256/256 [==============================] - 46s 178ms/step - loss: 3.6912 - accuracy: 0.8940 - val_loss: 2.8975 - val_accuracy: 0.9279\n", - "Epoch 4/6\n", - "256/256 [==============================] - 46s 177ms/step - loss: 2.4085 - accuracy: 0.9192 - val_loss: 2.0007 - val_accuracy: 0.9263\n", - "Epoch 5/6\n", - "256/256 [==============================] - 46s 179ms/step - loss: 1.7321 - accuracy: 0.9355 - val_loss: 1.5934 - val_accuracy: 0.9343\n", - "Epoch 6/6\n", - "256/256 [==============================] - 45s 173ms/step - loss: 1.4544 - accuracy: 0.9568 - val_loss: 1.4990 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-005-0.9343.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.5934\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.934295\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m1.5933588743\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m655.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m292.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m362.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "256/256 [==============================] - 50s 182ms/step - loss: 1.5912 - accuracy: 0.8970 - val_loss: 1.4238 - val_accuracy: 0.8702\n", - "Epoch 8/12\n", - "256/256 [==============================] - 46s 179ms/step - loss: 1.2142 - accuracy: 0.9092 - val_loss: 0.9844 - val_accuracy: 0.9327\n", - "Epoch 9/12\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.8487 - accuracy: 0.9282 - val_loss: 0.8125 - val_accuracy: 0.8654\n", - "Epoch 10/12\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.6126 - accuracy: 0.9473 - val_loss: 0.6094 - val_accuracy: 0.9183\n", - "Epoch 11/12\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.4659 - accuracy: 0.9631 - val_loss: 0.5463 - val_accuracy: 0.9327\n", - "Epoch 12/12\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.3893 - accuracy: 0.9695 - val_loss: 0.5088 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9343.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5088\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9342948794. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m1.5933588743 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5087834001\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "256/256 [==============================] - 49s 178ms/step - loss: 0.5400 - accuracy: 0.9124 - val_loss: 0.5205 - val_accuracy: 0.8734\n", - "Epoch 14/18\n", - "256/256 [==============================] - 44s 170ms/step - loss: 0.4499 - accuracy: 0.9204 - val_loss: 0.6405 - val_accuracy: 0.8061\n", - "Epoch 15/18\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.3581 - accuracy: 0.9326 - val_loss: 0.3466 - val_accuracy: 0.9263\n", - "Epoch 16/18\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.2619 - accuracy: 0.9526 - val_loss: 0.3205 - val_accuracy: 0.9343\n", - "Epoch 17/18\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.2040 - accuracy: 0.9636 - val_loss: 0.2980 - val_accuracy: 0.9231\n", - "Epoch 18/18\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.1561 - accuracy: 0.9810 - val_loss: 0.2959 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-016-0.9343.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3205\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9342948794. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5087834001 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.3204655647\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m272.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "256/256 [==============================] - 47s 173ms/step - loss: 0.3401 - accuracy: 0.9219 - val_loss: 0.3586 - val_accuracy: 0.9231\n", - "Epoch 20/24\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.2926 - accuracy: 0.9314 - val_loss: 0.2720 - val_accuracy: 0.9391\n", - "Epoch 21/24\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.2480 - accuracy: 0.9417 - val_loss: 0.2342 - val_accuracy: 0.9407\n", - "Epoch 22/24\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1886 - accuracy: 0.9578 - val_loss: 0.2387 - val_accuracy: 0.9391\n", - "Epoch 23/24\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1363 - accuracy: 0.9719 - val_loss: 0.2509 - val_accuracy: 0.9439\n", - "Epoch 24/24\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0934 - accuracy: 0.9846 - val_loss: 0.3320 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-023-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2509\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.934295 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.943910\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.3204655647 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2508632839\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.2724 - accuracy: 0.9258 - val_loss: 0.2060 - val_accuracy: 0.9359\n", - "Epoch 26/30\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.2342 - accuracy: 0.9324 - val_loss: 0.2237 - val_accuracy: 0.9247\n", - "Epoch 27/30\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1993 - accuracy: 0.9460 - val_loss: 0.1988 - val_accuracy: 0.9439\n", - "Epoch 28/30\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1530 - accuracy: 0.9565 - val_loss: 0.1867 - val_accuracy: 0.9343\n", - "Epoch 29/30\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1135 - accuracy: 0.9714 - val_loss: 0.2152 - val_accuracy: 0.9375\n", - "Epoch 30/30\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0714 - accuracy: 0.9858 - val_loss: 0.2431 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-027-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1988\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9439102411. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2508632839 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1988100111\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.2368 - accuracy: 0.9280 - val_loss: 0.2502 - val_accuracy: 0.9071\n", - "Epoch 32/36\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.2017 - accuracy: 0.9353 - val_loss: 0.1626 - val_accuracy: 0.9487\n", - "Epoch 33/36\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1691 - accuracy: 0.9482 - val_loss: 0.1943 - val_accuracy: 0.9375\n", - "Epoch 34/36\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.1253 - accuracy: 0.9617 - val_loss: 0.2888 - val_accuracy: 0.9247\n", - "Epoch 35/36\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.0962 - accuracy: 0.9771 - val_loss: 0.2163 - val_accuracy: 0.9247\n", - "Epoch 36/36\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0675 - accuracy: 0.9858 - val_loss: 0.2298 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-032-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1626\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.943910 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.948718\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1988100111 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1625901014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "256/256 [==============================] - 48s 179ms/step - loss: 0.2136 - accuracy: 0.9304 - val_loss: 0.1726 - val_accuracy: 0.9455\n", - "Epoch 38/42\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.2011 - accuracy: 0.9375 - val_loss: 0.2487 - val_accuracy: 0.9327\n", - "Epoch 39/42\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1592 - accuracy: 0.9548 - val_loss: 0.1889 - val_accuracy: 0.9359\n", - "Epoch 40/42\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1222 - accuracy: 0.9641 - val_loss: 0.1968 - val_accuracy: 0.9375\n", - "Epoch 41/42\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0787 - accuracy: 0.9817 - val_loss: 0.2208 - val_accuracy: 0.9343\n", - "Epoch 42/42\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0509 - accuracy: 0.9905 - val_loss: 0.2115 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1727\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m339.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "256/256 [==============================] - 49s 178ms/step - loss: 0.2009 - accuracy: 0.9336 - val_loss: 0.2026 - val_accuracy: 0.9327\n", - "Epoch 44/48\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.2026 - accuracy: 0.9370 - val_loss: 0.2794 - val_accuracy: 0.8958\n", - "Epoch 45/48\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1615 - accuracy: 0.9556 - val_loss: 0.2324 - val_accuracy: 0.9151\n", - "Epoch 46/48\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1263 - accuracy: 0.9661 - val_loss: 0.4028 - val_accuracy: 0.8670\n", - "Epoch 47/48\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0831 - accuracy: 0.9758 - val_loss: 0.2567 - val_accuracy: 0.9199\n", - "Epoch 48/48\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0602 - accuracy: 0.9839 - val_loss: 0.2553 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9327.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2026\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m337.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.2027 - accuracy: 0.9404 - val_loss: 0.1770 - val_accuracy: 0.9487\n", - "Epoch 50/54\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1915 - accuracy: 0.9414 - val_loss: 0.2318 - val_accuracy: 0.9375\n", - "Epoch 51/54\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1626 - accuracy: 0.9514 - val_loss: 0.1680 - val_accuracy: 0.9423\n", - "Epoch 52/54\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1098 - accuracy: 0.9683 - val_loss: 0.1973 - val_accuracy: 0.9375\n", - "Epoch 53/54\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0854 - accuracy: 0.9729 - val_loss: 0.1925 - val_accuracy: 0.9407\n", - "Epoch 54/54\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0523 - accuracy: 0.9890 - val_loss: 0.2014 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1770\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m64.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "256/256 [==============================] - 49s 179ms/step - loss: 0.2028 - accuracy: 0.9387 - val_loss: 0.1677 - val_accuracy: 0.9407\n", - "Epoch 56/60\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1863 - accuracy: 0.9414 - val_loss: 0.3440 - val_accuracy: 0.8606\n", - "Epoch 57/60\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1613 - accuracy: 0.9507 - val_loss: 0.1691 - val_accuracy: 0.9471\n", - "Epoch 58/60\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1220 - accuracy: 0.9639 - val_loss: 0.2088 - val_accuracy: 0.9247\n", - "Epoch 59/60\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0851 - accuracy: 0.9797 - val_loss: 0.2744 - val_accuracy: 0.9167\n", - "Epoch 60/60\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0552 - accuracy: 0.9875 - val_loss: 0.2893 - val_accuracy: 0.9183\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-057-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1692\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m337.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m62.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "256/256 [==============================] - 49s 181ms/step - loss: 0.2094 - accuracy: 0.9370 - val_loss: 0.1859 - val_accuracy: 0.9359\n", - "Epoch 62/66\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1882 - accuracy: 0.9414 - val_loss: 0.1760 - val_accuracy: 0.9407\n", - "Epoch 63/66\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1549 - accuracy: 0.9558 - val_loss: 0.1782 - val_accuracy: 0.9375\n", - "Epoch 64/66\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1094 - accuracy: 0.9680 - val_loss: 0.1783 - val_accuracy: 0.9391\n", - "Epoch 65/66\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0806 - accuracy: 0.9797 - val_loss: 0.1968 - val_accuracy: 0.9423\n", - "Epoch 66/66\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0524 - accuracy: 0.9897 - val_loss: 0.2237 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-065-0.9423.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1968\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1625901014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m63.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.2125 - accuracy: 0.9351 - val_loss: 0.2021 - val_accuracy: 0.9215\n", - "Epoch 68/72\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1656 - accuracy: 0.9487 - val_loss: 0.1583 - val_accuracy: 0.9503\n", - "Epoch 69/72\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1388 - accuracy: 0.9568 - val_loss: 0.3528 - val_accuracy: 0.8910\n", - "Epoch 70/72\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1090 - accuracy: 0.9709 - val_loss: 0.1715 - val_accuracy: 0.9391\n", - "Epoch 71/72\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0702 - accuracy: 0.9834 - val_loss: 0.1996 - val_accuracy: 0.9455\n", - "Epoch 72/72\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0405 - accuracy: 0.9907 - val_loss: 0.2232 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-068-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.948718 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.950321\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1625901014 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1583294570\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1781 - accuracy: 0.9424 - val_loss: 0.1552 - val_accuracy: 0.9391\n", - "Epoch 74/78\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1650 - accuracy: 0.9460 - val_loss: 0.1671 - val_accuracy: 0.9375\n", - "Epoch 75/78\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.1449 - accuracy: 0.9573 - val_loss: 0.2712 - val_accuracy: 0.8910\n", - "Epoch 76/78\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.0998 - accuracy: 0.9731 - val_loss: 0.2384 - val_accuracy: 0.9215\n", - "Epoch 77/78\n", - "256/256 [==============================] - 45s 173ms/step - loss: 0.0739 - accuracy: 0.9834 - val_loss: 0.2009 - val_accuracy: 0.9327\n", - "Epoch 78/78\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0348 - accuracy: 0.9951 - val_loss: 0.2481 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9391.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1552\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1583294570 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1552029550\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1764 - accuracy: 0.9387 - val_loss: 0.1677 - val_accuracy: 0.9407\n", - "Epoch 80/84\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1616 - accuracy: 0.9446 - val_loss: 0.3176 - val_accuracy: 0.9247\n", - "Epoch 81/84\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1349 - accuracy: 0.9580 - val_loss: 0.1958 - val_accuracy: 0.9263\n", - "Epoch 82/84\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0955 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9327\n", - "Epoch 83/84\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0651 - accuracy: 0.9829 - val_loss: 0.2379 - val_accuracy: 0.9359\n", - "Epoch 84/84\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0410 - accuracy: 0.9915 - val_loss: 0.3032 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1676\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1552029550. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1780 - accuracy: 0.9399 - val_loss: 0.1802 - val_accuracy: 0.9375\n", - "Epoch 86/90\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.1567 - accuracy: 0.9495 - val_loss: 0.1706 - val_accuracy: 0.9487\n", - "Epoch 87/90\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.1379 - accuracy: 0.9583 - val_loss: 0.1877 - val_accuracy: 0.9343\n", - "Epoch 88/90\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1008 - accuracy: 0.9724 - val_loss: 0.1901 - val_accuracy: 0.9439\n", - "Epoch 89/90\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0644 - accuracy: 0.9836 - val_loss: 0.2305 - val_accuracy: 0.9311\n", - "Epoch 90/90\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0454 - accuracy: 0.9895 - val_loss: 0.2917 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-086-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1707\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1552029550. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1800 - accuracy: 0.9375 - val_loss: 0.1534 - val_accuracy: 0.9455\n", - "Epoch 92/96\n", - "256/256 [==============================] - 45s 173ms/step - loss: 0.1601 - accuracy: 0.9536 - val_loss: 0.1803 - val_accuracy: 0.9279\n", - "Epoch 93/96\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1180 - accuracy: 0.9658 - val_loss: 0.2998 - val_accuracy: 0.8958\n", - "Epoch 94/96\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.0983 - accuracy: 0.9709 - val_loss: 0.1664 - val_accuracy: 0.9407\n", - "Epoch 95/96\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0749 - accuracy: 0.9795 - val_loss: 0.2747 - val_accuracy: 0.9247\n", - "Epoch 96/96\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0451 - accuracy: 0.9912 - val_loss: 0.2425 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1533\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1552029550 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1533422768\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "256/256 [==============================] - 49s 181ms/step - loss: 0.1688 - accuracy: 0.9441 - val_loss: 0.1706 - val_accuracy: 0.9407\n", - "Epoch 98/102\n", - "256/256 [==============================] - 45s 173ms/step - loss: 0.1649 - accuracy: 0.9490 - val_loss: 0.2913 - val_accuracy: 0.9022\n", - "Epoch 99/102\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1309 - accuracy: 0.9587 - val_loss: 0.3671 - val_accuracy: 0.8878\n", - "Epoch 100/102\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.0870 - accuracy: 0.9768 - val_loss: 0.2044 - val_accuracy: 0.9311\n", - "Epoch 101/102\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0652 - accuracy: 0.9841 - val_loss: 0.1967 - val_accuracy: 0.9423\n", - "Epoch 102/102\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0432 - accuracy: 0.9905 - val_loss: 0.2328 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-101-0.9423.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1968\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "256/256 [==============================] - 49s 178ms/step - loss: 0.1996 - accuracy: 0.9341 - val_loss: 0.1652 - val_accuracy: 0.9391\n", - "Epoch 104/108\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1715 - accuracy: 0.9470 - val_loss: 0.3157 - val_accuracy: 0.9054\n", - "Epoch 105/108\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1315 - accuracy: 0.9558 - val_loss: 0.1886 - val_accuracy: 0.9407\n", - "Epoch 106/108\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1008 - accuracy: 0.9695 - val_loss: 0.3061 - val_accuracy: 0.9183\n", - "Epoch 107/108\n", - "256/256 [==============================] - 45s 173ms/step - loss: 0.0711 - accuracy: 0.9812 - val_loss: 0.2253 - val_accuracy: 0.9375\n", - "Epoch 108/108\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.0413 - accuracy: 0.9919 - val_loss: 0.2657 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-105-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1886\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m273.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "256/256 [==============================] - 49s 179ms/step - loss: 0.2013 - accuracy: 0.9360 - val_loss: 0.1608 - val_accuracy: 0.9407\n", - "Epoch 110/114\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1561 - accuracy: 0.9431 - val_loss: 0.1782 - val_accuracy: 0.9343\n", - "Epoch 111/114\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1202 - accuracy: 0.9666 - val_loss: 0.3958 - val_accuracy: 0.8894\n", - "Epoch 112/114\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0780 - accuracy: 0.9810 - val_loss: 0.2509 - val_accuracy: 0.9231\n", - "Epoch 113/114\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0485 - accuracy: 0.9888 - val_loss: 0.3369 - val_accuracy: 0.9247\n", - "Epoch 114/114\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.0294 - accuracy: 0.9954 - val_loss: 0.3194 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-109-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1608\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "256/256 [==============================] - 48s 178ms/step - loss: 0.1663 - accuracy: 0.9468 - val_loss: 0.1575 - val_accuracy: 0.9439\n", - "Epoch 116/120\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1488 - accuracy: 0.9468 - val_loss: 0.1760 - val_accuracy: 0.9583\n", - "Epoch 117/120\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.1150 - accuracy: 0.9619 - val_loss: 0.2182 - val_accuracy: 0.9295\n", - "Epoch 118/120\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.0815 - accuracy: 0.9775 - val_loss: 0.1539 - val_accuracy: 0.9503\n", - "Epoch 119/120\n", - "256/256 [==============================] - 44s 172ms/step - loss: 0.0501 - accuracy: 0.9885 - val_loss: 0.1603 - val_accuracy: 0.9503\n", - "Epoch 120/120\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.1937 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-116-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1760\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.950321 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.958333\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1935 - accuracy: 0.9341 - val_loss: 0.2449 - val_accuracy: 0.9247\n", - "Epoch 122/126\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1680 - accuracy: 0.9456 - val_loss: 0.1690 - val_accuracy: 0.9519\n", - "Epoch 123/126\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1332 - accuracy: 0.9595 - val_loss: 0.1742 - val_accuracy: 0.9391\n", - "Epoch 124/126\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0813 - accuracy: 0.9763 - val_loss: 0.2512 - val_accuracy: 0.9199\n", - "Epoch 125/126\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0581 - accuracy: 0.9873 - val_loss: 0.2040 - val_accuracy: 0.9439\n", - "Epoch 126/126\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0363 - accuracy: 0.9915 - val_loss: 0.2348 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-122-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1690\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1753 - accuracy: 0.9434 - val_loss: 0.1615 - val_accuracy: 0.9503\n", - "Epoch 128/132\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1602 - accuracy: 0.9497 - val_loss: 0.1568 - val_accuracy: 0.9519\n", - "Epoch 129/132\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1141 - accuracy: 0.9651 - val_loss: 0.2989 - val_accuracy: 0.8926\n", - "Epoch 130/132\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0841 - accuracy: 0.9775 - val_loss: 0.2142 - val_accuracy: 0.9231\n", - "Epoch 131/132\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0540 - accuracy: 0.9873 - val_loss: 0.1712 - val_accuracy: 0.9471\n", - "Epoch 132/132\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0378 - accuracy: 0.9915 - val_loss: 0.1735 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1568\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1533422768. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "256/256 [==============================] - 49s 179ms/step - loss: 0.1620 - accuracy: 0.9475 - val_loss: 0.1623 - val_accuracy: 0.9487\n", - "Epoch 134/138\n", - "256/256 [==============================] - 44s 173ms/step - loss: 0.1447 - accuracy: 0.9507 - val_loss: 0.1766 - val_accuracy: 0.9375\n", - "Epoch 135/138\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1114 - accuracy: 0.9666 - val_loss: 0.1674 - val_accuracy: 0.9375\n", - "Epoch 136/138\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0812 - accuracy: 0.9773 - val_loss: 0.1690 - val_accuracy: 0.9423\n", - "Epoch 137/138\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0480 - accuracy: 0.9893 - val_loss: 0.1477 - val_accuracy: 0.9567\n", - "Epoch 138/138\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0368 - accuracy: 0.9919 - val_loss: 0.1655 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-137-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1477\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1533422768 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1477165371\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "256/256 [==============================] - 49s 179ms/step - loss: 0.1769 - accuracy: 0.9451 - val_loss: 0.1534 - val_accuracy: 0.9535\n", - "Epoch 140/144\n", - "256/256 [==============================] - 45s 173ms/step - loss: 0.1530 - accuracy: 0.9519 - val_loss: 0.2885 - val_accuracy: 0.8990\n", - "Epoch 141/144\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1160 - accuracy: 0.9634 - val_loss: 0.1553 - val_accuracy: 0.9503\n", - "Epoch 142/144\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0779 - accuracy: 0.9766 - val_loss: 0.1784 - val_accuracy: 0.9391\n", - "Epoch 143/144\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0565 - accuracy: 0.9863 - val_loss: 0.2021 - val_accuracy: 0.9295\n", - "Epoch 144/144\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0349 - accuracy: 0.9932 - val_loss: 0.1458 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-144-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1458\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1477165371 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1457828581\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m275.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "256/256 [==============================] - 49s 182ms/step - loss: 0.1718 - accuracy: 0.9451 - val_loss: 0.1678 - val_accuracy: 0.9583\n", - "Epoch 146/150\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1439 - accuracy: 0.9546 - val_loss: 0.1693 - val_accuracy: 0.9391\n", - "Epoch 147/150\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0999 - accuracy: 0.9678 - val_loss: 0.2323 - val_accuracy: 0.9279\n", - "Epoch 148/150\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0725 - accuracy: 0.9800 - val_loss: 0.1792 - val_accuracy: 0.9487\n", - "Epoch 149/150\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0514 - accuracy: 0.9861 - val_loss: 0.2630 - val_accuracy: 0.9167\n", - "Epoch 150/150\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0287 - accuracy: 0.9939 - val_loss: 0.2022 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-145-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9583333135. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1457828581. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1642 - accuracy: 0.9468 - val_loss: 0.1581 - val_accuracy: 0.9567\n", - "Epoch 152/156\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2170 - val_accuracy: 0.9167\n", - "Epoch 153/156\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1175 - accuracy: 0.9629 - val_loss: 0.1746 - val_accuracy: 0.9375\n", - "Epoch 154/156\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0827 - accuracy: 0.9810 - val_loss: 0.1589 - val_accuracy: 0.9535\n", - "Epoch 155/156\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0487 - accuracy: 0.9880 - val_loss: 0.1704 - val_accuracy: 0.9615\n", - "Epoch 156/156\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0386 - accuracy: 0.9912 - val_loss: 0.1708 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-155-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1704\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.958333 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.961538\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1457828581. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1748 - accuracy: 0.9468 - val_loss: 0.1345 - val_accuracy: 0.9583\n", - "Epoch 158/162\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1506 - accuracy: 0.9556 - val_loss: 0.1497 - val_accuracy: 0.9503\n", - "Epoch 159/162\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1231 - accuracy: 0.9646 - val_loss: 0.1463 - val_accuracy: 0.9503\n", - "Epoch 160/162\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0756 - accuracy: 0.9807 - val_loss: 0.1345 - val_accuracy: 0.9599\n", - "Epoch 161/162\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0556 - accuracy: 0.9861 - val_loss: 0.1793 - val_accuracy: 0.9535\n", - "Epoch 162/162\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0403 - accuracy: 0.9912 - val_loss: 0.1488 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-160-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1345\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1457828581 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1344851404\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1722 - accuracy: 0.9465 - val_loss: 0.1380 - val_accuracy: 0.9567\n", - "Epoch 164/168\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1446 - accuracy: 0.9563 - val_loss: 0.1513 - val_accuracy: 0.9551\n", - "Epoch 165/168\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0937 - accuracy: 0.9717 - val_loss: 0.2236 - val_accuracy: 0.9375\n", - "Epoch 166/168\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0685 - accuracy: 0.9775 - val_loss: 0.2023 - val_accuracy: 0.9407\n", - "Epoch 167/168\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0507 - accuracy: 0.9902 - val_loss: 0.2218 - val_accuracy: 0.9439\n", - "Epoch 168/168\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0256 - accuracy: 0.9958 - val_loss: 0.1981 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1380}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1980\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "256/256 [==============================] - 50s 185ms/step - loss: 0.1736 - accuracy: 0.9446 - val_loss: 0.1688 - val_accuracy: 0.9311\n", - "Epoch 170/174\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.1313 - accuracy: 0.9626 - val_loss: 0.1326 - val_accuracy: 0.9503\n", - "Epoch 171/174\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.1220 - accuracy: 0.9636 - val_loss: 0.1720 - val_accuracy: 0.9359\n", - "Epoch 172/174\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0843 - accuracy: 0.9753 - val_loss: 0.1356 - val_accuracy: 0.9599\n", - "Epoch 173/174\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0540 - accuracy: 0.9863 - val_loss: 0.1855 - val_accuracy: 0.9471\n", - "Epoch 174/174\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0307 - accuracy: 0.9932 - val_loss: 0.2005 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-172-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1357\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1858 - accuracy: 0.9370 - val_loss: 0.1737 - val_accuracy: 0.9391\n", - "Epoch 176/180\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1612 - accuracy: 0.9509 - val_loss: 0.1555 - val_accuracy: 0.9519\n", - "Epoch 177/180\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1049 - accuracy: 0.9700 - val_loss: 0.1565 - val_accuracy: 0.9423\n", - "Epoch 178/180\n", - "256/256 [==============================] - 47s 185ms/step - loss: 0.0882 - accuracy: 0.9766 - val_loss: 0.1467 - val_accuracy: 0.9551\n", - "Epoch 179/180\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0484 - accuracy: 0.9880 - val_loss: 0.2215 - val_accuracy: 0.9295\n", - "Epoch 180/180\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.1765 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1467}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1765\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m357.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1757 - accuracy: 0.9451 - val_loss: 0.2124 - val_accuracy: 0.9295\n", - "Epoch 182/186\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.1258 - accuracy: 0.9612 - val_loss: 0.1451 - val_accuracy: 0.9471\n", - "Epoch 183/186\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0908 - accuracy: 0.9714 - val_loss: 0.1544 - val_accuracy: 0.9407\n", - "Epoch 184/186\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0597 - accuracy: 0.9814 - val_loss: 0.1675 - val_accuracy: 0.9407\n", - "Epoch 185/186\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0437 - accuracy: 0.9885 - val_loss: 0.1770 - val_accuracy: 0.9423\n", - "Epoch 186/186\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0309 - accuracy: 0.9934 - val_loss: 0.1718 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1451}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1718\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "256/256 [==============================] - 52s 190ms/step - loss: 0.1776 - accuracy: 0.9463 - val_loss: 0.1711 - val_accuracy: 0.9391\n", - "Epoch 188/192\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1528 - accuracy: 0.9534 - val_loss: 0.1292 - val_accuracy: 0.9471\n", - "Epoch 189/192\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0933 - accuracy: 0.9744 - val_loss: 0.2000 - val_accuracy: 0.9327\n", - "Epoch 190/192\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0623 - accuracy: 0.9827 - val_loss: 0.2264 - val_accuracy: 0.9375\n", - "Epoch 191/192\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0344 - accuracy: 0.9927 - val_loss: 0.1794 - val_accuracy: 0.9423\n", - "Epoch 192/192\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0360 - accuracy: 0.9907 - val_loss: 0.1729 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-188-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1292\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1344851404 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1292192638\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "256/256 [==============================] - 49s 179ms/step - loss: 0.1605 - accuracy: 0.9482 - val_loss: 0.1399 - val_accuracy: 0.9503\n", - "Epoch 194/198\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1440 - accuracy: 0.9563 - val_loss: 0.1420 - val_accuracy: 0.9503\n", - "Epoch 195/198\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0963 - accuracy: 0.9709 - val_loss: 0.2075 - val_accuracy: 0.9247\n", - "Epoch 196/198\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0608 - accuracy: 0.9817 - val_loss: 0.1606 - val_accuracy: 0.9519\n", - "Epoch 197/198\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0335 - accuracy: 0.9917 - val_loss: 0.1792 - val_accuracy: 0.9455\n", - "Epoch 198/198\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0258 - accuracy: 0.9927 - val_loss: 0.1566 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1399}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1566\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 199/204\n", - "256/256 [==============================] - 49s 181ms/step - loss: 0.1732 - accuracy: 0.9475 - val_loss: 0.1640 - val_accuracy: 0.9407\n", - "Epoch 200/204\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1279 - accuracy: 0.9626 - val_loss: 0.1706 - val_accuracy: 0.9391\n", - "Epoch 201/204\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0953 - accuracy: 0.9734 - val_loss: 0.2047 - val_accuracy: 0.9263\n", - "Epoch 202/204\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0660 - accuracy: 0.9824 - val_loss: 0.1426 - val_accuracy: 0.9567\n", - "Epoch 203/204\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0415 - accuracy: 0.9910 - val_loss: 0.1959 - val_accuracy: 0.9311\n", - "Epoch 204/204\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0274 - accuracy: 0.9934 - val_loss: 0.2281 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1426}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2282\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 205/210\n", - "256/256 [==============================] - 49s 182ms/step - loss: 0.1669 - accuracy: 0.9514 - val_loss: 0.1420 - val_accuracy: 0.9503\n", - "Epoch 206/210\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.1423 - accuracy: 0.9543 - val_loss: 0.1907 - val_accuracy: 0.9343\n", - "Epoch 207/210\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1001 - accuracy: 0.9695 - val_loss: 0.1412 - val_accuracy: 0.9535\n", - "Epoch 208/210\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0686 - accuracy: 0.9790 - val_loss: 0.1942 - val_accuracy: 0.9471\n", - "Epoch 209/210\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0297 - accuracy: 0.9949 - val_loss: 0.2981 - val_accuracy: 0.9359\n", - "Epoch 210/210\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0242 - accuracy: 0.9944 - val_loss: 0.2094 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1412}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2093\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m354.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 211/216\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.1868 - val_accuracy: 0.9375\n", - "Epoch 212/216\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1259 - accuracy: 0.9617 - val_loss: 0.1831 - val_accuracy: 0.9391\n", - "Epoch 213/216\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0919 - accuracy: 0.9712 - val_loss: 0.2217 - val_accuracy: 0.9311\n", - "Epoch 214/216\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0478 - accuracy: 0.9871 - val_loss: 0.2754 - val_accuracy: 0.9087\n", - "Epoch 215/216\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0359 - accuracy: 0.9907 - val_loss: 0.2529 - val_accuracy: 0.9343\n", - "Epoch 216/216\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0211 - accuracy: 0.9961 - val_loss: 0.2805 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1831}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2804\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 217/222\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1722 - accuracy: 0.9502 - val_loss: 0.1632 - val_accuracy: 0.9295\n", - "Epoch 218/222\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1240 - accuracy: 0.9602 - val_loss: 0.1678 - val_accuracy: 0.9327\n", - "Epoch 219/222\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0765 - accuracy: 0.9766 - val_loss: 0.2456 - val_accuracy: 0.9279\n", - "Epoch 220/222\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0513 - accuracy: 0.9858 - val_loss: 0.2473 - val_accuracy: 0.9327\n", - "Epoch 221/222\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0336 - accuracy: 0.9910 - val_loss: 0.2045 - val_accuracy: 0.9391\n", - "Epoch 222/222\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0229 - accuracy: 0.9951 - val_loss: 0.2415 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1632}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m355.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 223/228\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1697 - accuracy: 0.9495 - val_loss: 0.1663 - val_accuracy: 0.9407\n", - "Epoch 224/228\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.1207 - accuracy: 0.9602 - val_loss: 0.2250 - val_accuracy: 0.9375\n", - "Epoch 225/228\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0851 - accuracy: 0.9734 - val_loss: 0.2260 - val_accuracy: 0.9183\n", - "Epoch 226/228\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0534 - accuracy: 0.9844 - val_loss: 0.2689 - val_accuracy: 0.9183\n", - "Epoch 227/228\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0336 - accuracy: 0.9912 - val_loss: 0.1695 - val_accuracy: 0.9487\n", - "Epoch 228/228\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0270 - accuracy: 0.9944 - val_loss: 0.2000 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1663}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1292}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2000\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1292192638. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 229/234\n", - "256/256 [==============================] - 49s 181ms/step - loss: 0.1689 - accuracy: 0.9468 - val_loss: 0.1285 - val_accuracy: 0.9631\n", - "Epoch 230/234\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1272 - accuracy: 0.9563 - val_loss: 0.1452 - val_accuracy: 0.9599\n", - "Epoch 231/234\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0854 - accuracy: 0.9717 - val_loss: 0.1544 - val_accuracy: 0.9455\n", - "Epoch 232/234\n", - "256/256 [==============================] - 47s 185ms/step - loss: 0.0443 - accuracy: 0.9890 - val_loss: 0.2259 - val_accuracy: 0.9471\n", - "Epoch 233/234\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0327 - accuracy: 0.9912 - val_loss: 0.1893 - val_accuracy: 0.9423\n", - "Epoch 234/234\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0184 - accuracy: 0.9973 - val_loss: 0.2051 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-229-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1285\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.961538 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.963141\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1292192638 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1284706891\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m364.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 235/240\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1315 - accuracy: 0.9578 - val_loss: 0.1410 - val_accuracy: 0.9455\n", - "Epoch 236/240\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1090 - accuracy: 0.9663 - val_loss: 0.2091 - val_accuracy: 0.9199\n", - "Epoch 237/240\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0828 - accuracy: 0.9773 - val_loss: 0.2883 - val_accuracy: 0.9103\n", - "Epoch 238/240\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0460 - accuracy: 0.9880 - val_loss: 0.1810 - val_accuracy: 0.9487\n", - "Epoch 239/240\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0343 - accuracy: 0.9912 - val_loss: 0.1887 - val_accuracy: 0.9471\n", - "Epoch 240/240\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0202 - accuracy: 0.9961 - val_loss: 0.2339 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1410}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2340\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m368.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 241/246\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1753 - accuracy: 0.9495 - val_loss: 0.1715 - val_accuracy: 0.9503\n", - "Epoch 242/246\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1165 - accuracy: 0.9619 - val_loss: 0.2048 - val_accuracy: 0.9423\n", - "Epoch 243/246\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0879 - accuracy: 0.9734 - val_loss: 0.2442 - val_accuracy: 0.9247\n", - "Epoch 244/246\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0521 - accuracy: 0.9836 - val_loss: 0.1963 - val_accuracy: 0.9519\n", - "Epoch 245/246\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0258 - accuracy: 0.9946 - val_loss: 0.2522 - val_accuracy: 0.9439\n", - "Epoch 246/246\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0192 - accuracy: 0.9954 - val_loss: 0.2851 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1715}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2852\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m359.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d26-h18_m59_s01\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 247/252\n", - "256/256 [==============================] - 50s 180ms/step - loss: 0.1802 - accuracy: 0.9492 - val_loss: 0.1543 - val_accuracy: 0.9503\n", - "Epoch 248/252\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.1269 - accuracy: 0.9604 - val_loss: 0.1767 - val_accuracy: 0.9455\n", - "Epoch 249/252\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0867 - accuracy: 0.9758 - val_loss: 0.1717 - val_accuracy: 0.9423\n", - "Epoch 250/252\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0567 - accuracy: 0.9841 - val_loss: 0.1735 - val_accuracy: 0.9519\n", - "Epoch 251/252\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0339 - accuracy: 0.9922 - val_loss: 0.2147 - val_accuracy: 0.9439\n", - "Epoch 252/252\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0228 - accuracy: 0.9958 - val_loss: 0.2881 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1543}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2880\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m368.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 253/258\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1547 - accuracy: 0.9504 - val_loss: 0.1559 - val_accuracy: 0.9487\n", - "Epoch 254/258\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1207 - accuracy: 0.9563 - val_loss: 0.1928 - val_accuracy: 0.9423\n", - "Epoch 255/258\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0783 - accuracy: 0.9785 - val_loss: 0.1629 - val_accuracy: 0.9439\n", - "Epoch 256/258\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0500 - accuracy: 0.9873 - val_loss: 0.1587 - val_accuracy: 0.9471\n", - "Epoch 257/258\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0477 - accuracy: 0.9883 - val_loss: 0.1575 - val_accuracy: 0.9407\n", - "Epoch 258/258\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0250 - accuracy: 0.9949 - val_loss: 0.1581 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1559}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1581\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 259/264\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1612 - accuracy: 0.9504 - val_loss: 0.1524 - val_accuracy: 0.9471\n", - "Epoch 260/264\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1203 - accuracy: 0.9631 - val_loss: 0.1503 - val_accuracy: 0.9455\n", - "Epoch 261/264\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0883 - accuracy: 0.9719 - val_loss: 0.1366 - val_accuracy: 0.9471\n", - "Epoch 262/264\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0584 - accuracy: 0.9846 - val_loss: 0.1622 - val_accuracy: 0.9439\n", - "Epoch 263/264\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0304 - accuracy: 0.9934 - val_loss: 0.1895 - val_accuracy: 0.9567\n", - "Epoch 264/264\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0203 - accuracy: 0.9951 - val_loss: 0.1946 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1366}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1947\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m358.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m275.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 265/270\n", - "256/256 [==============================] - 49s 180ms/step - loss: 0.1588 - accuracy: 0.9521 - val_loss: 0.1405 - val_accuracy: 0.9519\n", - "Epoch 266/270\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.1229 - accuracy: 0.9602 - val_loss: 0.1648 - val_accuracy: 0.9407\n", - "Epoch 267/270\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0756 - accuracy: 0.9807 - val_loss: 0.2319 - val_accuracy: 0.9343\n", - "Epoch 268/270\n", - "256/256 [==============================] - 45s 174ms/step - loss: 0.0609 - accuracy: 0.9846 - val_loss: 0.1934 - val_accuracy: 0.9407\n", - "Epoch 269/270\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0374 - accuracy: 0.9905 - val_loss: 0.2329 - val_accuracy: 0.9471\n", - "Epoch 270/270\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.0181 - accuracy: 0.9973 - val_loss: 0.2512 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2513\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m274.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 271/276\n", - "256/256 [==============================] - 51s 184ms/step - loss: 0.1579 - accuracy: 0.9509 - val_loss: 0.1906 - val_accuracy: 0.9343\n", - "Epoch 272/276\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.1273 - accuracy: 0.9624 - val_loss: 0.2282 - val_accuracy: 0.9327\n", - "Epoch 273/276\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0970 - accuracy: 0.9739 - val_loss: 0.1680 - val_accuracy: 0.9519\n", - "Epoch 274/276\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0563 - accuracy: 0.9863 - val_loss: 0.1776 - val_accuracy: 0.9455\n", - "Epoch 275/276\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0373 - accuracy: 0.9912 - val_loss: 0.1876 - val_accuracy: 0.9455\n", - "Epoch 276/276\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0260 - accuracy: 0.9939 - val_loss: 0.1915 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1680}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1916\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m363.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 277/282\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1668 - accuracy: 0.9451 - val_loss: 0.1678 - val_accuracy: 0.9359\n", - "Epoch 278/282\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1198 - accuracy: 0.9580 - val_loss: 0.1721 - val_accuracy: 0.9487\n", - "Epoch 279/282\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0848 - accuracy: 0.9751 - val_loss: 0.1921 - val_accuracy: 0.9327\n", - "Epoch 280/282\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0504 - accuracy: 0.9858 - val_loss: 0.2122 - val_accuracy: 0.9375\n", - "Epoch 281/282\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0362 - accuracy: 0.9902 - val_loss: 0.2112 - val_accuracy: 0.9327\n", - "Epoch 282/282\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0209 - accuracy: 0.9956 - val_loss: 0.2481 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1678}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2367\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m369.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 283/288\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1480 - accuracy: 0.9580 - val_loss: 0.1542 - val_accuracy: 0.9391\n", - "Epoch 284/288\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.1069 - accuracy: 0.9658 - val_loss: 0.1577 - val_accuracy: 0.9439\n", - "Epoch 285/288\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0650 - accuracy: 0.9844 - val_loss: 0.1959 - val_accuracy: 0.9327\n", - "Epoch 286/288\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0519 - accuracy: 0.9875 - val_loss: 0.1977 - val_accuracy: 0.9343\n", - "Epoch 287/288\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0305 - accuracy: 0.9905 - val_loss: 0.2265 - val_accuracy: 0.9151\n", - "Epoch 288/288\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0187 - accuracy: 0.9968 - val_loss: 0.2272 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1542}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 289/294\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1762 - accuracy: 0.9473 - val_loss: 0.2162 - val_accuracy: 0.9455\n", - "Epoch 290/294\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1135 - accuracy: 0.9678 - val_loss: 0.3055 - val_accuracy: 0.8974\n", - "Epoch 291/294\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0840 - accuracy: 0.9761 - val_loss: 0.2265 - val_accuracy: 0.9279\n", - "Epoch 292/294\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0474 - accuracy: 0.9893 - val_loss: 0.2201 - val_accuracy: 0.9327\n", - "Epoch 293/294\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0344 - accuracy: 0.9907 - val_loss: 0.2938 - val_accuracy: 0.9295\n", - "Epoch 294/294\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0204 - accuracy: 0.9963 - val_loss: 0.2755 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2162}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2545\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m373.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 295/300\n", - "256/256 [==============================] - 51s 185ms/step - loss: 0.1516 - accuracy: 0.9563 - val_loss: 0.2346 - val_accuracy: 0.9471\n", - "Epoch 296/300\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1040 - accuracy: 0.9695 - val_loss: 0.1616 - val_accuracy: 0.9359\n", - "Epoch 297/300\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.2492 - val_accuracy: 0.9295\n", - "Epoch 298/300\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0430 - accuracy: 0.9878 - val_loss: 0.5520 - val_accuracy: 0.9391\n", - "Epoch 299/300\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0277 - accuracy: 0.9927 - val_loss: 0.2504 - val_accuracy: 0.9439\n", - "Epoch 300/300\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0185 - accuracy: 0.9963 - val_loss: 0.2979 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1616}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2976\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 301/306\n", - "256/256 [==============================] - 51s 185ms/step - loss: 0.1597 - accuracy: 0.9553 - val_loss: 0.1904 - val_accuracy: 0.9343\n", - "Epoch 302/306\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1218 - accuracy: 0.9609 - val_loss: 0.1980 - val_accuracy: 0.9327\n", - "Epoch 303/306\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0741 - accuracy: 0.9775 - val_loss: 0.2119 - val_accuracy: 0.9311\n", - "Epoch 304/306\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0419 - accuracy: 0.9897 - val_loss: 0.2723 - val_accuracy: 0.9311\n", - "Epoch 305/306\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0236 - accuracy: 0.9941 - val_loss: 0.2654 - val_accuracy: 0.9327\n", - "Epoch 306/306\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0173 - accuracy: 0.9966 - val_loss: 0.2744 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.1904}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2743\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m375.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 307/312\n", - "256/256 [==============================] - 50s 185ms/step - loss: 0.1610 - accuracy: 0.9500 - val_loss: 0.2043 - val_accuracy: 0.9343\n", - "Epoch 308/312\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1183 - accuracy: 0.9592 - val_loss: 0.1794 - val_accuracy: 0.9295\n", - "Epoch 309/312\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0720 - accuracy: 0.9788 - val_loss: 0.1945 - val_accuracy: 0.9295\n", - "Epoch 310/312\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0340 - accuracy: 0.9929 - val_loss: 0.2620 - val_accuracy: 0.9263\n", - "Epoch 311/312\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0259 - accuracy: 0.9939 - val_loss: 0.2835 - val_accuracy: 0.9263\n", - "Epoch 312/312\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0240 - accuracy: 0.9944 - val_loss: 0.2667 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.1794}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2667\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 313/318\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1599 - accuracy: 0.9497 - val_loss: 0.1730 - val_accuracy: 0.9359\n", - "Epoch 314/318\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.1092 - accuracy: 0.9656 - val_loss: 0.2860 - val_accuracy: 0.9054\n", - "Epoch 315/318\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0641 - accuracy: 0.9812 - val_loss: 0.3437 - val_accuracy: 0.9279\n", - "Epoch 316/318\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0368 - accuracy: 0.9897 - val_loss: 0.3023 - val_accuracy: 0.9295\n", - "Epoch 317/318\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0274 - accuracy: 0.9927 - val_loss: 0.3100 - val_accuracy: 0.9439\n", - "Epoch 318/318\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.3571 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1730}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2571\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m370.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 319/324\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1551 - accuracy: 0.9514 - val_loss: 0.1751 - val_accuracy: 0.9375\n", - "Epoch 320/324\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1101 - accuracy: 0.9644 - val_loss: 0.3133 - val_accuracy: 0.9167\n", - "Epoch 321/324\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0659 - accuracy: 0.9805 - val_loss: 0.4211 - val_accuracy: 0.9006\n", - "Epoch 322/324\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0390 - accuracy: 0.9895 - val_loss: 0.2079 - val_accuracy: 0.9343\n", - "Epoch 323/324\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0272 - accuracy: 0.9929 - val_loss: 0.4473 - val_accuracy: 0.9006\n", - "Epoch 324/324\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0224 - accuracy: 0.9941 - val_loss: 0.4203 - val_accuracy: 0.9022\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1751}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9022\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4201\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m375.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 325/330\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1420 - accuracy: 0.9592 - val_loss: 0.2373 - val_accuracy: 0.9038\n", - "Epoch 326/330\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1116 - accuracy: 0.9670 - val_loss: 0.2164 - val_accuracy: 0.9215\n", - "Epoch 327/330\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0674 - accuracy: 0.9800 - val_loss: 0.1920 - val_accuracy: 0.9311\n", - "Epoch 328/330\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.2784 - val_accuracy: 0.9231\n", - "Epoch 329/330\n", - "256/256 [==============================] - 47s 184ms/step - loss: 0.0299 - accuracy: 0.9944 - val_loss: 0.2236 - val_accuracy: 0.9391\n", - "Epoch 330/330\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0192 - accuracy: 0.9958 - val_loss: 0.2388 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1920}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2387\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m367.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 331/336\n", - "256/256 [==============================] - 50s 184ms/step - loss: 0.1605 - accuracy: 0.9539 - val_loss: 0.2191 - val_accuracy: 0.9119\n", - "Epoch 332/336\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.1122 - accuracy: 0.9666 - val_loss: 0.2464 - val_accuracy: 0.9167\n", - "Epoch 333/336\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0759 - accuracy: 0.9780 - val_loss: 0.2621 - val_accuracy: 0.9231\n", - "Epoch 334/336\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0429 - accuracy: 0.9873 - val_loss: 0.2545 - val_accuracy: 0.9311\n", - "Epoch 335/336\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0257 - accuracy: 0.9934 - val_loss: 0.3023 - val_accuracy: 0.9343\n", - "Epoch 336/336\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0208 - accuracy: 0.9944 - val_loss: 0.3138 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.2191}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00908\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 337/342\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1435 - accuracy: 0.9561 - val_loss: 0.2077 - val_accuracy: 0.9359\n", - "Epoch 338/342\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0970 - accuracy: 0.9692 - val_loss: 0.2622 - val_accuracy: 0.9311\n", - "Epoch 339/342\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0615 - accuracy: 0.9824 - val_loss: 0.2184 - val_accuracy: 0.9391\n", - "Epoch 340/342\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0291 - accuracy: 0.9922 - val_loss: 0.2240 - val_accuracy: 0.9359\n", - "Epoch 341/342\n", - "256/256 [==============================] - 48s 185ms/step - loss: 0.0200 - accuracy: 0.9956 - val_loss: 0.2535 - val_accuracy: 0.9455\n", - "Epoch 342/342\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0157 - accuracy: 0.9963 - val_loss: 0.2630 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2077}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2717\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m388.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00902\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 343/348\n", - "256/256 [==============================] - 51s 187ms/step - loss: 0.1574 - accuracy: 0.9529 - val_loss: 0.1972 - val_accuracy: 0.9263\n", - "Epoch 344/348\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.1014 - accuracy: 0.9658 - val_loss: 0.1675 - val_accuracy: 0.9359\n", - "Epoch 345/348\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0612 - accuracy: 0.9783 - val_loss: 0.1686 - val_accuracy: 0.9343\n", - "Epoch 346/348\n", - "256/256 [==============================] - 48s 186ms/step - loss: 0.0375 - accuracy: 0.9905 - val_loss: 0.1797 - val_accuracy: 0.9487\n", - "Epoch 347/348\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0228 - accuracy: 0.9961 - val_loss: 0.2343 - val_accuracy: 0.9391\n", - "Epoch 348/348\n", - "256/256 [==============================] - 47s 184ms/step - loss: 0.0148 - accuracy: 0.9973 - val_loss: 0.1911 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1675}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1911\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00896\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 349/354\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1658 - accuracy: 0.9541 - val_loss: 0.1718 - val_accuracy: 0.9343\n", - "Epoch 350/354\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.1242 - accuracy: 0.9634 - val_loss: 0.2321 - val_accuracy: 0.9183\n", - "Epoch 351/354\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0737 - accuracy: 0.9797 - val_loss: 0.1836 - val_accuracy: 0.9375\n", - "Epoch 352/354\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0465 - accuracy: 0.9890 - val_loss: 0.2034 - val_accuracy: 0.9391\n", - "Epoch 353/354\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0335 - accuracy: 0.9910 - val_loss: 0.1833 - val_accuracy: 0.9487\n", - "Epoch 354/354\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0207 - accuracy: 0.9966 - val_loss: 0.1793 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1718}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1793\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m390.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0089\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 355/360\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1412 - accuracy: 0.9607 - val_loss: 0.1491 - val_accuracy: 0.9487\n", - "Epoch 356/360\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0949 - accuracy: 0.9705 - val_loss: 0.1979 - val_accuracy: 0.9343\n", - "Epoch 357/360\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0541 - accuracy: 0.9875 - val_loss: 0.1727 - val_accuracy: 0.9455\n", - "Epoch 358/360\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0342 - accuracy: 0.9919 - val_loss: 0.2038 - val_accuracy: 0.9375\n", - "Epoch 359/360\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0283 - accuracy: 0.9929 - val_loss: 0.2312 - val_accuracy: 0.9375\n", - "Epoch 360/360\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0118 - accuracy: 0.9990 - val_loss: 0.2565 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1491}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2566\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00884\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 361/366\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1452 - accuracy: 0.9561 - val_loss: 0.1841 - val_accuracy: 0.9327\n", - "Epoch 362/366\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0998 - accuracy: 0.9673 - val_loss: 0.1665 - val_accuracy: 0.9375\n", - "Epoch 363/366\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0691 - accuracy: 0.9802 - val_loss: 0.3034 - val_accuracy: 0.9183\n", - "Epoch 364/366\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0478 - accuracy: 0.9883 - val_loss: 0.2609 - val_accuracy: 0.9343\n", - "Epoch 365/366\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0278 - accuracy: 0.9958 - val_loss: 0.2328 - val_accuracy: 0.9423\n", - "Epoch 366/366\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0173 - accuracy: 0.9980 - val_loss: 0.2608 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1665}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2652\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m373.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00878\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 367/372\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1499 - accuracy: 0.9548 - val_loss: 0.1774 - val_accuracy: 0.9359\n", - "Epoch 368/372\n", - "256/256 [==============================] - 45s 175ms/step - loss: 0.1333 - accuracy: 0.9617 - val_loss: 0.1801 - val_accuracy: 0.9359\n", - "Epoch 369/372\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0664 - accuracy: 0.9832 - val_loss: 0.2012 - val_accuracy: 0.9375\n", - "Epoch 370/372\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0472 - accuracy: 0.9880 - val_loss: 0.2177 - val_accuracy: 0.9375\n", - "Epoch 371/372\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0232 - accuracy: 0.9941 - val_loss: 0.3455 - val_accuracy: 0.9375\n", - "Epoch 372/372\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0182 - accuracy: 0.9963 - val_loss: 0.2887 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1774}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2886\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m277.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00872\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 373/378\n", - "256/256 [==============================] - 50s 182ms/step - loss: 0.1479 - accuracy: 0.9595 - val_loss: 0.1663 - val_accuracy: 0.9359\n", - "Epoch 374/378\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0961 - accuracy: 0.9695 - val_loss: 0.3375 - val_accuracy: 0.9167\n", - "Epoch 375/378\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0637 - accuracy: 0.9822 - val_loss: 0.1676 - val_accuracy: 0.9423\n", - "Epoch 376/378\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0350 - accuracy: 0.9902 - val_loss: 0.4006 - val_accuracy: 0.9167\n", - "Epoch 377/378\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0212 - accuracy: 0.9951 - val_loss: 0.2728 - val_accuracy: 0.9375\n", - "Epoch 378/378\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0156 - accuracy: 0.9968 - val_loss: 0.2517 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1663}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2518\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m369.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m278.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00866\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 379/384\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1409 - accuracy: 0.9590 - val_loss: 0.1699 - val_accuracy: 0.9407\n", - "Epoch 380/384\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0980 - accuracy: 0.9697 - val_loss: 0.2313 - val_accuracy: 0.9311\n", - "Epoch 381/384\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0569 - accuracy: 0.9829 - val_loss: 0.2113 - val_accuracy: 0.9391\n", - "Epoch 382/384\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0316 - accuracy: 0.9907 - val_loss: 0.2091 - val_accuracy: 0.9455\n", - "Epoch 383/384\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0214 - accuracy: 0.9949 - val_loss: 0.2218 - val_accuracy: 0.9439\n", - "Epoch 384/384\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0166 - accuracy: 0.9958 - val_loss: 0.2408 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1699}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2411\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m381.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0086\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 385/390\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1582 - accuracy: 0.9563 - val_loss: 0.1527 - val_accuracy: 0.9359\n", - "Epoch 386/390\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0932 - accuracy: 0.9702 - val_loss: 0.1701 - val_accuracy: 0.9327\n", - "Epoch 387/390\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0601 - accuracy: 0.9846 - val_loss: 0.2358 - val_accuracy: 0.9295\n", - "Epoch 388/390\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.2870 - val_accuracy: 0.9327\n", - "Epoch 389/390\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0196 - accuracy: 0.9939 - val_loss: 0.3137 - val_accuracy: 0.9343\n", - "Epoch 390/390\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0127 - accuracy: 0.9976 - val_loss: 0.2885 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1527}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2886\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m376.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m282.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00854\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 391/396\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1452 - accuracy: 0.9553 - val_loss: 0.1814 - val_accuracy: 0.9359\n", - "Epoch 392/396\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.1002 - accuracy: 0.9690 - val_loss: 0.2043 - val_accuracy: 0.9407\n", - "Epoch 393/396\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0487 - accuracy: 0.9871 - val_loss: 0.1993 - val_accuracy: 0.9423\n", - "Epoch 394/396\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0382 - accuracy: 0.9890 - val_loss: 0.2489 - val_accuracy: 0.9359\n", - "Epoch 395/396\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.2892 - val_accuracy: 0.9263\n", - "Epoch 396/396\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0138 - accuracy: 0.9961 - val_loss: 0.2592 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1814}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2582\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00848\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 397/402\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1513 - accuracy: 0.9565 - val_loss: 0.1768 - val_accuracy: 0.9327\n", - "Epoch 398/402\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0983 - accuracy: 0.9670 - val_loss: 0.1998 - val_accuracy: 0.9295\n", - "Epoch 399/402\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0572 - accuracy: 0.9836 - val_loss: 0.1818 - val_accuracy: 0.9247\n", - "Epoch 400/402\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.2781 - val_accuracy: 0.9263\n", - "Epoch 401/402\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0237 - accuracy: 0.9946 - val_loss: 0.3038 - val_accuracy: 0.9327\n", - "Epoch 402/402\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0153 - accuracy: 0.9961 - val_loss: 0.3205 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1768}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3206\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m383.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00842\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 403/408\n", - "256/256 [==============================] - 51s 187ms/step - loss: 0.1445 - accuracy: 0.9587 - val_loss: 0.1564 - val_accuracy: 0.9375\n", - "Epoch 404/408\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.1045 - accuracy: 0.9670 - val_loss: 0.1896 - val_accuracy: 0.9343\n", - "Epoch 405/408\n", - "256/256 [==============================] - 45s 176ms/step - loss: 0.0665 - accuracy: 0.9814 - val_loss: 0.2436 - val_accuracy: 0.9215\n", - "Epoch 406/408\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0350 - accuracy: 0.9900 - val_loss: 0.3153 - val_accuracy: 0.9327\n", - "Epoch 407/408\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0263 - accuracy: 0.9932 - val_loss: 0.3088 - val_accuracy: 0.9295\n", - "Epoch 408/408\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0158 - accuracy: 0.9973 - val_loss: 0.3129 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1564}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m385.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00836\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 409/414\n", - "256/256 [==============================] - 51s 185ms/step - loss: 0.1507 - accuracy: 0.9609 - val_loss: 0.1957 - val_accuracy: 0.9231\n", - "Epoch 410/414\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.1024 - accuracy: 0.9675 - val_loss: 0.1784 - val_accuracy: 0.9391\n", - "Epoch 411/414\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0554 - accuracy: 0.9819 - val_loss: 0.2212 - val_accuracy: 0.9359\n", - "Epoch 412/414\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0261 - accuracy: 0.9946 - val_loss: 0.3009 - val_accuracy: 0.9343\n", - "Epoch 413/414\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0181 - accuracy: 0.9949 - val_loss: 0.2524 - val_accuracy: 0.9423\n", - "Epoch 414/414\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0161 - accuracy: 0.9966 - val_loss: 0.2830 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1784}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2830\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m386.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0083\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 415/420\n", - "256/256 [==============================] - 50s 183ms/step - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.1950 - val_accuracy: 0.9359\n", - "Epoch 416/420\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0943 - accuracy: 0.9695 - val_loss: 0.1502 - val_accuracy: 0.9519\n", - "Epoch 417/420\n", - "256/256 [==============================] - 47s 184ms/step - loss: 0.0576 - accuracy: 0.9827 - val_loss: 0.2426 - val_accuracy: 0.9375\n", - "Epoch 418/420\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0388 - accuracy: 0.9924 - val_loss: 0.2247 - val_accuracy: 0.9407\n", - "Epoch 419/420\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0186 - accuracy: 0.9963 - val_loss: 0.2401 - val_accuracy: 0.9471\n", - "Epoch 420/420\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0181 - accuracy: 0.9966 - val_loss: 0.2419 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1502}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2419\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m385.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00824\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 421/426\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1338 - accuracy: 0.9619 - val_loss: 0.1441 - val_accuracy: 0.9471\n", - "Epoch 422/426\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0927 - accuracy: 0.9712 - val_loss: 0.1686 - val_accuracy: 0.9439\n", - "Epoch 423/426\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0593 - accuracy: 0.9846 - val_loss: 0.1884 - val_accuracy: 0.9439\n", - "Epoch 424/426\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0420 - accuracy: 0.9883 - val_loss: 0.2641 - val_accuracy: 0.9471\n", - "Epoch 425/426\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0265 - accuracy: 0.9929 - val_loss: 0.1887 - val_accuracy: 0.9455\n", - "Epoch 426/426\n", - "256/256 [==============================] - 47s 182ms/step - loss: 0.0181 - accuracy: 0.9968 - val_loss: 0.2185 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1441}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2184\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00818\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 427/432\n", - "256/256 [==============================] - 51s 188ms/step - loss: 0.1452 - accuracy: 0.9609 - val_loss: 0.1648 - val_accuracy: 0.9407\n", - "Epoch 428/432\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.1003 - accuracy: 0.9702 - val_loss: 0.1981 - val_accuracy: 0.9407\n", - "Epoch 429/432\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0583 - accuracy: 0.9832 - val_loss: 0.2143 - val_accuracy: 0.9247\n", - "Epoch 430/432\n", - "256/256 [==============================] - 45s 177ms/step - loss: 0.0400 - accuracy: 0.9893 - val_loss: 0.2011 - val_accuracy: 0.9407\n", - "Epoch 431/432\n", - "256/256 [==============================] - 46s 177ms/step - loss: 0.0192 - accuracy: 0.9958 - val_loss: 0.2325 - val_accuracy: 0.9407\n", - "Epoch 432/432\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0115 - accuracy: 0.9978 - val_loss: 0.2928 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1648}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2920\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00812\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 433/438\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1506 - accuracy: 0.9561 - val_loss: 0.2703 - val_accuracy: 0.9439\n", - "Epoch 434/438\n", - "256/256 [==============================] - 47s 185ms/step - loss: 0.0979 - accuracy: 0.9675 - val_loss: 0.2695 - val_accuracy: 0.9167\n", - "Epoch 435/438\n", - "256/256 [==============================] - 47s 184ms/step - loss: 0.0595 - accuracy: 0.9827 - val_loss: 0.2406 - val_accuracy: 0.9343\n", - "Epoch 436/438\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0400 - accuracy: 0.9922 - val_loss: 0.2288 - val_accuracy: 0.9407\n", - "Epoch 437/438\n", - "256/256 [==============================] - 47s 183ms/step - loss: 0.0267 - accuracy: 0.9929 - val_loss: 0.2331 - val_accuracy: 0.9391\n", - "Epoch 438/438\n", - "256/256 [==============================] - 46s 179ms/step - loss: 0.0196 - accuracy: 0.9958 - val_loss: 0.2661 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.2288}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2667\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00806\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 439/444\n", - "256/256 [==============================] - 51s 186ms/step - loss: 0.1462 - accuracy: 0.9575 - val_loss: 0.1617 - val_accuracy: 0.9455\n", - "Epoch 440/444\n", - "256/256 [==============================] - 46s 180ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.1821 - val_accuracy: 0.9423\n", - "Epoch 441/444\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0683 - accuracy: 0.9785 - val_loss: 0.3554 - val_accuracy: 0.9391\n", - "Epoch 442/444\n", - "256/256 [==============================] - 47s 181ms/step - loss: 0.0348 - accuracy: 0.9897 - val_loss: 0.2999 - val_accuracy: 0.9407\n", - "Epoch 443/444\n", - "256/256 [==============================] - 46s 178ms/step - loss: 0.0242 - accuracy: 0.9946 - val_loss: 0.3233 - val_accuracy: 0.9407\n", - "Epoch 444/444\n", - "256/256 [==============================] - 46s 181ms/step - loss: 0.0161 - accuracy: 0.9973 - val_loss: 0.4936 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1617}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.1285}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1284706891. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m395.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m283.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.008\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "\n", - "KeyboardInterrupt.\n", - "Training done.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import gc\n", "# Garbage Collection (memory)\n", @@ -21815,7 +2390,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21834,7 +2409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21854,23 +2429,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", "start_time": "2023-12-28T07:04:51.032425100Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[91mFailed to load model history.\n", - "Error: name 'history' is not defined\n" - ] - } - ], + "outputs": [], "source": [ "from turtle import left\n", "import matplotlib.pyplot as plt\n", @@ -22085,78 +2651,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "notebookRunGroups": { "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 4s 4s/step\n", - "20/20 [==============================] - 2s 93ms/step\n", - "Val data acc:\n", - "+-----------+-----------+\n", - "| Metric | Value |\n", - "+-----------+-----------+\n", - "| Accuracy | 93.75 |\n", - "| Precision | 94.444444 |\n", - "| F1 Score | 93.72549 |\n", - "| Recall | 93.75 |\n", - "+-----------+-----------+\n", - "Test data acc:\n", - "+-----------+-----------+\n", - "| Metric | Value |\n", - "+-----------+-----------+\n", - "| Accuracy | 96.474359 |\n", - "| Precision | 96.533528 |\n", - "| F1 Score | 96.451081 |\n", - "| Recall | 96.474359 |\n", - "+-----------+-----------+\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 0s 38ms/step\n" - ] - }, - { - "ename": "error", - "evalue": "OpenCV(4.8.0) :-1: error: (-5:Bad argument) in function 'resize'\n> Overload resolution failed:\n> - src is not a numpy array, neither a scalar\n> - Expected Ptr for argument 'src'\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31merror\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[7], line 55\u001b[0m\n\u001b[0;32m 53\u001b[0m img \u001b[38;5;241m=\u001b[39m x_val[i]\n\u001b[0;32m 54\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m make_gradcam_heatmap(img[np\u001b[38;5;241m.\u001b[39mnewaxis, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], model, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop_activation\u001b[39m\u001b[38;5;124m'\u001b[39m, second_last_conv_layer_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop_conv\u001b[39m\u001b[38;5;124m'\u001b[39m, sensitivity_map \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m) \n\u001b[1;32m---> 55\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheatmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 56\u001b[0m heatmap \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39muint8(\u001b[38;5;241m255\u001b[39m \u001b[38;5;241m*\u001b[39m heatmap)\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# Apply Adaptive Histogram Equalization\u001b[39;00m\n", - "\u001b[1;31merror\u001b[0m: OpenCV(4.8.0) :-1: error: (-5:Bad argument) in function 'resize'\n> Overload resolution failed:\n> - src is not a numpy array, neither a scalar\n> - Expected Ptr for argument 'src'\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 87af27d..0e09db9 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -1,128 +1,128 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -We as members, contributors, and leaders pledge to make participation in our -community a harassment-free experience for everyone, regardless of age, body -size, visible or invisible disability, ethnicity, sex characteristics, gender -identity and expression, level of experience, education, socio-economic status, -nationality, personal appearance, race, religion, or sexual identity -and orientation. - -We pledge to act and interact in ways that contribute to an open, welcoming, -diverse, inclusive, and healthy community. - -## Our Standards - -Examples of behavior that contributes to a positive environment for our -community include: - -* Demonstrating empathy and kindness toward other people -* Being respectful of differing opinions, viewpoints, and experiences -* Giving and gracefully accepting constructive feedback -* Accepting responsibility and apologizing to those affected by our mistakes, - and learning from the experience -* Focusing on what is best not just for us as individuals, but for the - overall community - -Examples of unacceptable behavior include: - -* The use of sexualized language or imagery, and sexual attention or - advances of any kind -* Trolling, insulting or derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or email - address, without their explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Enforcement Responsibilities - -Community leaders are responsible for clarifying and enforcing our standards of -acceptable behavior and will take appropriate and fair corrective action in -response to any behavior that they deem inappropriate, threatening, offensive, -or harmful. - -Community leaders have the right and responsibility to remove, edit, or reject -comments, commits, code, wiki edits, issues, and other contributions that are -not aligned to this Code of Conduct, and will communicate reasons for moderation -decisions when appropriate. - -## Scope - -This Code of Conduct applies within all community spaces, and also applies when -an individual is officially representing the community in public spaces. -Examples of representing our community include using an official e-mail address, -posting via an official social media account, or acting as an appointed -representative at an online or offline event. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported to the community leaders responsible for enforcement at -aydin.hamediasl@gmail.com. -All complaints will be reviewed and investigated promptly and fairly. - -All community leaders are obligated to respect the privacy and security of the -reporter of any incident. - -## Enforcement Guidelines - -Community leaders will follow these Community Impact Guidelines in determining -the consequences for any action they deem in violation of this Code of Conduct: - -### 1. Correction - -**Community Impact**: Use of inappropriate language or other behavior deemed -unprofessional or unwelcome in the community. - -**Consequence**: A private, written warning from community leaders, providing -clarity around the nature of the violation and an explanation of why the -behavior was inappropriate. A public apology may be requested. - -### 2. Warning - -**Community Impact**: A violation through a single incident or series -of actions. - -**Consequence**: A warning with consequences for continued behavior. No -interaction with the people involved, including unsolicited interaction with -those enforcing the Code of Conduct, for a specified period of time. This -includes avoiding interactions in community spaces as well as external channels -like social media. Violating these terms may lead to a temporary or -permanent ban. - -### 3. Temporary Ban - -**Community Impact**: A serious violation of community standards, including -sustained inappropriate behavior. - -**Consequence**: A temporary ban from any sort of interaction or public -communication with the community for a specified period of time. No public or -private interaction with the people involved, including unsolicited interaction -with those enforcing the Code of Conduct, is allowed during this period. -Violating these terms may lead to a permanent ban. - -### 4. Permanent Ban - -**Community Impact**: Demonstrating a pattern of violation of community -standards, including sustained inappropriate behavior, harassment of an -individual, or aggression toward or disparagement of classes of individuals. - -**Consequence**: A permanent ban from any sort of public interaction within -the community. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], -version 2.0, available at -https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. - -Community Impact Guidelines were inspired by [Mozilla's code of conduct -enforcement ladder](https://github.com/mozilla/diversity). - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see the FAQ at -https://www.contributor-covenant.org/faq. Translations are available at -https://www.contributor-covenant.org/translations. +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +aydin.hamediasl@gmail.com. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6b43282..98326f8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1 +1 @@ -### Please make pull requests for Alpha-b branch. +### Please make pull requests for Alpha-b branch. diff --git a/Cache_clear.cmd b/Cache_clear.cmd index 6e80886..4715d15 100644 --- a/Cache_clear.cmd +++ b/Cache_clear.cmd @@ -1,14 +1,14 @@ -@echo off -setlocal - -set "folder=cache" - -for /d %%a in ("%folder%\*") do ( - rd /s /q "%%~fa" -) - -for %%a in ("%folder%\*") do ( - del /f /q "%%~fa" -) - -endlocal +@echo off +setlocal + +set "folder=cache" + +for /d %%a in ("%folder%\*") do ( + rd /s /q "%%~fa" +) + +for %%a in ("%folder%\*") do ( + del /f /q "%%~fa" +) + +endlocal diff --git a/Check_MCUS.py b/Check_MCUS.py index c15454f..e366daa 100644 --- a/Check_MCUS.py +++ b/Check_MCUS.py @@ -1,24 +1,24 @@ -import hashlib -import sys - -def calculate_hash(file_path): - with open(file_path, 'rb') as file: - bytes = file.read() - readable_hash = hashlib.sha256(bytes).hexdigest() - return readable_hash - -def compare_files(file1, file2): - file1_hash = calculate_hash(file1) - file2_hash = calculate_hash(file2) - - if file1_hash == file2_hash: - print(f"The files {file1} and {file2} are identical.") - else: - print(f"The files {file1} and {file2} are different.") - sys.exit(1) - -# Replace with your file paths -file1 = "Model_T&T.ipynb" -file2 = "BETA_E_Model_T&T.ipynb" - -compare_files(file1, file2) +import hashlib +import sys + +def calculate_hash(file_path): + with open(file_path, 'rb') as file: + bytes = file.read() + readable_hash = hashlib.sha256(bytes).hexdigest() + return readable_hash + +def compare_files(file1, file2): + file1_hash = calculate_hash(file1) + file2_hash = calculate_hash(file2) + + if file1_hash == file2_hash: + print(f"The files {file1} and {file2} are identical.") + else: + print(f"The files {file1} and {file2} are different.") + sys.exit(1) + +# Replace with your file paths +file1 = "Model_T&T.ipynb" +file2 = "BETA_E_Model_T&T.ipynb" + +compare_files(file1, file2) diff --git a/Create_requirements.cmd b/Create_requirements.cmd index d9b67c6..24c709c 100644 --- a/Create_requirements.cmd +++ b/Create_requirements.cmd @@ -1,4 +1,4 @@ -@echo off -del requirements.txt >nul 2>&1 -pigar -l INFO generate - +@echo off +del requirements.txt >nul 2>&1 +pigar -l INFO generate + diff --git a/Exports/V4/EPO_src_Gzip_compressed .md b/Exports/V4/EPO_src_Gzip_compressed .md index 3c50448..0ee6535 100644 --- a/Exports/V4/EPO_src_Gzip_compressed .md +++ b/Exports/V4/EPO_src_Gzip_compressed .md @@ -1,4 +1,4 @@ -# Gzip compressed src -``` 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 +# Gzip compressed src +``` 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 ``` \ No newline at end of file diff --git a/Exports/V4/Python_EPO.py b/Exports/V4/Python_EPO.py index 45db659..01cdcf9 100644 --- a/Exports/V4/Python_EPO.py +++ b/Exports/V4/Python_EPO.py @@ -1,1663 +1,1663 @@ -# %% [markdown] -# # keras model -# - -# %% [markdown] -# ## pylibs -# - -# %% -# Main -import os -import time -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -import cv2 -import glob -import pprint -import random -import datetime -import gpu_control -import numpy as np -import pandas as pd -from tqdm import tqdm -from hyperas import optim -from keras.losses import categorical_crossentropy -import tensorflow as tf -from keras.models import Model -from scipy.ndimage import zoom -import matplotlib.pyplot as plt -from model_profiler import model_profiler -from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam -from tensorflow_addons.optimizers import Yogi -from adabelief_tf import AdaBeliefOptimizer -from keras.regularizers import l2 -from keras.models import load_model -from matplotlib import pyplot as plt -from PIL import Image, ImageDraw, ImageFont -from keras import Sequential -from random import randint, choice, shuffle -from keras.callbacks import EarlyStopping -from keras.callbacks import TensorBoard -from keras.utils import to_categorical -from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler -from sklearn.model_selection import train_test_split -from keras.preprocessing.image import ImageDataGenerator -from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, SeparableConv2D, Input, Concatenate, GlobalAveragePooling2D, CuDNNLSTM, concatenate, Reshape -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -# Other -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - - -# %% [markdown] -# ## Conf -# - -# %% [markdown] -# ### Data processing conf - -# %% -# Directory paths -train_dir = 'Data_set/train' -test_dir = 'Data_set/test' -validation_dir = 'Data_set/val' -img_res = [224, 224, 3] -# img_res = [224, 224, 3] -# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) -interpolation_order_IFG = 2 -categorical_IMP = True -Make_EV_DATA = False -R_fill_mode = True -add_img_grain = True -Save_TS = True -ADBD = 1 -OP_HDC = False -SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 -LNTS = 0 -adjust_brightness_Mode = True -RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge -scale_data_NP_M = False - -# %% [markdown] -# ### Training - -# %% -SAVE_TYPE = 'H5' - -# %% [markdown] -# ## data processing -# - -# %% -#scale_data -def scale_data_NP(data): - if scale_data_NP_M: - data = data.astype('float32') - data = (data - 127.5) / 127.5 - return data - else: - return data / 255 -#add_image_grain -def add_image_grain(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -#adjust_brightness -# V1 -def adjust_brightness(images, target_average): - # Calculate the average pixel value of all the images - overall_average = np.mean(images) - - # Iterate over each image in the array - for i in range(len(images)): - # Calculate the average pixel value of the current image - image_average = np.mean(images[i]) - - # Compare the image average with the overall average - if image_average > overall_average + 10: - # Increase brightness by adding a constant value - images[i] = np.clip(images[i] - random.randint(6, 25), 0, 255) - elif image_average < overall_average - 10: - # Decrease brightness by subtracting a constant value - images[i] = np.clip(images[i] + random.randint(6, 25), 0, 255) - - return images -# V2 (Very slow NOT Recommended) -# def adjust_brightness(images, target_average): -# # Calculate the average pixel value of all the images -# overall_average = np.mean(images) - -# # Initialize a variable to keep track of the number of deleted images -# deleted_images = 0 - -# # Create a progress bar -# pbar = tqdm(total=len(images), desc='Processing images') - -# # Iterate over each image in the array -# for i in range(len(images)): -# # Adjust the index to account for deleted images -# adjusted_index = i - deleted_images - -# # Calculate the average pixel value of the current image -# image_average = np.mean(images[adjusted_index]) - -# # Compare the image average with the overall average -# if image_average > overall_average + 50 or image_average < overall_average - 60: -# # If the image brightness is 45 units higher than the overall average, delete the image -# images = np.delete(images, adjusted_index, axis=0) -# # Increment the count of deleted images -# deleted_images += 1 -# elif image_average > overall_average + 10: -# # Increase brightness by adding a random value between 6 and 25 -# images[adjusted_index] = np.clip(images[adjusted_index] - random.randint(6, 25), 0, 255) -# elif image_average < overall_average - 10: -# # Decrease brightness by subtracting a random value between 6 and 25 -# images[adjusted_index] = np.clip(images[adjusted_index] + random.randint(6, 25), 0, 255) - -# # Update the progress bar -# pbar.update(1) - -# # Close the progress bar -# pbar.close() - -# print(f'deleted_images: {deleted_images}') -# return images -#apply_clahe_rgb_array -def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): - # Create a CLAHE object - clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) - - # Iterate over each image in the array - for i in range(len(images)): - # Split the image into color channels - b, g, r = cv2.split(images[i]) - - # Convert the channels to the appropriate format - b = cv2.convertScaleAbs(b) - g = cv2.convertScaleAbs(g) - r = cv2.convertScaleAbs(r) - - # Apply adaptive histogram equalization to each channel - equalized_b = clahe.apply(b) - equalized_g = clahe.apply(g) - equalized_r = clahe.apply(r) - - # Merge the equalized channels back into an image - equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) - - # Replace the original image with the equalized image in the array - images[i] = equalized_image - - return images -#noise_func -def noise_func(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(0.001, 0.024) - intensityL1 = random.uniform(0.005, 0.026) - else: - intensityL2 = random.uniform(0.001, 0.037) - intensityL1 = random.uniform(0.001, 0.037) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.026) # Random intensity between 0 and 0.026 - new_image = add_image_grain(new_image, intensity=intensity) - return new_image -#shuffle_data -def shuffle_data(x, y): - indices = np.arange(x.shape[0]) - np.random.shuffle(indices) - x = x[indices] - y = y[indices] - return x, y -#save_images_to_dir -def save_images_to_dir(images, labels, dir_path): - # create the directory if it doesn't exist - if not os.path.exists(dir_path): - os.makedirs(dir_path) - # iterate over the images and labels - for i, (image, label) in enumerate(zip(images, labels)): - # get the class label - class_label = np.argmax(label) - # create the file path - file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') - # save the image to the file path - plt.imsave(file_path, image.squeeze()) -# Create an ImageDataGenerator for the training set -if OP_HDC: - print_Color('Using OP_HDC IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.86, 1.13), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=interpolation_order_IFG, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -else: - print_Color('Using Def IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.26, - shear_range=0.25, - width_shift_range=0.25, - brightness_range=(0.8, 1.2), - height_shift_range=0.25, - channel_shift_range=100, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -train_datagen_SM = ImageDataGenerator( - horizontal_flip=False, - vertical_flip=False, - rotation_range=20, - zoom_range=0.07, - shear_range=0.07, - width_shift_range=0.07, - brightness_range=(0.99, 1.01), - height_shift_range=0.07, - channel_shift_range=0, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False -) -# Create an iterator for the training set -train_generator_SM = train_datagen_SM.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') -# Create an ImageDataGenerator for the validation set (OP) -if Make_EV_DATA: - val_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the validation set - val_generator = val_datagen.flow_from_directory( - validation_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), - class_mode='binary', - color_mode='rgb') - - # Create an ImageDataGenerator for the test set - test_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the test set - test_generator = test_datagen.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), - class_mode='binary', - color_mode='rgb') -# Load all images and labels into memory -print_Color('Loading all images and labels into memory...', ['yellow']) -x_train, y_train = next(iter(train_generator_SM)) -if Make_EV_DATA: - x_val, y_val = next(iter(val_generator)) - x_test, y_test = next(iter(test_generator)) -# fit parameters from data -# train_datagen.fit(x_train) -#to_categorical (TEMP) -if categorical_IMP: - print_Color('Making categorical data...', ['yellow']) - y_train = to_categorical(y_train, num_classes=2) - if Make_EV_DATA: - y_val = to_categorical(y_val, num_classes=2) - y_test = to_categorical(y_test, num_classes=2) -print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', - ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], - advanced_mode=True) -if ADBD > 0: - for i in range(ADBD): - # ADB_clip_limit Scheduler>>> - if i == 0: - ADB_clip_limit = 1.6 - else: - #V1>>> - CL_SLM = 2.4 - ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) - # Try it in win graphing calculator copy and paste: - # β”Œ-------------┬--┬---------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ - # β””-------------β”΄--β”΄---------------β”˜ - #V2>>> - # CL_SLM_2 = 1.4 - # CL_SLM_Start_2 = 2 - # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) - # Try it in win graphing calculator copy and paste: - # β”Œ-----------------┬--┬-------------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ - # β””-----------------β”΄--β”΄-------------------β”˜ - print(f'> Generating ADB[{i+1}/{ADBD}]...') - # prepare an iterators to scale images - train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) - - # get augmented data - x_train_augmented, y_train_augmented = train_iterator.next() - print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') - print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') - x_train_augmented = np.clip(x_train_augmented, 0, 255) - #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) - x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss - print(f'> └───Adding the Generated ADB...') - # append augmented data to original data - x_train = np.concatenate([x_train, x_train_augmented]) - y_train = np.concatenate([y_train, y_train_augmented]) - #free up memory - del y_train_augmented - del x_train_augmented -# normalizing -print_Color('Normalizing image data...', ['yellow']) -if adjust_brightness_Mode: - x_train = adjust_brightness(x_train, np.mean(x_train)) -x_train = np.clip(x_train, 0, 255) -if RANGE_NOM: - x_train = scale_data_NP(x_train) -y_train = np.array(y_train) -if Make_EV_DATA: - x_test = np.clip(x_test, 0, 255) - x_val = np.clip(x_val, 0, 255) - if RANGE_NOM: - x_val = scale_data_NP(x_val) - y_val = np.array(y_val) - if RANGE_NOM: - x_test = scale_data_NP(x_test) - y_test = np.array(y_test) -# Check the range of image data -print_Color(f'~*Grayscale range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) -# Check the data type of image data -print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) -# Calculate the ratio of two labels -if categorical_IMP: - label_ratio = np.sum(y_train[:, 0]) / (np.sum(y_train[:, 1]) + 1e-10) -else: - label_ratio = np.sum(y_train == 0) / (np.sum(y_train == 1) + 1e-10) -label_ratio_percentage = label_ratio * 100 -print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage:.2f}% NORMAL', ['normal', 'red', 'magenta', 'green'], advanced_mode=True) -print_Color('Setting LNTS...', ['yellow']) -# Get the total number of samples in the arrays -num_samples = x_train.shape[0] -print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -if LNTS != 0: - print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) - print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) - # Generate random indices to select LNTS samples - indices = np.random.choice(num_samples, size=LNTS, replace=False) - # Select the samples using the generated indices - x_selected = x_train[indices] - y_selected = y_train[indices] - x_train = x_selected - y_train = y_selected - #free up memory - del x_selected - del y_selected - del indices - #Debug - num_samples = x_train.shape[0] - print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -# Shuffle the training data -print_Color('shuffling data...', ['yellow']) -x_train, y_train = shuffle_data(x_train, y_train) -#save_images_to_dir -if Save_TS: - print_Color('Saving TS...', ['yellow']) - SITD = np.random.choice(num_samples, size=400, replace=False) - S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) - if RANGE_NOM: - if scale_data_NP_M: - save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) -print_Color('Done.', ['green']) - -# %% [markdown] -# ## Save EV Dataset - -# %% -if Make_EV_DATA: - np.save(f'Database\\x_val{SL_EX}.npy', x_val) - np.save(f'Database\\y_val{SL_EX}.npy', y_val) - np.save(f'Database\\x_test{SL_EX}.npy', x_test) - np.save(f'Database\\y_test{SL_EX}.npy', y_test) - -# %% [markdown] -# ## Load EV Dataset - -# %% -x_val = np.load(f'Database\\x_val{SL_EX}.npy') -y_val = np.load(f'Database\\y_val{SL_EX}.npy') -x_test = np.load(f'Database\\x_test{SL_EX}.npy') -y_test = np.load(f'Database\\y_test{SL_EX}.npy') - -# %% [markdown] -# ## Creating the model -# - -# %% [markdown] -# ### Rev1 -# ``` -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: β‰…95.1 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(EfficientNetB7) -# ``` - -# %% -from keras.applications import EfficientNetB7 - -EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.1 -# ``` -# statuses: S.Ready (can improve) -# Working: βœ… -# Max fine tuned acc: β‰…93.2 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(ConvNeXtLarge) -# ``` - -# %% -from keras.applications import ConvNeXtLarge - -ConvNeXtLarge_M = ConvNeXtLarge(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') -# define new model -model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) - -# compile model -opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy', 'binary_accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.2 -# ``` -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: 95.3 -# Max fine tuned acc TLRev2: 96.47 -# type: transfer learning>>>(EfficientNetB7::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -#FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 - print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model.output) - Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.3 -# ``` -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: ⚠️ -# type: transfer learning>>>(EfficientNetB7|Xception::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -from keras.applications.xception import Xception - -#FUNC -def Combo_Model(freeze_layers1, freeze_layers2): - # Define a common input - common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) - - # Base model 1 - base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) - # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) - base_model1_out = base_model1(common_input) - - # Base model 2 - base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) - # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) - base_model2_out = base_model2(common_input) - - print('Total base_model1 layers: ', len(base_model1.layers)) - print('Total base_model2 layers: ', len(base_model2.layers)) - - # Freeze the specified number of layers in both models - for layer in base_model1.layers[:freeze_layers1]: - layer.trainable = False - for layer in base_model2.layers[:freeze_layers2]: - layer.trainable = False - - # Unfreeze the rest in both models - for layer in base_model1.layers[freeze_layers1:]: - layer.trainable = True - for layer in base_model2.layers[freeze_layers2:]: - layer.trainable = True - - # Combine the output of the two base models - combined = concatenate([base_model1_out, base_model2_out]) - - # adding CDL - base_model_FT = GlobalAveragePooling2D()(combined) - Dense_L1 = Dense(2048, activation='relu', kernel_regularizer=l2(0.04))(base_model_FT) - Dropout_L1 = Dropout(0.4)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - combo_model = Model(inputs=common_input, outputs=predictions) - print('Total model layers: ', len(combo_model.layers)) - - #OPT/compile - opt = SGD(momentum=0.9) - combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return combo_model - -print('Creating the model...') -# Main -freeze_layers_1 = 0 -freeze_layers_2 = 0 -model = Combo_Model(freeze_layers_1, freeze_layers_2) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.4 -# ``` -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: β‰…95.64 -# type: transfer learning>>>(EfficientNetV2XL) -# ``` - -# %% -from keras_efficientnet_v2 import EfficientNetV2XL - -EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.5) -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -freeze_layers = 0 -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 - -#FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 - print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - - # adding LSTM layers - lstm_seq_length = 49 - lstm_input_shape = (lstm_seq_length, base_model.output_shape[1]) - reshape_layer = Reshape(target_shape=(lstm_seq_length, -1))(base_model.output) - lstm_layer = CuDNNLSTM(512, input_shape=lstm_input_shape)(reshape_layer) - - # adding dense layers - Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.04))(lstm_layer) - Dropout_L1 = Dropout(0.4)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - - -# %% [markdown] -# ### V(T) Beta2 - -# %% -from keras.applications import InceptionResNetV2 - -#FUNC -def Eff_B7_NS(freeze_layers): - base_model = InceptionResNetV2(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 - print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model.output) - Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.04))(base_model_FT) - Dropout_L1 = Dropout(0.4)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### LR FINDER - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF/Other -LRF_OPT = SGD(momentum=0.9) -LFR_batch_size = 1 # or any other batch size that fits in your memory -LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) -# Instantiate LrFinder -lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) - -# Start range_test -lr_find.range_test(LRF_dataset) -lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) - -# %% [markdown] -# ## Loading the model - -# %% [markdown] -# ### Loading the full model - -# %% -import efficientnet.tfkeras -# Configuration -PRMC = False -freeze_from_opposite = True -Extra_EXT = '_T' -freeze_layers = 0 -randomly_frozen_layers = 0 -freeze_last_seven = True -# CEC_opt = Adagrad() -# CEC_opt = Yogi() -# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -CEC_opt = SGD(momentum=0.9, nesterov=False) -# CEC_opt = Adam() -# Main -try: - if SAVE_TYPE == 'TF': - model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) - else: - model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) -except (ImportError, IOError) as e: - print(f'\033[91mfailed to load the model ERROR:\n{e}') -else: - print('\033[92mLoading model done.') - if not PRMC: - print('Compiling the AI model...\033[0m') - - for layer in model.layers: - layer.trainable = True - - # Select random layers to freeze - frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) - - for i, layer in enumerate(model.layers): - if i in frozen_layer_indices: - layer.trainable = False - else: - if freeze_from_opposite and (i > len(model.layers) - freeze_layers): - layer.trainable = False - elif (not freeze_from_opposite) and i < freeze_layers: - layer.trainable = False - else: - layer.trainable = True - - for layer in model.layers[-7:]: - layer.trainable = not freeze_last_seven - - model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) - model.summary(show_trainable=True, expand_nested=True) - print('done.') - - -# %% [markdown] -# ### Loading model weights - -# %% -model.load_weights('PAI_model_weights.h5') -print('done.') - -# %% [markdown] -# ## Training - -# %% [markdown] -# #### Usage: -# ##### Start with Rev2 if it didnt work train it a little bit with Rev1 and then train it with Rev2 -# ##### flowchart: -# ![FC](TRAIN_FC.png) - -# %% [markdown] -# #### Rev2 (THE BEST) -# ``` -# Working: βœ… -# Other: -# - Tensorboard doesn't work. -# + Perverts overfitting. -# - Slow training. -# + Achieving higher acc. -# - Some models dont work. -# ``` - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -# CONF -max_epoch = 256 # 128 for small models 256 for full Fine tuning and big models -subset_epoch = 8 # change it if you are using a combined model or a big one| DEF=6 / COMM=8 | Too little can result the model not Learn the patterns and too much makes the model overfit on that subset and perform badly on the next subset -subset_epoch_FT = 6 -PL_epoch = 16 # <=16 for small models and >=24 for big models -subset_size = 2048 -Conf_batch_size_REV2 = 8 -OneCycleLr_MAXLR = 0.01 -OneCycleLr_DEC_A = 0.0005 -OneCycleLr_MINLR = 0.0015 -TerminateOnHighTemp_M = True # can make your training a little bit slower, but it can save your expensive gpu (TURN IT OFF FOR TPU OR CPU TRAINING) -Use_ES_ONSUBT = False -EarlyStopping_P = 5 -BEST_RSN = 'PAI_model_T' -#VAR -OneCycleLr_CUNLR = OneCycleLr_MAXLR -all_histories = [] -best_acc = 0 -best_loss = float('inf') -#Funcs -def add_image_grain_TRLRev2(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(0.001, 0.016) - intensityL1 = random.uniform(0.005, 0.020) - else: - intensityL2 = random.uniform(0.001, 0.027) - intensityL1 = random.uniform(0.001, 0.028) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.022) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -#CONST -train_SUB_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.88, 1.12), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=2, - fill_mode='nearest', - preprocessing_function=noise_func_TRLRev2 - ) -class TerminateOnHighTemp(tf.keras.callbacks.Callback): - def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): - super().__init__() - self.active = active - self.check_every_n_batches = check_every_n_batches - self.high_temp = high_temp - self.low_temp = low_temp - self.pause_time = pause_time - self.batch_counter = 0 - - def on_batch_end(self, batch, logs=None): - if not self.active: - return - self.batch_counter += 1 - if self.batch_counter % self.check_every_n_batches == 0: - temperature = gpu_control.get_temperature() - if temperature > self.high_temp: - print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) - time.sleep(self.pause_time) - while gpu_control.get_temperature() > self.low_temp: - time.sleep(4) - print_Color('Resuming training...', ['yellow']) -#callbacks -steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 -early_stopping = EarlyStopping(monitor='val_accuracy', patience=EarlyStopping_P, verbose=1, restore_best_weights=True, mode='max') -TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, - check_every_n_batches=5, - high_temp=75, - low_temp=58, - pause_time=60) -#MAIN -print('Training the model...') -try: - for epoch in range(1, max_epoch): - # Start Epoch - STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' - C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT - start_FULL_time = time.time() - print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch}~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) - # DP - print_Color('Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - print_Color(f'~*Taking a subset of ~*[{subset_size}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) - x_SUB_train = x_train[subset_indices] - y_SUB_train = y_train[subset_indices] - print_Color('Augmenting data...', ['yellow']) - train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, y_SUB_train, shuffle=False, batch_size=len(x_SUB_train)).next() - x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) / 255 - y_SUB_train = train_SUB_augmented_images[1] - # learning_rate_schedule_SUB - if epoch > PL_epoch and OneCycleLr_CUNLR > OneCycleLr_MINLR: - OneCycleLr_CUNLR -= OneCycleLr_DEC_A - - learning_rate_schedule_SUB = OneCycleLr(max_lr=OneCycleLr_CUNLR, steps_per_epoch=steps_per_epoch_train_SUB, epochs=C_subset_epoch) - #FV - print_Color(f'~*Setting model OneCycleLr::maxlr to ~*[{OneCycleLr_CUNLR:.6f}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - print_Color(f'~*Setting model subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - # Train - print_Color('Training on subset...', ['green']) - start_SUBO_time = time.time() - SUB_history = model.fit(x_SUB_train, - y_SUB_train, - epochs=C_subset_epoch, - batch_size=Conf_batch_size_REV2, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[learning_rate_schedule_SUB, - TerminateOnHighTemp_CB, - early_stopping] if Use_ES_ONSUBT else [learning_rate_schedule_SUB, - TerminateOnHighTemp_CB] - ) - end_SUBO_time = time.time() - print_Color('Subset training done.', ['green']) - all_histories.append(SUB_history.history) - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Evaluate the model on the test data - evaluation = model.evaluate(x_test, y_test, verbose=0) - - # Extract the loss and accuracy from the evaluation results - loss = evaluation[0] - acc = evaluation[1] - - # If the accuracy is higher than the best_acc - if acc > best_acc: - print("Improved model accuracy from {} to {}. Saving model.".format(best_acc, acc)) - - # Update the best_acc - best_acc = acc - - # Save the model - if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(BEST_RSN, save_format='tf') - else: - model.save(f'{BEST_RSN}.h5') - else: - print("Model accuracy did not improve from {}. Not saving model.".format(best_acc)) - - # If the loss is higher than the best_loss - if loss < best_loss: - print("Improved model loss from {} to {}. Saving model.".format(best_loss, loss)) - - # Update the best_acc - best_loss = loss - - # Save the model - if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(BEST_RSN + '_BL', save_format='tf') - else: - model.save(f'{BEST_RSN}_BL.h5') - else: - print("Model loss did not improve from {}. Not saving model.".format(best_loss)) - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Epoch end - end_time = time.time() - epoch_time = end_time - start_FULL_time - print(f"Time taken for epoch(FULL) {epoch}: {epoch_time:.2f} sec") - epoch_SUB_time = end_SUBO_time - start_SUBO_time - print(f"Time taken for epoch(SUBo) {epoch}: {epoch_SUB_time:.2f} sec") - print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) -except KeyboardInterrupt: - print('\nKeyboardInterrupt.') -# End -history = {} -for key in all_histories[0].keys(): - # For each metric, concatenate the values from all histories - history[key] = np.concatenate([h[key] for h in all_histories]) -print('Training done.\n') - -# %% [markdown] -# #### Rev1 -# ``` -# Working: βœ… -# Other: -# + Tensorboard works. -# - Can cause overfitting. -# ``` - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF -WTD_augmentation = True -Conf_batch_size = 4 -Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) -#TensorBoard conf -TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) -# Learning rate configuration -Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr -OneCycleLr_MAXLR = 0.0174 -# First time -if Learning_rate_conf == 1: - learning_rate_start = 8e-04 - learning_rate_max = 5e-03 - learning_rate_min = 5e-05 - learning_rate_rampup_epochs = 5 - learning_rate_sustain_epochs = 1 - learning_rate_exp_decay = .3 - #TEMP - # learning_rate_start = 8e-04 - # learning_rate_max = 1e-02 - # learning_rate_min = 8e-04 - # learning_rate_rampup_epochs = 5 - # learning_rate_sustain_epochs = 3 - # learning_rate_exp_decay = .45 -# 2th time -if Learning_rate_conf == 2: - if Learning_rate_conf_SET2C == 1: - learning_rate_start = 4.10e-06 - learning_rate_max = 4.10e-06 - learning_rate_min = 4.10e-06 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 2: - learning_rate_start = 4e-07 - learning_rate_max = 4e-07 - learning_rate_min = 4e-07 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 3: - learning_rate_start = 5e-04 - learning_rate_max = 5e-04 - learning_rate_min = 5e-04 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 -# Function to build learning rate schedule -if Learning_rate_conf in [1,2]: - def build_learning_rate_fn(lr_start=learning_rate_start, - lr_max=learning_rate_max, - lr_min=learning_rate_min, - lr_rampup_epochs=learning_rate_rampup_epochs, - lr_sustain_epochs=learning_rate_sustain_epochs, - lr_exp_decay=learning_rate_exp_decay): - lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync - def learning_rate_fn(epoch): - if epoch < lr_rampup_epochs: - lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start - elif epoch < lr_rampup_epochs + lr_sustain_epochs: - lr = lr_max - else: - lr = (lr_max - lr_min) *\ - lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min - return lr - return learning_rate_fn -#WTD_augmentation -if WTD_augmentation: - print_Color('Using WTD_augmentation...', ['yellow']) - def TF_add_image_grain(image, intensity = 0.01): - # Generate random noise array in the range [0, 1] - noise = tf.random.uniform(shape=tf.shape(image), minval=0, maxval=1, dtype=tf.float32) - - # Scale the noise array - scaled_noise = noise * intensity - - # Add the noise to the image - noisy_image = tf.math.add(image, scaled_noise) - - # Clip - if RANGE_NOM: - noisy_image = tf.clip_by_value(noisy_image, -1.0, 1.0) - else: - noisy_image = tf.clip_by_value(noisy_image, 0.0, 255.0) - - return noisy_image - # Function to augment images - def augment_images(image, label): - image = tf.image.random_flip_left_right(image) - image = tf.image.random_flip_up_down(image) - image = tf.image.random_contrast(image, 0.2, 1.8) - image = tf.image.random_brightness(image, max_delta=0.3) - # Random intensity between 0 and 0.04 - intensity = random.uniform(0, 0.04) - image = TF_add_image_grain(image, intensity=intensity) - # Add random rotation - # image = tf.image.rot90(image, k=random.randint(0, 3)) - return image, label - - # Create TensorFlow dataset - AUTO = tf.data.experimental.AUTOTUNE - train_dataset = ( - tf.data.Dataset.from_tensor_slices((x_train, y_train)) - .map(augment_images, num_parallel_calls=AUTO) - .repeat() - .shuffle(len(x_train)) - .batch(Conf_batch_size) - .prefetch(AUTO) - ) - -# Calculate steps per epoch -steps_per_epoch_train = len(x_train) // Conf_batch_size - -# Set up callbacks -class EpochEndMON(tf.keras.callbacks.Callback): - def on_epoch_end(self, epoch, logs=None): - optimizer = self.model.optimizer - if hasattr(optimizer, 'lr'): - lr = tf.keras.backend.get_value(optimizer.lr) - print(f'\nLearning rate for epoch {epoch+1} is {lr}') - if hasattr(optimizer, 'momentum'): - momentum = tf.keras.backend.get_value(optimizer.momentum) - print(f'Momentum for epoch {epoch+1} is {momentum}') - if logs: - val_loss = logs.get('val_loss') - val_acc = logs.get('val_accuracy') - print(f'Validation loss for epoch {epoch+1} is {val_loss}') - print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') - - print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') - -# Instantiate the callback -EpochEndMON_callback = EpochEndMON() -if Learning_rate_conf in [1,2]: - learning_rate_fn = build_learning_rate_fn() - learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) -else: - learning_rate_schedule = OneCycleLr(max_lr=OneCycleLr_MAXLR, steps_per_epoch=steps_per_epoch_train, epochs=20) -if SAVE_TYPE == 'TF': - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -else: - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' -tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq) - -# Train the model -print('Log dir:', log_dir) -#MInfo -print("Input Shape:", model.input_shape) -print("Output Shape:", model.output_shape) -print("Loss Function:", model.loss) -print('Training the model...\n') -if WTD_augmentation: - history = model.fit(train_dataset, - epochs=256, - steps_per_epoch=steps_per_epoch_train, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) -else: - history = model.fit(x_train, - y_train, - epochs=256, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) -print('Training done.\n') - -# %% [markdown] -# ## Saving model weights -# - -# %% -Extra_EXT = '_T' -# Save the weights -print('Saving weights...') -model.save_weights('PAI_model_weights.h5') -print('Saving full model...') -if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') -else: - try: - model.save(f'PAI_model{Extra_EXT}.h5') - except ValueError: - print('failed to save in .h5 format!') - print('Saving full model in tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') - -# %% [markdown] -# ## Garbage Collection (memory) - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() - -# %% [markdown] -# ## Analyse model Training performance - -# %% -def convert_history(history): - if isinstance(history, tf.keras.callbacks.History): - return history.history - else: - return history -try: - EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' - history = convert_history(history) - #loss - plt.plot(history['loss'], label='loss') - try: - plt.plot(history['val_loss'], label='val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_loss.') - plt.title('Model Loss') - plt.ylabel('Loss') - plt.xlabel(EPM) - plt.grid(True) - plt.ylim(top=(max(history['val_loss'][8:]) + min(history['val_loss'])) / 2, bottom=0) - plt.show() - #acc - plt.plot(history['accuracy'], label='accuracy') - try: - plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy.') - plt.title('Model Accuracy') - plt.ylabel('Accuracy') - plt.xlabel(EPM) - plt.grid(True) - plt.show() -except (ValueError, NameError): - print('\033[91mfailed to load model history.') - -# %% [markdown] -# ## Analyse model Predicting performance - -# %% [markdown] -# ### Gradcam heatmap - -# %% [markdown] -# #### V2 - -# %% -def compute_heatmap(model, img_array, conv_layer_name, pred_index): - """ - Helper function to compute the heatmap for a given convolutional layer. - """ - grad_model = tf.keras.models.Model( - [model.inputs], - [model.get_layer(conv_layer_name).output, model.output] - ) - - with tf.GradientTape() as tape: - conv_layer_output, preds = grad_model(img_array) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, conv_layer_output) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - - conv_layer_output = conv_layer_output[0] - heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] - heatmap = tf.squeeze(heatmap) - heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) - return heatmap - -def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0): - """ - Function to compute the Grad-CAM heatmap for a specific class, given an input image. - """ - if pred_index is None: - preds = model.predict(img_array) - pred_index = tf.argmax(preds[0]) - - # Compute heatmap for the last convolutional layer - heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index) - - # Apply threshold and adjust sensitivity - heatmap = np.where(heatmap > threshold, heatmap, 0) - heatmap = heatmap ** sensitivity_map - - if second_last_conv_layer_name is not None: - # Compute heatmap for the second last convolutional layer - heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) - - # Apply threshold and adjust sensitivity - heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0) - heatmap_second = heatmap_second ** sensitivity_map - - # Average the two heatmaps - heatmap = (heatmap + heatmap_second) / 2.0 - - return heatmap - -# %% [markdown] -# #### V3 - -# %% [markdown] -# ### Main test - -# %% -import seaborn as sns -from sklearn.metrics import confusion_matrix, accuracy_score -from scipy.stats import binom -from tqdm import tqdm -import efficientnet.tfkeras -import cv2 -import gc -# Garbage Collection (memory) -gc.collect() - -Extra_EXT = '_T' -prob_L = 0.9995 -tick_spacing = 5 -Train_data_test = False -if SAVE_TYPE == 'TF': - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}') -else: - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}.h5') - -# Ensure the model's input_shape matches your data -assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' - -# Make predictions on validation data -val_predictions = model.predict(x_val) -val_predictions = np.argmax(val_predictions, axis=1) - -# Make predictions on Train data -if Train_data_test: - Train_predictions = model.predict(x_train) - Train_predictions = np.argmax(Train_predictions, axis=1) - -# Make predictions on test data -test_predictions = model.predict(x_test) -test_predictions = np.argmax(test_predictions, axis=1) - -# Convert y_val and y_test from one-hot encoder to their original form -y_val_original = np.argmax(y_val, axis=1) -y_test_original = np.argmax(y_test, axis=1) -if Train_data_test: - y_train_original = np.argmax(y_train, axis=1) - -# Calculate accuracy on validation data -val_accuracy = accuracy_score(y_val_original, val_predictions) - -# Calculate accuracy on Train data -if Train_data_test: - Train_accuracy = accuracy_score(y_val_original, Train_predictions) - -# Calculate accuracy on test data -test_accuracy = accuracy_score(y_test_original, test_predictions) - -# Print acc -if Train_data_test: - print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}') -print(f'The accuracy of the model on validation data is {val_accuracy:.2%}') -print(f'The accuracy of the model on test data is {test_accuracy:.2%}') - -# Visualize the predictions on validation data as a grid of squares -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - plt.imshow(x_val[i]) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() -#Heatmap -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - img = x_val[i] - heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) - heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) - heatmap = np.uint8(255 * heatmap) - # Apply Adaptive Histogram Equalization - clahe = cv2.createCLAHE(clipLimit=4, tileGridSize=(4,4)) # Create CLAHE object - heatmap = clahe.apply(heatmap) - heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) - if RANGE_NOM: - superimposed_img = (heatmap / 255) * 0.5 + img - else: - superimposed_img = (heatmap / 255) * 0.5 + (img / 255) - #clip - superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] - plt.imshow(superimposed_img) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() - -# Define the list of labels -labels = ['NORMAL', 'PNEUMONIA'] - -# Create a confusion matrix for validation data -val_cm = confusion_matrix(y_val_original, val_predictions) - -# Create a confusion matrix for test data -test_cm = confusion_matrix(y_test_original, test_predictions) - -# Plot the confusion matrix as a heatmap for validation data -plt.figure(figsize=(8, 6)) -sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Validation Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Plot the confusion matrix as a heatmap for test data -plt.figure(figsize=(8, 6)) -sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Test Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Define the range of test data sizes to use -data_sizes = range(1, len(x_test), 4) -# Calculate the probability of a wrong prediction based on test accuracy -prob_wrong = 1 - test_accuracy - -# Create a list to store the number of incorrect predictions for each test data size -incorrect_predictions = [] - -# Generate predictions and track incorrect predictions for each data size -for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): - # Garbage Collection (memory) - gc.collect() - # Randomly select a subset of test data - indices = np.random.choice(len(x_test), size, replace=False) - x_test_subset = x_test[indices] - y_test_subset = y_test[indices] - - # Make predictions on the subset of test data - test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) - test_predictions = np.argmax(test_predictions, axis=1) - y_test_original_subset = np.argmax(y_test_subset, axis=1) - - # Calculate the number of incorrect predictions - incorrect_preds = np.sum(test_predictions != y_test_original_subset) - incorrect_predictions.append(incorrect_preds) - -# Plot the number of incorrect predictions vs. the number of data points -plt.figure(figsize=(10, 6)) -plt.plot(data_sizes, incorrect_predictions) -plt.xlabel('Number of Data Points') -plt.ylabel('Number of Incorrect Predictions') -# Add gridlines for the x and y axes -plt.grid(True) - -# Change the tick spacing for the x and y axes -plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) -plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) - -plt.title('Number of Incorrect Predictions vs. Number of Data Points') -plt.show() - -# Define the range of test data sizes to use -data_sizes = range(1, len(x_test), 1) - -# Calculate the probability of a wrong prediction based on test accuracy -prob_wrong = 1 - test_accuracy - -# Create a list to store the probability of getting at least one wrong answer for each test data size -probabilities = [] - -# Calculate the probability of getting at least one wrong answer for each data size -for size in data_sizes: - # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 - cdf = binom.cdf(0, size, prob_wrong) - # Subtract the CDF from 1 to get the probability of getting at least one wrong answer - prob = 1 - cdf - probabilities.append(prob) - -# Find the index of the first data point that has a probability greater than prob_L% -index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) - -# Limit the x-axis to the first data point that has a probability greater than prob_L% -data_sizes = data_sizes[:index+1] -probabilities = probabilities[:index+1] - -# Plot the probability vs. the number of data points -plt.figure(figsize=(10, 6)) -plt.plot(data_sizes, probabilities) -plt.xlabel('Number of Data Points') -plt.ylabel('Probability') - -# Add gridlines for the x and y axes -plt.grid(True) - -# Change the tick spacing for the x and y axes -plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 2)) -plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100)) - -plt.ylim(top=1.01) - -plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') -plt.show() - - +# %% [markdown] +# # keras model +# + +# %% [markdown] +# ## pylibs +# + +# %% +# Main +import os +import time +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +import cv2 +import glob +import pprint +import random +import datetime +import gpu_control +import numpy as np +import pandas as pd +from tqdm import tqdm +from hyperas import optim +from keras.losses import categorical_crossentropy +import tensorflow as tf +from keras.models import Model +from scipy.ndimage import zoom +import matplotlib.pyplot as plt +from model_profiler import model_profiler +from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam +from tensorflow_addons.optimizers import Yogi +from adabelief_tf import AdaBeliefOptimizer +from keras.regularizers import l2 +from keras.models import load_model +from matplotlib import pyplot as plt +from PIL import Image, ImageDraw, ImageFont +from keras import Sequential +from random import randint, choice, shuffle +from keras.callbacks import EarlyStopping +from keras.callbacks import TensorBoard +from keras.utils import to_categorical +from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, SeparableConv2D, Input, Concatenate, GlobalAveragePooling2D, CuDNNLSTM, concatenate, Reshape +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +# Other +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + + +# %% [markdown] +# ## Conf +# + +# %% [markdown] +# ### Data processing conf + +# %% +# Directory paths +train_dir = 'Data_set/train' +test_dir = 'Data_set/test' +validation_dir = 'Data_set/val' +img_res = [224, 224, 3] +# img_res = [224, 224, 3] +# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) +interpolation_order_IFG = 2 +categorical_IMP = True +Make_EV_DATA = False +R_fill_mode = True +add_img_grain = True +Save_TS = True +ADBD = 1 +OP_HDC = False +SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 +LNTS = 0 +adjust_brightness_Mode = True +RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge +scale_data_NP_M = False + +# %% [markdown] +# ### Training + +# %% +SAVE_TYPE = 'H5' + +# %% [markdown] +# ## data processing +# + +# %% +#scale_data +def scale_data_NP(data): + if scale_data_NP_M: + data = data.astype('float32') + data = (data - 127.5) / 127.5 + return data + else: + return data / 255 +#add_image_grain +def add_image_grain(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +#adjust_brightness +# V1 +def adjust_brightness(images, target_average): + # Calculate the average pixel value of all the images + overall_average = np.mean(images) + + # Iterate over each image in the array + for i in range(len(images)): + # Calculate the average pixel value of the current image + image_average = np.mean(images[i]) + + # Compare the image average with the overall average + if image_average > overall_average + 10: + # Increase brightness by adding a constant value + images[i] = np.clip(images[i] - random.randint(6, 25), 0, 255) + elif image_average < overall_average - 10: + # Decrease brightness by subtracting a constant value + images[i] = np.clip(images[i] + random.randint(6, 25), 0, 255) + + return images +# V2 (Very slow NOT Recommended) +# def adjust_brightness(images, target_average): +# # Calculate the average pixel value of all the images +# overall_average = np.mean(images) + +# # Initialize a variable to keep track of the number of deleted images +# deleted_images = 0 + +# # Create a progress bar +# pbar = tqdm(total=len(images), desc='Processing images') + +# # Iterate over each image in the array +# for i in range(len(images)): +# # Adjust the index to account for deleted images +# adjusted_index = i - deleted_images + +# # Calculate the average pixel value of the current image +# image_average = np.mean(images[adjusted_index]) + +# # Compare the image average with the overall average +# if image_average > overall_average + 50 or image_average < overall_average - 60: +# # If the image brightness is 45 units higher than the overall average, delete the image +# images = np.delete(images, adjusted_index, axis=0) +# # Increment the count of deleted images +# deleted_images += 1 +# elif image_average > overall_average + 10: +# # Increase brightness by adding a random value between 6 and 25 +# images[adjusted_index] = np.clip(images[adjusted_index] - random.randint(6, 25), 0, 255) +# elif image_average < overall_average - 10: +# # Decrease brightness by subtracting a random value between 6 and 25 +# images[adjusted_index] = np.clip(images[adjusted_index] + random.randint(6, 25), 0, 255) + +# # Update the progress bar +# pbar.update(1) + +# # Close the progress bar +# pbar.close() + +# print(f'deleted_images: {deleted_images}') +# return images +#apply_clahe_rgb_array +def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): + # Create a CLAHE object + clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) + + # Iterate over each image in the array + for i in range(len(images)): + # Split the image into color channels + b, g, r = cv2.split(images[i]) + + # Convert the channels to the appropriate format + b = cv2.convertScaleAbs(b) + g = cv2.convertScaleAbs(g) + r = cv2.convertScaleAbs(r) + + # Apply adaptive histogram equalization to each channel + equalized_b = clahe.apply(b) + equalized_g = clahe.apply(g) + equalized_r = clahe.apply(r) + + # Merge the equalized channels back into an image + equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) + + # Replace the original image with the equalized image in the array + images[i] = equalized_image + + return images +#noise_func +def noise_func(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(0.001, 0.024) + intensityL1 = random.uniform(0.005, 0.026) + else: + intensityL2 = random.uniform(0.001, 0.037) + intensityL1 = random.uniform(0.001, 0.037) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.026) # Random intensity between 0 and 0.026 + new_image = add_image_grain(new_image, intensity=intensity) + return new_image +#shuffle_data +def shuffle_data(x, y): + indices = np.arange(x.shape[0]) + np.random.shuffle(indices) + x = x[indices] + y = y[indices] + return x, y +#save_images_to_dir +def save_images_to_dir(images, labels, dir_path): + # create the directory if it doesn't exist + if not os.path.exists(dir_path): + os.makedirs(dir_path) + # iterate over the images and labels + for i, (image, label) in enumerate(zip(images, labels)): + # get the class label + class_label = np.argmax(label) + # create the file path + file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') + # save the image to the file path + plt.imsave(file_path, image.squeeze()) +# Create an ImageDataGenerator for the training set +if OP_HDC: + print_Color('Using OP_HDC IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.86, 1.13), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=interpolation_order_IFG, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +else: + print_Color('Using Def IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.26, + shear_range=0.25, + width_shift_range=0.25, + brightness_range=(0.8, 1.2), + height_shift_range=0.25, + channel_shift_range=100, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +train_datagen_SM = ImageDataGenerator( + horizontal_flip=False, + vertical_flip=False, + rotation_range=20, + zoom_range=0.07, + shear_range=0.07, + width_shift_range=0.07, + brightness_range=(0.99, 1.01), + height_shift_range=0.07, + channel_shift_range=0, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False +) +# Create an iterator for the training set +train_generator_SM = train_datagen_SM.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') +# Create an ImageDataGenerator for the validation set (OP) +if Make_EV_DATA: + val_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the validation set + val_generator = val_datagen.flow_from_directory( + validation_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), + class_mode='binary', + color_mode='rgb') + + # Create an ImageDataGenerator for the test set + test_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the test set + test_generator = test_datagen.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), + class_mode='binary', + color_mode='rgb') +# Load all images and labels into memory +print_Color('Loading all images and labels into memory...', ['yellow']) +x_train, y_train = next(iter(train_generator_SM)) +if Make_EV_DATA: + x_val, y_val = next(iter(val_generator)) + x_test, y_test = next(iter(test_generator)) +# fit parameters from data +# train_datagen.fit(x_train) +#to_categorical (TEMP) +if categorical_IMP: + print_Color('Making categorical data...', ['yellow']) + y_train = to_categorical(y_train, num_classes=2) + if Make_EV_DATA: + y_val = to_categorical(y_val, num_classes=2) + y_test = to_categorical(y_test, num_classes=2) +print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', + ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], + advanced_mode=True) +if ADBD > 0: + for i in range(ADBD): + # ADB_clip_limit Scheduler>>> + if i == 0: + ADB_clip_limit = 1.6 + else: + #V1>>> + CL_SLM = 2.4 + ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) + # Try it in win graphing calculator copy and paste: + # β”Œ-------------┬--┬---------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ + # β””-------------β”΄--β”΄---------------β”˜ + #V2>>> + # CL_SLM_2 = 1.4 + # CL_SLM_Start_2 = 2 + # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) + # Try it in win graphing calculator copy and paste: + # β”Œ-----------------┬--┬-------------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ + # β””-----------------β”΄--β”΄-------------------β”˜ + print(f'> Generating ADB[{i+1}/{ADBD}]...') + # prepare an iterators to scale images + train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) + + # get augmented data + x_train_augmented, y_train_augmented = train_iterator.next() + print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') + print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') + x_train_augmented = np.clip(x_train_augmented, 0, 255) + #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) + x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss + print(f'> └───Adding the Generated ADB...') + # append augmented data to original data + x_train = np.concatenate([x_train, x_train_augmented]) + y_train = np.concatenate([y_train, y_train_augmented]) + #free up memory + del y_train_augmented + del x_train_augmented +# normalizing +print_Color('Normalizing image data...', ['yellow']) +if adjust_brightness_Mode: + x_train = adjust_brightness(x_train, np.mean(x_train)) +x_train = np.clip(x_train, 0, 255) +if RANGE_NOM: + x_train = scale_data_NP(x_train) +y_train = np.array(y_train) +if Make_EV_DATA: + x_test = np.clip(x_test, 0, 255) + x_val = np.clip(x_val, 0, 255) + if RANGE_NOM: + x_val = scale_data_NP(x_val) + y_val = np.array(y_val) + if RANGE_NOM: + x_test = scale_data_NP(x_test) + y_test = np.array(y_test) +# Check the range of image data +print_Color(f'~*Grayscale range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) +# Check the data type of image data +print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) +# Calculate the ratio of two labels +if categorical_IMP: + label_ratio = np.sum(y_train[:, 0]) / (np.sum(y_train[:, 1]) + 1e-10) +else: + label_ratio = np.sum(y_train == 0) / (np.sum(y_train == 1) + 1e-10) +label_ratio_percentage = label_ratio * 100 +print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage:.2f}% NORMAL', ['normal', 'red', 'magenta', 'green'], advanced_mode=True) +print_Color('Setting LNTS...', ['yellow']) +# Get the total number of samples in the arrays +num_samples = x_train.shape[0] +print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +if LNTS != 0: + print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) + print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) + # Generate random indices to select LNTS samples + indices = np.random.choice(num_samples, size=LNTS, replace=False) + # Select the samples using the generated indices + x_selected = x_train[indices] + y_selected = y_train[indices] + x_train = x_selected + y_train = y_selected + #free up memory + del x_selected + del y_selected + del indices + #Debug + num_samples = x_train.shape[0] + print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +# Shuffle the training data +print_Color('shuffling data...', ['yellow']) +x_train, y_train = shuffle_data(x_train, y_train) +#save_images_to_dir +if Save_TS: + print_Color('Saving TS...', ['yellow']) + SITD = np.random.choice(num_samples, size=400, replace=False) + S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) + if RANGE_NOM: + if scale_data_NP_M: + save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) +print_Color('Done.', ['green']) + +# %% [markdown] +# ## Save EV Dataset + +# %% +if Make_EV_DATA: + np.save(f'Database\\x_val{SL_EX}.npy', x_val) + np.save(f'Database\\y_val{SL_EX}.npy', y_val) + np.save(f'Database\\x_test{SL_EX}.npy', x_test) + np.save(f'Database\\y_test{SL_EX}.npy', y_test) + +# %% [markdown] +# ## Load EV Dataset + +# %% +x_val = np.load(f'Database\\x_val{SL_EX}.npy') +y_val = np.load(f'Database\\y_val{SL_EX}.npy') +x_test = np.load(f'Database\\x_test{SL_EX}.npy') +y_test = np.load(f'Database\\y_test{SL_EX}.npy') + +# %% [markdown] +# ## Creating the model +# + +# %% [markdown] +# ### Rev1 +# ``` +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: β‰…95.1 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(EfficientNetB7) +# ``` + +# %% +from keras.applications import EfficientNetB7 + +EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.1 +# ``` +# statuses: S.Ready (can improve) +# Working: βœ… +# Max fine tuned acc: β‰…93.2 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(ConvNeXtLarge) +# ``` + +# %% +from keras.applications import ConvNeXtLarge + +ConvNeXtLarge_M = ConvNeXtLarge(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') +# define new model +model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) + +# compile model +opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy', 'binary_accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.2 +# ``` +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: 95.3 +# Max fine tuned acc TLRev2: 96.47 +# type: transfer learning>>>(EfficientNetB7::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +#FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 + print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model.output) + Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.3 +# ``` +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: ⚠️ +# type: transfer learning>>>(EfficientNetB7|Xception::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +from keras.applications.xception import Xception + +#FUNC +def Combo_Model(freeze_layers1, freeze_layers2): + # Define a common input + common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) + + # Base model 1 + base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) + # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) + base_model1_out = base_model1(common_input) + + # Base model 2 + base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) + # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) + base_model2_out = base_model2(common_input) + + print('Total base_model1 layers: ', len(base_model1.layers)) + print('Total base_model2 layers: ', len(base_model2.layers)) + + # Freeze the specified number of layers in both models + for layer in base_model1.layers[:freeze_layers1]: + layer.trainable = False + for layer in base_model2.layers[:freeze_layers2]: + layer.trainable = False + + # Unfreeze the rest in both models + for layer in base_model1.layers[freeze_layers1:]: + layer.trainable = True + for layer in base_model2.layers[freeze_layers2:]: + layer.trainable = True + + # Combine the output of the two base models + combined = concatenate([base_model1_out, base_model2_out]) + + # adding CDL + base_model_FT = GlobalAveragePooling2D()(combined) + Dense_L1 = Dense(2048, activation='relu', kernel_regularizer=l2(0.04))(base_model_FT) + Dropout_L1 = Dropout(0.4)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + combo_model = Model(inputs=common_input, outputs=predictions) + print('Total model layers: ', len(combo_model.layers)) + + #OPT/compile + opt = SGD(momentum=0.9) + combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return combo_model + +print('Creating the model...') +# Main +freeze_layers_1 = 0 +freeze_layers_2 = 0 +model = Combo_Model(freeze_layers_1, freeze_layers_2) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.4 +# ``` +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: β‰…95.64 +# type: transfer learning>>>(EfficientNetV2XL) +# ``` + +# %% +from keras_efficientnet_v2 import EfficientNetV2XL + +EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.5) +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +freeze_layers = 0 +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 + +#FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 + print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + + # adding LSTM layers + lstm_seq_length = 49 + lstm_input_shape = (lstm_seq_length, base_model.output_shape[1]) + reshape_layer = Reshape(target_shape=(lstm_seq_length, -1))(base_model.output) + lstm_layer = CuDNNLSTM(512, input_shape=lstm_input_shape)(reshape_layer) + + # adding dense layers + Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.04))(lstm_layer) + Dropout_L1 = Dropout(0.4)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + + +# %% [markdown] +# ### V(T) Beta2 + +# %% +from keras.applications import InceptionResNetV2 + +#FUNC +def Eff_B7_NS(freeze_layers): + base_model = InceptionResNetV2(input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 + print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model.output) + Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.04))(base_model_FT) + Dropout_L1 = Dropout(0.4)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### LR FINDER + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF/Other +LRF_OPT = SGD(momentum=0.9) +LFR_batch_size = 1 # or any other batch size that fits in your memory +LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) +# Instantiate LrFinder +lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) + +# Start range_test +lr_find.range_test(LRF_dataset) +lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) + +# %% [markdown] +# ## Loading the model + +# %% [markdown] +# ### Loading the full model + +# %% +import efficientnet.tfkeras +# Configuration +PRMC = False +freeze_from_opposite = True +Extra_EXT = '_T' +freeze_layers = 0 +randomly_frozen_layers = 0 +freeze_last_seven = True +# CEC_opt = Adagrad() +# CEC_opt = Yogi() +# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +CEC_opt = SGD(momentum=0.9, nesterov=False) +# CEC_opt = Adam() +# Main +try: + if SAVE_TYPE == 'TF': + model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) + else: + model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) +except (ImportError, IOError) as e: + print(f'\033[91mfailed to load the model ERROR:\n{e}') +else: + print('\033[92mLoading model done.') + if not PRMC: + print('Compiling the AI model...\033[0m') + + for layer in model.layers: + layer.trainable = True + + # Select random layers to freeze + frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) + + for i, layer in enumerate(model.layers): + if i in frozen_layer_indices: + layer.trainable = False + else: + if freeze_from_opposite and (i > len(model.layers) - freeze_layers): + layer.trainable = False + elif (not freeze_from_opposite) and i < freeze_layers: + layer.trainable = False + else: + layer.trainable = True + + for layer in model.layers[-7:]: + layer.trainable = not freeze_last_seven + + model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) + model.summary(show_trainable=True, expand_nested=True) + print('done.') + + +# %% [markdown] +# ### Loading model weights + +# %% +model.load_weights('PAI_model_weights.h5') +print('done.') + +# %% [markdown] +# ## Training + +# %% [markdown] +# #### Usage: +# ##### Start with Rev2 if it didnt work train it a little bit with Rev1 and then train it with Rev2 +# ##### flowchart: +# ![FC](TRAIN_FC.png) + +# %% [markdown] +# #### Rev2 (THE BEST) +# ``` +# Working: βœ… +# Other: +# - Tensorboard doesn't work. +# + Perverts overfitting. +# - Slow training. +# + Achieving higher acc. +# - Some models dont work. +# ``` + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +# CONF +max_epoch = 256 # 128 for small models 256 for full Fine tuning and big models +subset_epoch = 8 # change it if you are using a combined model or a big one| DEF=6 / COMM=8 | Too little can result the model not Learn the patterns and too much makes the model overfit on that subset and perform badly on the next subset +subset_epoch_FT = 6 +PL_epoch = 16 # <=16 for small models and >=24 for big models +subset_size = 2048 +Conf_batch_size_REV2 = 8 +OneCycleLr_MAXLR = 0.01 +OneCycleLr_DEC_A = 0.0005 +OneCycleLr_MINLR = 0.0015 +TerminateOnHighTemp_M = True # can make your training a little bit slower, but it can save your expensive gpu (TURN IT OFF FOR TPU OR CPU TRAINING) +Use_ES_ONSUBT = False +EarlyStopping_P = 5 +BEST_RSN = 'PAI_model_T' +#VAR +OneCycleLr_CUNLR = OneCycleLr_MAXLR +all_histories = [] +best_acc = 0 +best_loss = float('inf') +#Funcs +def add_image_grain_TRLRev2(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(0.001, 0.016) + intensityL1 = random.uniform(0.005, 0.020) + else: + intensityL2 = random.uniform(0.001, 0.027) + intensityL1 = random.uniform(0.001, 0.028) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.022) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +#CONST +train_SUB_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.88, 1.12), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=2, + fill_mode='nearest', + preprocessing_function=noise_func_TRLRev2 + ) +class TerminateOnHighTemp(tf.keras.callbacks.Callback): + def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): + super().__init__() + self.active = active + self.check_every_n_batches = check_every_n_batches + self.high_temp = high_temp + self.low_temp = low_temp + self.pause_time = pause_time + self.batch_counter = 0 + + def on_batch_end(self, batch, logs=None): + if not self.active: + return + self.batch_counter += 1 + if self.batch_counter % self.check_every_n_batches == 0: + temperature = gpu_control.get_temperature() + if temperature > self.high_temp: + print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) + time.sleep(self.pause_time) + while gpu_control.get_temperature() > self.low_temp: + time.sleep(4) + print_Color('Resuming training...', ['yellow']) +#callbacks +steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 +early_stopping = EarlyStopping(monitor='val_accuracy', patience=EarlyStopping_P, verbose=1, restore_best_weights=True, mode='max') +TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, + check_every_n_batches=5, + high_temp=75, + low_temp=58, + pause_time=60) +#MAIN +print('Training the model...') +try: + for epoch in range(1, max_epoch): + # Start Epoch + STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' + C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT + start_FULL_time = time.time() + print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch}~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) + # DP + print_Color('Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + print_Color(f'~*Taking a subset of ~*[{subset_size}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) + x_SUB_train = x_train[subset_indices] + y_SUB_train = y_train[subset_indices] + print_Color('Augmenting data...', ['yellow']) + train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, y_SUB_train, shuffle=False, batch_size=len(x_SUB_train)).next() + x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) / 255 + y_SUB_train = train_SUB_augmented_images[1] + # learning_rate_schedule_SUB + if epoch > PL_epoch and OneCycleLr_CUNLR > OneCycleLr_MINLR: + OneCycleLr_CUNLR -= OneCycleLr_DEC_A + + learning_rate_schedule_SUB = OneCycleLr(max_lr=OneCycleLr_CUNLR, steps_per_epoch=steps_per_epoch_train_SUB, epochs=C_subset_epoch) + #FV + print_Color(f'~*Setting model OneCycleLr::maxlr to ~*[{OneCycleLr_CUNLR:.6f}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + print_Color(f'~*Setting model subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + # Train + print_Color('Training on subset...', ['green']) + start_SUBO_time = time.time() + SUB_history = model.fit(x_SUB_train, + y_SUB_train, + epochs=C_subset_epoch, + batch_size=Conf_batch_size_REV2, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[learning_rate_schedule_SUB, + TerminateOnHighTemp_CB, + early_stopping] if Use_ES_ONSUBT else [learning_rate_schedule_SUB, + TerminateOnHighTemp_CB] + ) + end_SUBO_time = time.time() + print_Color('Subset training done.', ['green']) + all_histories.append(SUB_history.history) + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Evaluate the model on the test data + evaluation = model.evaluate(x_test, y_test, verbose=0) + + # Extract the loss and accuracy from the evaluation results + loss = evaluation[0] + acc = evaluation[1] + + # If the accuracy is higher than the best_acc + if acc > best_acc: + print("Improved model accuracy from {} to {}. Saving model.".format(best_acc, acc)) + + # Update the best_acc + best_acc = acc + + # Save the model + if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(BEST_RSN, save_format='tf') + else: + model.save(f'{BEST_RSN}.h5') + else: + print("Model accuracy did not improve from {}. Not saving model.".format(best_acc)) + + # If the loss is higher than the best_loss + if loss < best_loss: + print("Improved model loss from {} to {}. Saving model.".format(best_loss, loss)) + + # Update the best_acc + best_loss = loss + + # Save the model + if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(BEST_RSN + '_BL', save_format='tf') + else: + model.save(f'{BEST_RSN}_BL.h5') + else: + print("Model loss did not improve from {}. Not saving model.".format(best_loss)) + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Epoch end + end_time = time.time() + epoch_time = end_time - start_FULL_time + print(f"Time taken for epoch(FULL) {epoch}: {epoch_time:.2f} sec") + epoch_SUB_time = end_SUBO_time - start_SUBO_time + print(f"Time taken for epoch(SUBo) {epoch}: {epoch_SUB_time:.2f} sec") + print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) +except KeyboardInterrupt: + print('\nKeyboardInterrupt.') +# End +history = {} +for key in all_histories[0].keys(): + # For each metric, concatenate the values from all histories + history[key] = np.concatenate([h[key] for h in all_histories]) +print('Training done.\n') + +# %% [markdown] +# #### Rev1 +# ``` +# Working: βœ… +# Other: +# + Tensorboard works. +# - Can cause overfitting. +# ``` + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF +WTD_augmentation = True +Conf_batch_size = 4 +Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) +#TensorBoard conf +TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) +# Learning rate configuration +Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr +OneCycleLr_MAXLR = 0.0174 +# First time +if Learning_rate_conf == 1: + learning_rate_start = 8e-04 + learning_rate_max = 5e-03 + learning_rate_min = 5e-05 + learning_rate_rampup_epochs = 5 + learning_rate_sustain_epochs = 1 + learning_rate_exp_decay = .3 + #TEMP + # learning_rate_start = 8e-04 + # learning_rate_max = 1e-02 + # learning_rate_min = 8e-04 + # learning_rate_rampup_epochs = 5 + # learning_rate_sustain_epochs = 3 + # learning_rate_exp_decay = .45 +# 2th time +if Learning_rate_conf == 2: + if Learning_rate_conf_SET2C == 1: + learning_rate_start = 4.10e-06 + learning_rate_max = 4.10e-06 + learning_rate_min = 4.10e-06 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 2: + learning_rate_start = 4e-07 + learning_rate_max = 4e-07 + learning_rate_min = 4e-07 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 3: + learning_rate_start = 5e-04 + learning_rate_max = 5e-04 + learning_rate_min = 5e-04 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 +# Function to build learning rate schedule +if Learning_rate_conf in [1,2]: + def build_learning_rate_fn(lr_start=learning_rate_start, + lr_max=learning_rate_max, + lr_min=learning_rate_min, + lr_rampup_epochs=learning_rate_rampup_epochs, + lr_sustain_epochs=learning_rate_sustain_epochs, + lr_exp_decay=learning_rate_exp_decay): + lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync + def learning_rate_fn(epoch): + if epoch < lr_rampup_epochs: + lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start + elif epoch < lr_rampup_epochs + lr_sustain_epochs: + lr = lr_max + else: + lr = (lr_max - lr_min) *\ + lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min + return lr + return learning_rate_fn +#WTD_augmentation +if WTD_augmentation: + print_Color('Using WTD_augmentation...', ['yellow']) + def TF_add_image_grain(image, intensity = 0.01): + # Generate random noise array in the range [0, 1] + noise = tf.random.uniform(shape=tf.shape(image), minval=0, maxval=1, dtype=tf.float32) + + # Scale the noise array + scaled_noise = noise * intensity + + # Add the noise to the image + noisy_image = tf.math.add(image, scaled_noise) + + # Clip + if RANGE_NOM: + noisy_image = tf.clip_by_value(noisy_image, -1.0, 1.0) + else: + noisy_image = tf.clip_by_value(noisy_image, 0.0, 255.0) + + return noisy_image + # Function to augment images + def augment_images(image, label): + image = tf.image.random_flip_left_right(image) + image = tf.image.random_flip_up_down(image) + image = tf.image.random_contrast(image, 0.2, 1.8) + image = tf.image.random_brightness(image, max_delta=0.3) + # Random intensity between 0 and 0.04 + intensity = random.uniform(0, 0.04) + image = TF_add_image_grain(image, intensity=intensity) + # Add random rotation + # image = tf.image.rot90(image, k=random.randint(0, 3)) + return image, label + + # Create TensorFlow dataset + AUTO = tf.data.experimental.AUTOTUNE + train_dataset = ( + tf.data.Dataset.from_tensor_slices((x_train, y_train)) + .map(augment_images, num_parallel_calls=AUTO) + .repeat() + .shuffle(len(x_train)) + .batch(Conf_batch_size) + .prefetch(AUTO) + ) + +# Calculate steps per epoch +steps_per_epoch_train = len(x_train) // Conf_batch_size + +# Set up callbacks +class EpochEndMON(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs=None): + optimizer = self.model.optimizer + if hasattr(optimizer, 'lr'): + lr = tf.keras.backend.get_value(optimizer.lr) + print(f'\nLearning rate for epoch {epoch+1} is {lr}') + if hasattr(optimizer, 'momentum'): + momentum = tf.keras.backend.get_value(optimizer.momentum) + print(f'Momentum for epoch {epoch+1} is {momentum}') + if logs: + val_loss = logs.get('val_loss') + val_acc = logs.get('val_accuracy') + print(f'Validation loss for epoch {epoch+1} is {val_loss}') + print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') + + print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') + +# Instantiate the callback +EpochEndMON_callback = EpochEndMON() +if Learning_rate_conf in [1,2]: + learning_rate_fn = build_learning_rate_fn() + learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) +else: + learning_rate_schedule = OneCycleLr(max_lr=OneCycleLr_MAXLR, steps_per_epoch=steps_per_epoch_train, epochs=20) +if SAVE_TYPE == 'TF': + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +else: + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' +tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq) + +# Train the model +print('Log dir:', log_dir) +#MInfo +print("Input Shape:", model.input_shape) +print("Output Shape:", model.output_shape) +print("Loss Function:", model.loss) +print('Training the model...\n') +if WTD_augmentation: + history = model.fit(train_dataset, + epochs=256, + steps_per_epoch=steps_per_epoch_train, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) +else: + history = model.fit(x_train, + y_train, + epochs=256, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) +print('Training done.\n') + +# %% [markdown] +# ## Saving model weights +# + +# %% +Extra_EXT = '_T' +# Save the weights +print('Saving weights...') +model.save_weights('PAI_model_weights.h5') +print('Saving full model...') +if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') +else: + try: + model.save(f'PAI_model{Extra_EXT}.h5') + except ValueError: + print('failed to save in .h5 format!') + print('Saving full model in tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') + +# %% [markdown] +# ## Garbage Collection (memory) + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() + +# %% [markdown] +# ## Analyse model Training performance + +# %% +def convert_history(history): + if isinstance(history, tf.keras.callbacks.History): + return history.history + else: + return history +try: + EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' + history = convert_history(history) + #loss + plt.plot(history['loss'], label='loss') + try: + plt.plot(history['val_loss'], label='val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_loss.') + plt.title('Model Loss') + plt.ylabel('Loss') + plt.xlabel(EPM) + plt.grid(True) + plt.ylim(top=(max(history['val_loss'][8:]) + min(history['val_loss'])) / 2, bottom=0) + plt.show() + #acc + plt.plot(history['accuracy'], label='accuracy') + try: + plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy.') + plt.title('Model Accuracy') + plt.ylabel('Accuracy') + plt.xlabel(EPM) + plt.grid(True) + plt.show() +except (ValueError, NameError): + print('\033[91mfailed to load model history.') + +# %% [markdown] +# ## Analyse model Predicting performance + +# %% [markdown] +# ### Gradcam heatmap + +# %% [markdown] +# #### V2 + +# %% +def compute_heatmap(model, img_array, conv_layer_name, pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + + # Apply threshold and adjust sensitivity + heatmap = np.where(heatmap > threshold, heatmap, 0) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + + # Apply threshold and adjust sensitivity + heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + + return heatmap + +# %% [markdown] +# #### V3 + +# %% [markdown] +# ### Main test + +# %% +import seaborn as sns +from sklearn.metrics import confusion_matrix, accuracy_score +from scipy.stats import binom +from tqdm import tqdm +import efficientnet.tfkeras +import cv2 +import gc +# Garbage Collection (memory) +gc.collect() + +Extra_EXT = '_T' +prob_L = 0.9995 +tick_spacing = 5 +Train_data_test = False +if SAVE_TYPE == 'TF': + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}') +else: + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}.h5') + +# Ensure the model's input_shape matches your data +assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' + +# Make predictions on validation data +val_predictions = model.predict(x_val) +val_predictions = np.argmax(val_predictions, axis=1) + +# Make predictions on Train data +if Train_data_test: + Train_predictions = model.predict(x_train) + Train_predictions = np.argmax(Train_predictions, axis=1) + +# Make predictions on test data +test_predictions = model.predict(x_test) +test_predictions = np.argmax(test_predictions, axis=1) + +# Convert y_val and y_test from one-hot encoder to their original form +y_val_original = np.argmax(y_val, axis=1) +y_test_original = np.argmax(y_test, axis=1) +if Train_data_test: + y_train_original = np.argmax(y_train, axis=1) + +# Calculate accuracy on validation data +val_accuracy = accuracy_score(y_val_original, val_predictions) + +# Calculate accuracy on Train data +if Train_data_test: + Train_accuracy = accuracy_score(y_val_original, Train_predictions) + +# Calculate accuracy on test data +test_accuracy = accuracy_score(y_test_original, test_predictions) + +# Print acc +if Train_data_test: + print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}') +print(f'The accuracy of the model on validation data is {val_accuracy:.2%}') +print(f'The accuracy of the model on test data is {test_accuracy:.2%}') + +# Visualize the predictions on validation data as a grid of squares +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + plt.imshow(x_val[i]) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() +#Heatmap +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + img = x_val[i] + heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) + heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) + heatmap = np.uint8(255 * heatmap) + # Apply Adaptive Histogram Equalization + clahe = cv2.createCLAHE(clipLimit=4, tileGridSize=(4,4)) # Create CLAHE object + heatmap = clahe.apply(heatmap) + heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + if RANGE_NOM: + superimposed_img = (heatmap / 255) * 0.5 + img + else: + superimposed_img = (heatmap / 255) * 0.5 + (img / 255) + #clip + superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] + plt.imshow(superimposed_img) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() + +# Define the list of labels +labels = ['NORMAL', 'PNEUMONIA'] + +# Create a confusion matrix for validation data +val_cm = confusion_matrix(y_val_original, val_predictions) + +# Create a confusion matrix for test data +test_cm = confusion_matrix(y_test_original, test_predictions) + +# Plot the confusion matrix as a heatmap for validation data +plt.figure(figsize=(8, 6)) +sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Validation Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Plot the confusion matrix as a heatmap for test data +plt.figure(figsize=(8, 6)) +sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Test Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Define the range of test data sizes to use +data_sizes = range(1, len(x_test), 4) +# Calculate the probability of a wrong prediction based on test accuracy +prob_wrong = 1 - test_accuracy + +# Create a list to store the number of incorrect predictions for each test data size +incorrect_predictions = [] + +# Generate predictions and track incorrect predictions for each data size +for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): + # Garbage Collection (memory) + gc.collect() + # Randomly select a subset of test data + indices = np.random.choice(len(x_test), size, replace=False) + x_test_subset = x_test[indices] + y_test_subset = y_test[indices] + + # Make predictions on the subset of test data + test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) + test_predictions = np.argmax(test_predictions, axis=1) + y_test_original_subset = np.argmax(y_test_subset, axis=1) + + # Calculate the number of incorrect predictions + incorrect_preds = np.sum(test_predictions != y_test_original_subset) + incorrect_predictions.append(incorrect_preds) + +# Plot the number of incorrect predictions vs. the number of data points +plt.figure(figsize=(10, 6)) +plt.plot(data_sizes, incorrect_predictions) +plt.xlabel('Number of Data Points') +plt.ylabel('Number of Incorrect Predictions') +# Add gridlines for the x and y axes +plt.grid(True) + +# Change the tick spacing for the x and y axes +plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) +plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) + +plt.title('Number of Incorrect Predictions vs. Number of Data Points') +plt.show() + +# Define the range of test data sizes to use +data_sizes = range(1, len(x_test), 1) + +# Calculate the probability of a wrong prediction based on test accuracy +prob_wrong = 1 - test_accuracy + +# Create a list to store the probability of getting at least one wrong answer for each test data size +probabilities = [] + +# Calculate the probability of getting at least one wrong answer for each data size +for size in data_sizes: + # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 + cdf = binom.cdf(0, size, prob_wrong) + # Subtract the CDF from 1 to get the probability of getting at least one wrong answer + prob = 1 - cdf + probabilities.append(prob) + +# Find the index of the first data point that has a probability greater than prob_L% +index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) + +# Limit the x-axis to the first data point that has a probability greater than prob_L% +data_sizes = data_sizes[:index+1] +probabilities = probabilities[:index+1] + +# Plot the probability vs. the number of data points +plt.figure(figsize=(10, 6)) +plt.plot(data_sizes, probabilities) +plt.xlabel('Number of Data Points') +plt.ylabel('Probability') + +# Add gridlines for the x and y axes +plt.grid(True) + +# Change the tick spacing for the x and y axes +plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 2)) +plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100)) + +plt.ylim(top=1.01) + +plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') +plt.show() + + diff --git a/Exports/V5/Python_EPO.py b/Exports/V5/Python_EPO.py index d6b0624..f399e0a 100644 --- a/Exports/V5/Python_EPO.py +++ b/Exports/V5/Python_EPO.py @@ -1,2211 +1,2211 @@ -# Copyright (c) 2023 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT - -# %% [markdown] -# # keras/TF model -# 
-#  Copyright (c) 2023 Aydin Hamedi
-#  
-#  This software is released under the MIT License.
-#  https://opensource.org/licenses/MIT
-#
- -# %% [markdown] -# ## Pre Conf - -# %% -CPU_only = False # True to Force TF to use the cpu - -# %% [markdown] -# ## Pylibs - -# %% -import os -import sys -import time -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -if CPU_only: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' -import cv2 -import glob -import keras -import pprint -import random -import shutil -import gzip -import glob -import pickle -import datetime -import subprocess -import gpu_control -import numpy as np -import pandas as pd -from tqdm import tqdm -import seaborn as sns -from hyperas import optim -# import tensorflow_addons as tfa -from keras_adabound import AdaBound -from importlib import reload -from keras.losses import categorical_crossentropy -import tensorflow as tf -from keras.models import Model -from scipy.ndimage import zoom -import matplotlib.pyplot as plt -from model_profiler import model_profiler -from keras_gradient_noise import add_gradient_noise -from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax -# from tensorflow_addons.optimizers import Yogi -from adabelief_tf import AdaBeliefOptimizer -from sklearn.preprocessing import LabelEncoder -from imblearn.over_sampling import SMOTE -from keras.regularizers import l2 -from keras.models import load_model -from matplotlib import pyplot as plt -from PIL import Image, ImageDraw, ImageFont -from keras import Sequential -from random import randint, choice, shuffle -from keras.callbacks import EarlyStopping -from keras.callbacks import TensorBoard -from keras.utils import to_categorical -from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler -from sklearn.model_selection import train_test_split -from keras.preprocessing.image import ImageDataGenerator -from keras.layers import Conv2D,\ - MaxPooling2D,\ - Flatten,\ - Dense,\ - Dropout,\ - BatchNormalization,\ - SeparableConv2D,\ - Input, Concatenate,\ - GlobalAveragePooling2D,\ - CuDNNLSTM, concatenate,\ - Reshape, Multiply -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from Utils.Other import * -# Other -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - - -# %% [markdown] -# ## Conf -# - -# %% [markdown] -# ### Data processing conf - -# %% -# Directory paths# Directory paths for training, test and validation image data -train_dir = 'Database\\Train\\Data\\train' -test_dir = 'Database\\Train\\Data\\test' -validation_dir = 'Database\\Train\\Data\\val' -img_res = [224, 224, 3] -# img_res = [324, 324, 3] -# img_res = [224, 224, 3] -# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) -interpolation_order_IFG = 2 -categorical_IMP = True -Make_EV_DATA = False -R_fill_mode = True -add_img_grain = True -Save_TS = True -Use_SMOTE = False # (⚠️Beta⚠️) -ADBD = 1 -OP_HDC = False -SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 -LNTS = 0 -Debug_OUT = False -adjust_brightness_Mode = True -RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️) -scale_data_NP_M = False # (⚠️deprecated⚠️) - -# %% [markdown] -# ### Training - -# %% -SAVE_TYPE = 'H5' -Use_mixed_float16 = False -#Other -if Use_mixed_float16: - tf.keras.mixed_precision.set_global_policy('mixed_float16') -else: - tf.keras.mixed_precision.set_global_policy('float32') - -print(tf.keras.mixed_precision.global_policy()) - -# %% [markdown] -# ## data processing -# - -# %% -#Z_SCORE_normalize -def Z_SCORE_normalize(arr): - arr = arr.astype('float32') - mean = np.mean(arr) - std_dev = np.std(arr) - arr = (arr - mean) / std_dev - return arr -#normalize_TO_RANGE -def normalize_TO_RANGE(arr, min_val, max_val): - arr = arr.astype('float32') - arr = (arr - arr.min()) / (arr.max() - arr.min()) - arr = arr * (max_val - min_val) + min_val - return arr -#scale_data -def scale_data_NP(data): - if scale_data_NP_M: - data = data.astype('float32') - data = (data - 127.5) / 127.5 - return data - else: - return data / 255 -#add_image_grain -def add_image_grain(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -#apply_clahe_rgb_array -def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): - # Create a CLAHE object - clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) - - # Iterate over each image in the array - for i in range(len(images)): - # Split the image into color channels - b, g, r = cv2.split(images[i]) - - # Convert the channels to the appropriate format - b = cv2.convertScaleAbs(b) - g = cv2.convertScaleAbs(g) - r = cv2.convertScaleAbs(r) - - # Apply adaptive histogram equalization to each channel - equalized_b = clahe.apply(b) - equalized_g = clahe.apply(g) - equalized_r = clahe.apply(r) - - # Merge the equalized channels back into an image - equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) - - # Replace the original image with the equalized image in the array - images[i] = equalized_image - - return images -#noise_func -def noise_func(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.05, 0.05) - intensityL1 = random.uniform(-0.04, 0.04) - else: - intensityL2 = random.uniform(-0.06, 0.06) - intensityL1 = random.uniform(-0.04, 0.04) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026 - new_image = add_image_grain(new_image, intensity=intensity) - return new_image -#shuffle_data -def shuffle_data(x, y): - indices = np.arange(x.shape[0]) - np.random.shuffle(indices) - x = x[indices] - y = y[indices] - return x, y -#save_images_to_dir -def save_images_to_dir(images, labels, dir_path): - # create the directory if it doesn't exist - if not os.path.exists(dir_path): - os.makedirs(dir_path) - # iterate over the images and labels - for i, (image, label) in enumerate(zip(images, labels)): - # get the class label - class_label = np.argmax(label) - # create the file path - file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') - # save the image to the file path - plt.imsave(file_path, image.squeeze()) - # compress the directory - shutil.make_archive(dir_path, 'gztar', dir_path) - # remove the original directory - shutil.rmtree(dir_path) -#Debug_img_Save -def Debug_img_Save(img, id = 'DEF'): - SITD = np.random.choice(img.shape[0], size=400, replace=False) - S_dir = f'Samples\\Debug\\{id}\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) - save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir) -# Create an ImageDataGenerator for the training set -if OP_HDC: - print_Color('Using OP_HDC IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.86, 1.1), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=interpolation_order_IFG, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -else: - print_Color('Using Def IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.26, - shear_range=0.25, - width_shift_range=0.25, - brightness_range=(0.78, 1.1), - height_shift_range=0.25, - channel_shift_range=100, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -train_datagen_SM = ImageDataGenerator( - horizontal_flip=False, - vertical_flip=False, - rotation_range=20, - zoom_range=0.07, - shear_range=0.07, - width_shift_range=0.07, - brightness_range=(0.99, 1.01), - height_shift_range=0.07, - channel_shift_range=0, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False -) -# Create an iterator for the training set -train_generator_SM = train_datagen_SM.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') -# Create an ImageDataGenerator for the validation set (OP) -if Make_EV_DATA: - val_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the validation set - val_generator = val_datagen.flow_from_directory( - validation_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), - class_mode='binary', - color_mode='rgb') - - # Create an ImageDataGenerator for the test set - test_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the test set - test_generator = test_datagen.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), - class_mode='binary', - color_mode='rgb') -# Load all images and labels into memory -print_Color('Loading all images and labels into memory...', ['yellow']) -x_train, y_train = next(iter(train_generator_SM)) -if Make_EV_DATA: - x_val, y_val = next(iter(val_generator)) - x_test, y_test = next(iter(test_generator)) -if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG -# fit parameters from data -# train_datagen.fit(x_train) -#to_categorical (TEMP) -if categorical_IMP: - print_Color('Making categorical data...', ['yellow']) - y_train = to_categorical(y_train, num_classes=2) - if Make_EV_DATA: - y_val = to_categorical(y_val, num_classes=2) - y_test = to_categorical(y_test, num_classes=2) -# Use_SMOTE -if Use_SMOTE: - print_Color('SMOTE...', ['yellow']) - # Convert y_train from one-hot encoding to label encoding - y_train_label_encoded = np.argmax(y_train, axis=1) - - # Print the original label distribution - unique, counts = np.unique(y_train_label_encoded, return_counts=True) - print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) - - # Use SMOTE to oversample the minority class - smote = SMOTE(random_state=42) - x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) - - # Print the resampled label distribution - unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) - print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) - - # Reshape x_train_res back to the original x_train shape - x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) - - # Convert y_train_res from label encoding back to one-hot encoding - y_train_res = to_categorical(y_train_res_label_encoded) - - # Calculate the ratio of two labels after resampling - pneumonia_count = np.sum(y_train_res[:, 1]) - total_count = y_train_res.shape[0] - label_ratio_res = pneumonia_count / total_count - label_ratio_percentage_res = label_ratio_res * 100 - - # Replace the original data with the resampled data - x_train = x_train_res - y_train = y_train_res - - # Delete the resampled data to free up memory - del x_train_res, y_train_res_label_encoded, y_train_res -# Generating augmented data -print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', - ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], - advanced_mode=True) -if ADBD > 0: - for i in range(ADBD): - # ADB_clip_limit Scheduler>>> - if i == 0: - ADB_clip_limit = 0.8 - else: - #V1>>> - CL_SLM = 2.4 - ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) - # Try it in win graphing calculator copy and paste: - # β”Œ-------------┬--┬---------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ - # β””-------------β”΄--β”΄---------------β”˜ - #V2>>> - # CL_SLM_2 = 1.4 - # CL_SLM_Start_2 = 2 - # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) - # Try it in win graphing calculator copy and paste: - # β”Œ-----------------┬--┬-------------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ - # β””-----------------β”΄--β”΄-------------------β”˜ - print(f'> Generating ADB[{i+1}/{ADBD}]...') - # prepare an iterators to scale images - train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) - - # get augmented data - x_train_augmented, y_train_augmented = train_iterator.next() - print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') - print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') - x_train_augmented = np.clip(x_train_augmented, 0, 255) - if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG - #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) - x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss - print(f'> └───Adding the Generated ADB...') - if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG - # append augmented data to original data - x_train = np.concatenate([x_train, x_train_augmented]) - y_train = np.concatenate([y_train, y_train_augmented]) - #free up memory - del y_train_augmented - del x_train_augmented -# normalizing -print_Color('Normalizing image data...', ['yellow']) -if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG -x_train = np.clip(x_train, 0, 255) -if RANGE_NOM: - x_train = scale_data_NP(x_train) -y_train = np.array(y_train) -if Make_EV_DATA: - x_test = np.clip(x_test, 0, 255) - x_val = np.clip(x_val, 0, 255) - if RANGE_NOM: - x_val = scale_data_NP(x_val) - y_val = np.array(y_val) - if RANGE_NOM: - x_test = scale_data_NP(x_test) - y_test = np.array(y_test) -if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG -# Check the data type of image data -print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) -# Check the range of image data -print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) -# Calculate the ratio of two labels -if categorical_IMP: - label_sums = np.sum(y_train, axis=0) - label_ratio = label_sums / (np.sum(y_train) + 1e-10) - label_ratio_percentage = label_ratio * 100 - print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', - ['normal', 'red', 'magenta', 'green'], advanced_mode=True) -print_Color('Setting LNTS...', ['yellow']) -# Get the total number of samples in the arrays -num_samples = x_train.shape[0] -print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -if LNTS != 0: - print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) - print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) - # Generate random indices to select LNTS samples - indices = np.random.choice(num_samples, size=LNTS, replace=False) - # Select the samples using the generated indices - x_selected = x_train[indices] - y_selected = y_train[indices] - x_train = x_selected - y_train = y_selected - #free up memory - del x_selected - del y_selected - del indices - #Debug - num_samples = x_train.shape[0] - print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -# Shuffle the training data -print_Color('shuffling data...', ['yellow']) -x_train, y_train = shuffle_data(x_train, y_train) -#save_images_to_dir -if Save_TS: - print_Color('Saving TS...', ['yellow']) - SITD = np.random.choice(num_samples, size=400, replace=False) - S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) - if RANGE_NOM: - if scale_data_NP_M: - save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) -print_Color('Done.', ['green']) - -# %% [markdown] -# ## Save EV Dataset - -# %% -np.save(f'Database\\Test\\Data\\x_val{SL_EX}.npy', x_val) -np.save(f'Database\\Test\\Data\\y_val{SL_EX}.npy', y_val) -np.save(f'Database\\Test\\Data\\x_test{SL_EX}.npy', x_test) -np.save(f'Database\\Test\\Data\\y_test{SL_EX}.npy', y_test) - -# %% [markdown] -# ## Load EV Dataset - -# %% -x_val = np.load(f'Database\\Test\\Data\\x_val{SL_EX}.npy') -y_val = np.load(f'Database\\Test\\Data\\y_val{SL_EX}.npy') -x_test = np.load(f'Database\\Test\\Data\\x_test{SL_EX}.npy') -y_test = np.load(f'Database\\Test\\Data\\y_test{SL_EX}.npy') - -# %% [markdown] -# ## Data Analyzation - -# %% -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -import seaborn as sns -from scipy.stats import zscore - -# Select a subset of your data -subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels -subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values -indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False) -indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False) -subset_pixels = x_train[indices_pixels] -subset_mean = x_train[indices_mean] - -# Reshape the data for calculating Z-scores -reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1]) -reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1]) - -# Calculate the mean intensity -mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1) -mean_intensity_mean = reshaped_data_mean.mean(axis=-1) - -# Stack the mean intensity with the reshaped data -data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)]) -data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)]) - -# Calculate Z-scores -z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0)) -z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0)) - -# Identify outliers -outliers_pixels = np.where(z_scores_pixels > 3) -outliers_mean = np.where(z_scores_mean > 3) - -# Create a 3D scatter plot for RGB channels -fig = plt.figure(figsize=(10, 20)) - -# Plot for individual pixels -ax = fig.add_subplot(211, projection='3d') -ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1) -ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red') -ax.set_title('Z-Score Scatter Plot for Individual Pixels') -ax.set_xlabel('Red') -ax.set_ylabel('Green') -ax.set_zlabel('Blue') - -# Plot for mean RGB values -ax = fig.add_subplot(212, projection='3d') -ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1) -ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red') -ax.set_title('Z-Score Scatter Plot for Mean RGB Values') -ax.set_xlabel('Red') -ax.set_ylabel('Green') -ax.set_zlabel('Blue') - -# Density plot of the mean intensity -plt.figure(figsize=(10, 5)) -sns.kdeplot(data=z_scores_pixels[:, -1], fill=True) -plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels') -plt.xlabel('Z-Score') - -sns.kdeplot(data=z_scores_mean[:, -1], fill=True) -plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values') -plt.xlabel('Z-Score') - -# Display the plot -plt.show() - -# %% [markdown] -# ## Creating the model -# - -# %% [markdown] -# ### Rev1 -# ``` -# recommended: ⚠️ -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: β‰…95.1 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(EfficientNetB7) -# ``` - -# %% -from keras.applications import EfficientNetB7 - -EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.1 -# ``` -# recommended: ❌ -# statuses: S.Ready (can improve) -# Working: ❌ -# Max fine tuned acc: β‰…93.2 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(ConvNeXtLarge) -# ``` - -# %% -from keras.applications import ConvNeXtLarge - -ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False) -# define new model -model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.2 -# ``` -# recommended: βœ… -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: 95.3 -# Max fine tuned acc TLRev2: 96.96 -# type: transfer learning>>>(EfficientNetB7::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -# FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENB7(input_shape=( - img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / - len(base_model.layers)) * 100 - print( - f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model.output) - Dense_L1 = Dense(512, activation='relu', - kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', - kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model( - inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - # OPT/compile - opt = SGD(momentum=0.9, nesterov=False) - # opt = Nadam() - # opt = Adamax() - # opt = RMSprop(momentum=0.9) - # opt = Adagrad() - # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.3 -# ``` -# recommended: ❌ -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: ⚠️ -# type: transfer learning>>>(EfficientNetB7|Xception::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -from keras.applications.xception import Xception - -#FUNC -def Combo_Model(freeze_layers1, freeze_layers2): - # Define a common input - common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) - - # Base model 1 - base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) - base_model1_out = base_model1(common_input) - - # Base model 2 - base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False) - # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) - base_model2_out = base_model2(common_input) - - print('Total base_model1 layers: ', len(base_model1.layers)) - print('Total base_model2 layers: ', len(base_model2.layers)) - - # Freeze the specified number of layers in both models - for layer in base_model1.layers[:freeze_layers1]: - layer.trainable = False - for layer in base_model2.layers[:freeze_layers2]: - layer.trainable = False - - # Unfreeze the rest in both models - for layer in base_model1.layers[freeze_layers1:]: - layer.trainable = True - for layer in base_model2.layers[freeze_layers2:]: - layer.trainable = True - - # Combine the output of the two base models - combined = concatenate([GlobalAveragePooling2D()(base_model1_out), GlobalAveragePooling2D()(base_model2_out)]) - - # adding CDL - Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined) - Dropout_L1 = Dropout(0.4)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - combo_model = Model(inputs=common_input, outputs=predictions) - print('Total model layers: ', len(combo_model.layers)) - - #OPT/compile - opt = SGD(momentum=0.9) - combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return combo_model - -print('Creating the model...') -# Main -freeze_layers_1 = 0 -freeze_layers_2 = 0 -model = Combo_Model(freeze_layers_1, freeze_layers_2) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.4 -# ``` -# recommended: ⚠️ -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: β‰…95.64 -# type: transfer learning>>>(EfficientNetV2XL) -# ``` - -# %% -from keras_efficientnet_v2 import EfficientNetV2XL - -EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4) -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -# opt = SGD(momentum=0.9) -opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False) -# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -freeze_layers = 0 -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta - -# %% -from efficientnet.keras import EfficientNetL2 as KENBL2 -#FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]), - weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5', - include_top=False, - drop_connect_rate=0) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 - print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model.output) - Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta2 - -# %% -from keras_efficientnet_v2 import EfficientNetV2S - -EfficientNet_M = EfficientNetV2S(input_shape=(img_res[0], img_res[1], img_res[2]), num_classes=2, dropout=0.5) -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -freeze_layers = 0 -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta3 - -# %% -from keras.applications import ConvNeXtXLarge -from keras.layers import Lambda -#FUNC -def Eff_B7_NS(): - # Add a Lambda layer at the beginning to scale the input - input = Input(shape=(img_res[0], img_res[1], img_res[2])) - x = Lambda(lambda image: image * 255)(input) - - base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x) - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model) - Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -model = Eff_B7_NS() -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### LR FINDER - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF/Other -LRF_OPT = SGD(momentum=0.9) -LFR_batch_size = 1 # or any other batch size that fits in your memory -LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) -# Instantiate LrFinder -lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) - -# Start range_test -lr_find.range_test(LRF_dataset) -lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) - -# %% [markdown] -# ### Model vis - -# %% -dot_img_file = 'model_1.png' -keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True) - -# %% [markdown] -# ## Loading the model - -# %% [markdown] -# ### Loading the full model - -# %% -import efficientnet.tfkeras -# Configuration -PRMC = False -freeze_from_opposite = False -Extra_EXT = '_T' -freeze_layers = 0 -randomly_frozen_layers = 0 -freeze_last_seven = True -# CEC_opt = Adagrad() -# CEC_opt = Yogi() -# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -CEC_opt = SGD(momentum=0.9, nesterov=False) -# CEC_opt = Adam() -# Main -try: - if SAVE_TYPE == 'TF': - model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) - else: - model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) -except (ImportError, IOError) as e: - print(f'\033[91mfailed to load the model ERROR:\n{e}') -else: - print('\033[92mLoading model done.') - if not PRMC: - print('Compiling the AI model...\033[0m') - - for layer in model.layers: - layer.trainable = True - - # Select random layers to freeze - frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) - - for i, layer in enumerate(model.layers): - if i in frozen_layer_indices: - layer.trainable = False - else: - if freeze_from_opposite and (i > len(model.layers) - freeze_layers): - layer.trainable = False - elif (not freeze_from_opposite) and i < freeze_layers: - layer.trainable = False - else: - layer.trainable = True - - for layer in model.layers[-7:]: - layer.trainable = not freeze_last_seven - - model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) - model.summary(show_trainable=True, expand_nested=True) - print('done.') - -# %% [markdown] -# ### Loading model weights - -# %% -model.load_weights('PAI_model_weights.h5') -print('done.') - -# %% [markdown] -# ### Reset FC - -# %% -for layer in model.layers[-7:]: - if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'): - weight_initializer = layer.kernel_initializer - bias_initializer = layer.bias_initializer - - old_weights, old_biases = layer.get_weights() - - layer.set_weights([ - weight_initializer(shape=old_weights.shape), - bias_initializer(shape=len(old_biases)) - ]) - - -# %% [markdown] -# ## Training - -# %% [markdown] -# #### Rev2 (THE BEST) -# ``` -# Working: βœ… -# Other: -# + Tensorboard works. -# + Perverts overfitting. -# + Lower memory usage. -# - Slow training. -# + Achieving higher acc. -# - Some models dont work. -# ``` - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -# CONF <--------------------------------------------------------------------------> -# Hyperparameters for training the model: -max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models. -subset_epoch = 6 # subset_epoch: Number of epochs to train each subset. -subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs. -PL_epoch = 24 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. -subset_size = 2048 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 4096. -Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size. -RES_Train = False # RES_Train: Resume training if True. -MAX_LR = 0.011 # MAX_LR: Maximum learning rate. -DEC_LR = 0.00006 # DEC_LR: Learning rate decay. -MIN_LR = 0.0005 # MIN_LR: Minimum learning rate. -RES_LR = 0.006 # RES_LR: Resuming learning rate. -OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True) -Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True. -Debug_OUTPUT_DPS_freq = 32 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch). -TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage. -SAVE_FULLM = True # SAVE_FULLM: Save full model if True. -USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True. -AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True. -AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs). -load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy. -load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'. -load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)). -load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True. -ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint. -ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf'). -Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage. -Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️). -EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️). -Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler. -Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work). -BEST_RSN = 'PAI_model_T' # Best model save name prefix. -ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow). -IMAGE_GEN_PATH = 'Data\\image_SUB_generator.pkl' -# CONF END <----------------------------------------------------------------------> -#Prep -if RES_Train: - MAX_LR = RES_LR - PL_epoch = 1 -#VAR -Total_SUB_epoch_C = 0 # TO FIX TensorBoard -CU_LR = MAX_LR -all_histories = [] -chosen_indices = [] -subset_sizes = [] -best_acc = 0 -best_loss = float('inf') -#Funcs -def normalize_TO_RANGE(arr, min_val, max_val): - arr = arr.astype('float32') - arr = (arr - arr.min()) / (arr.max() - arr.min()) - arr = arr * (max_val - min_val) + min_val - return arr - -def Z_SCORE_normalize(arr): - arr = arr.astype('float32') - mean = np.mean(arr) - std_dev = np.std(arr) - arr = (arr - mean) / std_dev - return arr - -def add_image_grain_TRLRev2(image, intensity = 0.01): - # Generate random noise array - noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \ - + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2 - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -# noise_func_TRLRev2 ([REV1 OLD]) -if not USE_REV2_DP: - def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.08, 0.08) - intensityL1 = random.uniform(-0.05, 0.05) - else: - intensityL2 = random.uniform(-0.09, 0.09) - intensityL1 = random.uniform(-0.06, 0.06) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 112) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.07) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -# noise_func_TRLRev2 ([REV2 NEW]) -else: - def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.07, 0.07) - intensityL1 = random.uniform(-0.06, 0.06) - else: - intensityL2 = random.uniform(-0.09, 0.09) - intensityL1 = random.uniform(-0.07, 0.07) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 112) - - for channel in range(3): # Iterate over each RGB channel - image_channel = image[:, :, channel] - new_image_channel = new_image[:, :, channel] - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image_channel.shape[0], block_size_L2): - for j in range(0, image_channel.shape[1], block_size_L2): - block = image_channel[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block - image_channel = new_image_channel - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image_channel.shape[0], block_size_L1): - for j in range(0, image_channel.shape[1], block_size_L1): - block = image_channel[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block - - new_image[:, :, channel] = new_image_channel - - if add_img_grain: - intensity = random.uniform(0, 0.05) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -#CONST -train_SUB_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.18, - shear_range=0.18, - width_shift_range=0.18, - brightness_range=(0.82, 1.18), - height_shift_range=0.18, - channel_shift_range=100, - featurewise_center=True, - featurewise_std_normalization=True, - zca_whitening=False, - interpolation_order=2, - fill_mode='nearest', - preprocessing_function=noise_func_TRLRev2 - ) -class TerminateOnHighTemp(tf.keras.callbacks.Callback): - def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): - super().__init__() - self.active = active - self.check_every_n_batches = check_every_n_batches - self.high_temp = high_temp - self.low_temp = low_temp - self.pause_time = pause_time - self.batch_counter = 0 - - def on_batch_end(self, batch, logs=None): - if not self.active: - return - self.batch_counter += 1 - if self.batch_counter % self.check_every_n_batches == 0: - temperature = gpu_control.get_temperature() - if temperature > self.high_temp: - print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) - time.sleep(self.pause_time) - while gpu_control.get_temperature() > self.low_temp: - time.sleep(4) - print_Color('Resuming training...', ['yellow']) -class ExtendedTensorBoard(TensorBoard): - def on_epoch_end(self, epoch, logs=None): - logs = logs or {} - logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr) - logs['momentum'] = self.model.optimizer.momentum - super().on_epoch_end(epoch, logs) -class DummyCallback(Callback): - pass -steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 -#callbacks>>> -# EarlyStopping -early_stopping = EarlyStopping(monitor='val_accuracy', - patience=EarlyStopping_P, - verbose=1, restore_best_weights=True, - mode='max' - ) if Use_ES_ONSUBT else DummyCallback() -# ModelCheckpoint -checkpoint_SUB = ModelCheckpoint(f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', - monitor=load_SUB_BRW_MODE, - save_best_only=True, mode=ModelCheckpoint_mode, - save_weights_only = True - ) if load_SUB_BRW else DummyCallback() -checkpoint_SUB.best = ModelCheckpoint_Reset_TO -# TerminateOnHighTemp -TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, - check_every_n_batches=6, - high_temp=72, - low_temp=58, - pause_time=60) -# TensorBoard -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -if Use_extended_tensorboard: - tensorboard_callback = ExtendedTensorBoard( - log_dir=log_dir, - write_images=False, # Uses a lot of memory - histogram_freq=1, - update_freq='epoch', - write_grads=True, - profile_batch='256,512' if Use_tensorboard_profiler else 0 - ) -else: - tensorboard_callback = TensorBoard( - log_dir=log_dir, - write_images=False, # Uses a lot of memory - histogram_freq=1, - update_freq='epoch', - write_grads=True, - profile_batch='256,512' if Use_tensorboard_profiler else 0 - ) -# OneCycleLr -if OneCycleLr_UFTS: - learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR, - steps_per_epoch=steps_per_epoch_train_SUB, - epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) -#PRES -# ... -#MAIN -print('Training the model...') -# INFOp -print_Color('\nSetup Verbose:', ['yellow']) -print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -#warnings -P_warning('RES_Train is True.') if RES_Train else None -print_Color('Setup Verbose END.', ['yellow']) -# MAIN LOOP -try: - for epoch in range(1, max_epoch): - # Start Epoch - STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' - C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT - if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1 - start_FULL_time = time.time() - if Auto_clear_cache: - subprocess.run(["Cache_clear.cmd"], shell=True) - # TSEC: Total-Subset-Epoch-Count - print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) - # DP - if not AdvSubsetC: - print_Color('Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - if AdvSubsetC: - if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0: - print_Color('└───Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - chosen_indices = [] # Reset chosen_indices - - available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices)) - - if len(available_indices) < subset_size: - #DEBUG - # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.') - # If there are not enough available indices, choose from the indices that were chosen the longest time ago - old_indices = chosen_indices[:subset_size - len(available_indices)] - subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False)) - - # Update the list of chosen indices and their sizes - chosen_indices = chosen_indices[len(old_indices):] + subset_indices - subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices) - else: - subset_indices = list(np.random.choice(available_indices, subset_size, replace=False)) - - # Add the chosen indices to the list of already chosen indices - chosen_indices += subset_indices - subset_sizes += [subset_size] * len(subset_indices) - else: - subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) - # Taking the subset - x_SUB_train = x_train[subset_indices] - y_SUB_train = y_train[subset_indices] - x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train) - assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}' - print_Color('Preparing train data...', ['yellow']) - # if epoch == 1: # OLD - # print_Color('- ImageDataGenerator fit...', ['yellow']) - # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) - # print_Color('- ImageDataGenerator fit done.', ['yellow']) - if epoch == 1 or ALWAYS_REFIT_IDG == 2: - if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG: - print_Color('- Loading fitted ImageDataGenerator...', ['yellow']) - train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) - else: - print_Color('- Fitting ImageDataGenerator...', ['yellow']) - IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12 - train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) - pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb')) - print_Color('- ImageDataGenerator fit done.', ['yellow']) - - print_Color('- Augmenting Image Data...', ['yellow']) - train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, - y_SUB_train, - shuffle=False, - batch_size=len(x_SUB_train) - ).next() - print_Color('- Normalizing Image Data...', ['yellow']) - x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) - # x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 1) / 255 - x_SUB_train = x_SUB_train / 255 - x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1) - y_SUB_train = train_SUB_augmented_images[1] - # DEBUG - if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1): - SITD = np.random.choice(subset_size, size=400, replace=False) - S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) - save_images_to_dir(x_SUB_train[SITD], y_SUB_train[SITD], S_dir) - # learning_rate_schedule_SUB - if PL_epoch == 0: - CU_LR = MIN_LR - elif epoch >= PL_epoch and CU_LR > MIN_LR: - if (CU_LR - DEC_LR) < MIN_LR: - CU_LR = MIN_LR - else: - CU_LR -= DEC_LR - if not OneCycleLr_UFTS: - learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR, - steps_per_epoch=steps_per_epoch_train_SUB, - epochs=C_subset_epoch) - #FV - print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + "~*~*") if not OneCycleLr_UFTS else "~*OneCycleLr_UFTS Is ON~*"}]~*...', - ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True) - print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - # Train - print_Color('Training on subset...', ['green']) - start_SUBO_time = time.time() - SUB_history = model.fit(x_SUB_train, - y_SUB_train, - epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C) - batch_size=Conf_batch_size_REV2, - validation_data=(x_test, y_test), - verbose='auto', - initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard - callbacks=[ - learning_rate_schedule_SUB, - TerminateOnHighTemp_CB, - checkpoint_SUB, - early_stopping, - tensorboard_callback - ] - ) - end_SUBO_time = time.time() - print_Color('Subset training done.', ['green']) - if load_SUB_BRW_LMODE == 1: - if max(SUB_history.history['val_accuracy']) > best_acc: - load_weights = True - elif min(SUB_history.history['val_loss']) < best_loss: - load_weights = True - else: - load_weights = False - else: - load_weights = True - - if load_SUB_BRW and load_weights: - print_Color('Loading the best weights...', ['yellow']) - # Get the filename of the best weights file - list_of_files = glob.glob('cache\\*.h5') - try: - best_weights_filename = max(list_of_files, key=os.path.getctime) - print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow']) - model.load_weights(best_weights_filename) - except Exception as Err: - print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red']) - elif load_SUB_BRW and (not load_weights): - # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history["val_accuracy"]):.4f}}}, loss{{{min(SUB_history.history["val_loss"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]', - # ['yellow']) # OLD - print_Color_V2(f'Not loading weights[BSR:acc{{{95.675647:.4f}}}, loss{{{0.0111:.4f}}}|BTR:acc{{{97.56456:.4f}}}, loss{{{0.002:.4f}}}]') - all_histories.append(SUB_history.history) - checkpoint_SUB.best = ModelCheckpoint_Reset_TO - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Evaluate the model on the test data - evaluation = model.evaluate(x_test, y_test, verbose=0) - - # Extract the loss and accuracy from the evaluation results - loss = evaluation[0] - acc = evaluation[1] - print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True) - print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True) - # If the accuracy is higher than the best_acc - if acc > best_acc: - print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.') - # Update the best_acc - best_acc = acc - if SAVE_FULLM: - # Save the model - if SAVE_TYPE == 'TF': - print_Color_V2(f'Saving full model tf format...') - model.save(BEST_RSN, save_format='tf') - else: - print_Color_V2(f'Saving full model H5 format...') - model.save(f'{BEST_RSN}.h5') - model.save_weights('PAI_model_weights.h5') - else: - print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.') - - # If the loss is higher than the best_loss - if loss < best_loss: - print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.') - - # Update the best_acc - best_loss = loss - - if SAVE_FULLM: - # Save the model - if SAVE_TYPE == 'TF': - print_Color_V2(f'Saving full model tf format...') - model.save(BEST_RSN + '_BL', save_format='tf') - else: - print_Color_V2(f'Saving full model H5 format...') - model.save(f'{BEST_RSN}_BL.h5') - model.save_weights('PAI_model_weights_BL.h5') - else: - print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Epoch end - end_time = time.time() - epoch_time = end_time - start_FULL_time - print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec') - epoch_SUB_time = end_SUBO_time - start_SUBO_time - print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec') - epoch_OTHERO_time = epoch_time - epoch_SUB_time - print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec') - print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) - Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard -except KeyboardInterrupt: - print('\nKeyboardInterrupt.') -# End -try: - history = {} - for key in all_histories[0].keys(): - # For each metric, concatenate the values from all histories - history[key] = np.concatenate([h[key] for h in all_histories]) -except Exception as Err: - print(f'Failed to make model `history` var.\nERROR: {Err}') - -print('Training done.\n') -# del vars -try: - del train_SUB_datagen - del train_SUB_augmented_images -except NameError: - pass - -# %% [markdown] -# #### Rev1 (⚠️deprecated⚠️) -# ``` -# Working: βœ… -# Other: -# + Tensorboard works. -# - Can cause overfitting. -# ``` - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF -Conf_batch_size = 8 -OneCycleLr_epoch = 20 -Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) -#TensorBoard conf -TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) -# Learning rate configuration -Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr -MAX_LR = 0.0174 -# First time -if Learning_rate_conf == 1: - learning_rate_start = 8e-04 - learning_rate_max = 5e-03 - learning_rate_min = 5e-05 - learning_rate_rampup_epochs = 5 - learning_rate_sustain_epochs = 1 - learning_rate_exp_decay = .3 - #TEMP - # learning_rate_start = 8e-04 - # learning_rate_max = 1e-02 - # learning_rate_min = 8e-04 - # learning_rate_rampup_epochs = 5 - # learning_rate_sustain_epochs = 3 - # learning_rate_exp_decay = .45 -# 2th time -if Learning_rate_conf == 2: - if Learning_rate_conf_SET2C == 1: - learning_rate_start = 4.10e-06 - learning_rate_max = 4.10e-06 - learning_rate_min = 4.10e-06 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 2: - learning_rate_start = 4e-07 - learning_rate_max = 4e-07 - learning_rate_min = 4e-07 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 3: - learning_rate_start = 5e-04 - learning_rate_max = 5e-04 - learning_rate_min = 5e-04 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 -# Function to build learning rate schedule -if Learning_rate_conf in [1,2]: - def build_learning_rate_fn(lr_start=learning_rate_start, - lr_max=learning_rate_max, - lr_min=learning_rate_min, - lr_rampup_epochs=learning_rate_rampup_epochs, - lr_sustain_epochs=learning_rate_sustain_epochs, - lr_exp_decay=learning_rate_exp_decay): - lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync - def learning_rate_fn(epoch): - if epoch < lr_rampup_epochs: - lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start - elif epoch < lr_rampup_epochs + lr_sustain_epochs: - lr = lr_max - else: - lr = (lr_max - lr_min) *\ - lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min - return lr - return learning_rate_fn - -# Calculate steps per epoch -steps_per_epoch_train = len(x_train) // Conf_batch_size - -# Set up callbacks -class EpochEndMON(tf.keras.callbacks.Callback): - def on_epoch_end(self, epoch, logs=None): - optimizer = self.model.optimizer - if hasattr(optimizer, 'lr'): - lr = tf.keras.backend.get_value(optimizer.lr) - print(f'\nLearning rate for epoch {epoch+1} is {lr}') - if hasattr(optimizer, 'momentum'): - momentum = tf.keras.backend.get_value(optimizer.momentum) - print(f'Momentum for epoch {epoch+1} is {momentum}') - if logs: - val_loss = logs.get('val_loss') - val_acc = logs.get('val_accuracy') - print(f'Validation loss for epoch {epoch+1} is {val_loss}') - print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') - - print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') - -# Instantiate the callback -EpochEndMON_callback = EpochEndMON() -if Learning_rate_conf in [1,2]: - learning_rate_fn = build_learning_rate_fn() - learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) -else: - learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch) -if SAVE_TYPE == 'TF': - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -else: - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' -tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True) - -# Train the model -print('Log dir:', log_dir) -#MInfo -print('Input Shape:', model.input_shape) -print('Output Shape:', model.output_shape) -print('Loss Function:', model.loss) -print('Training the model...\n') -history = model.fit(x_train, - y_train, - epochs=256, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) -print('Training done.\n') - -# %% [markdown] -# ## Saving model weights -# - -# %% -Extra_EXT = '_T' -# Save the weights -print('Saving weights...') -model.save_weights('PAI_model_weights.h5') -print('Saving full model...') -if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') -else: - try: - model.save(f'PAI_model{Extra_EXT}.h5') - except ValueError: - print('failed to save in .h5 format!') - print('Saving full model in tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') - -# %% [markdown] -# ## Garbage Collection (memory) - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() - -# %% [markdown] -# ## Analyse model Training performance - -# %% -# Save history -save_list(history, 'history\\model_history.pkl.gz', compress=True) - -# %% -# load history -history = load_list('history\\model_history.pkl.gz', compressed=True) - -# %% -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -import seaborn as sns - -# Chunk size for 3D plot -chunk_size = 6 # Change this to your desired chunk size - -def convert_history(history): - if isinstance(history, tf.keras.callbacks.History): - return history.history - else: - return history - -def chunked_data(data, chunk_size): - return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)] - - -try: - EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' - history = convert_history(history) - - # Calculate deltas - delta_loss = np.diff(history['loss']) - delta_accuracy = np.diff(history['accuracy']) - - try: - delta_val_loss = np.diff(history['val_loss']) - delta_val_accuracy = np.diff(history['val_accuracy']) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') - - plt.figure(figsize=(16, 10)) - # Loss - plt.subplot(2, 2, 1) - plt.plot(history['loss'], label='loss') - try: - plt.plot(history['val_loss'], label='val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_loss.') - plt.title('Model Loss') - plt.ylabel('Loss') - plt.xlabel(EPM) - plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - # Density plot for loss - plt.subplot(2, 2, 2) - plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) - try: - plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss (density plot).') - plt.title('Density Plot for Loss') - plt.xlabel('Loss') - plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - - # Accuracy - plt.subplot(2, 2, 3) - plt.plot(history['accuracy'], label='accuracy') - try: - plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy.') - plt.title('Model Accuracy') - plt.ylabel('Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Density plot for accuracy - plt.subplot(2, 2, 4) - plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) - try: - plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy (density plot).') - plt.title('Density Plot for Accuracy') - plt.xlabel('Accuracy') - plt.grid(True) - - # Delta Loss - plt.figure(figsize=(14, 8)) - plt.subplot(2, 2, 1) - plt.plot(delta_loss, label='delta_loss') - try: - plt.plot(delta_val_loss, label='delta_val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_loss.') - plt.title('Delta Model Loss') - plt.ylabel('Delta Loss') - plt.ylim(top=1.5, bottom=-1.5) - plt.xlabel(EPM) - plt.grid(True) - # Delta Accuracy - plt.subplot(2, 2, 2) - plt.plot(delta_accuracy, label='delta_accuracy') - try: - plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_accuracy.') - plt.title('Delta Model Accuracy') - plt.ylabel('Delta Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Calculate chunked data - chunked_loss = chunked_data(history['val_loss'], chunk_size) - chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) - - # Clip the loss values to a maximum of max(history['val_loss'][10:]) - max_loss = max(history['val_loss'][10:]) - chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) - - # Create 3D surface plots for each chunk - fig = plt.figure(figsize=(14, 8)) - ax = fig.add_subplot(121, projection='3d') - X = np.arange(len(chunked_loss)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Loss') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Loss') - - ax = fig.add_subplot(122, projection='3d') - X = np.arange(len(chunked_accuracy)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Accuracy') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Accuracy') - - # Function to calculate the average of chunks - def chunked_average(values, chunk_size): - return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] - - avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) - avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) - - # Find the chunk with the highest average accuracy - max_acc_chunk_index = np.argmax(avg_accuracy_chunks) - max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] - - # Create a pile plot for accuracy - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy') - plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') - plt.xlabel('Chunk') - plt.ylabel('Average Accuracy') - plt.title('Average Validation Accuracy per Chunk') - plt.legend() - - # Create a pile plot for loss - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') - plt.xlabel('Chunk') - plt.ylabel('Average Loss') - plt.title('Average Validation Loss per Chunk') - plt.legend() - - # Function to calculate the average of each epoch across chunks, ignoring the first chunk - def average_across_chunks(values, chunk_size): - num_chunks = len(values) // chunk_size - avg_values = [] - for epoch in range(chunk_size): - epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] - avg_values.append(np.mean(epoch_values)) - return avg_values - - # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk - avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) - avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) - - # Create a bar plot for average accuracy and loss of each epoch across chunks - plt.figure(figsize=(12, 6)) - - # Create an index for each epoch - epoch_indices = np.arange(len(avg_accuracy_epochs)) - - # Plot accuracy and loss as bars - plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) - plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) - - # Add labels and title - plt.xlabel('Epoch (within chunk)') - plt.ylabel('Average Value') - plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') - plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers - plt.legend() - - plt.tight_layout() - plt.show() - -except (ValueError, NameError) as E: - print(f'\033[91mFailed to load model history.\nError: {E}') - -# %% [markdown] -# ## Analyse model Predicting performance - -# %% [markdown] -# ### Gradcam heatmap - -# %% [markdown] -# #### V2 - -# %% -def compute_heatmap(model, img_array, conv_layer_name, pred_index): - """ - Helper function to compute the heatmap for a given convolutional layer. - """ - grad_model = tf.keras.models.Model( - [model.inputs], - [model.get_layer(conv_layer_name).output, model.output] - ) - - with tf.GradientTape() as tape: - conv_layer_output, preds = grad_model(img_array) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, conv_layer_output) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - - conv_layer_output = conv_layer_output[0] - heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] - heatmap = tf.squeeze(heatmap) - heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) - return heatmap - -def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0): - """ - Function to compute the Grad-CAM heatmap for a specific class, given an input image. - """ - if pred_index is None: - preds = model.predict(img_array) - pred_index = tf.argmax(preds[0]) - - # Compute heatmap for the last convolutional layer - heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index) - - # Apply threshold and adjust sensitivity - heatmap = np.where(heatmap > threshold, heatmap, 0) - heatmap = heatmap ** sensitivity_map - - if second_last_conv_layer_name is not None: - # Compute heatmap for the second last convolutional layer - heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) - - # Apply threshold and adjust sensitivity - heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0) - heatmap_second = heatmap_second ** sensitivity_map - - # Average the two heatmaps - heatmap = (heatmap + heatmap_second) / 2.0 - - return heatmap - -# %% [markdown] -# #### V3 - -# %% [markdown] -# ### Main test - -# %% -import seaborn as sns -from sklearn.metrics import confusion_matrix, accuracy_score -from scipy.stats import binom -from tqdm import tqdm -import efficientnet.tfkeras -import cv2 -import gc -# Garbage Collection (memory) -gc.collect() - -Extra_EXT = '_T' # _T or _T_BL -prob_L = 0.9995 -tick_spacing = 5 -Train_data_test = False -if SAVE_TYPE == 'TF': - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}') -else: - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}.h5') - -# Ensure the model's input_shape matches your data -assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' - -# Make predictions on validation data -val_predictions = model.predict(x_val) -val_predictions = np.argmax(val_predictions, axis=1) - -# Make predictions on Train data -if Train_data_test: - Train_predictions = model.predict(x_train) - Train_predictions = np.argmax(Train_predictions, axis=1) - -# Make predictions on test data -test_predictions = model.predict(x_test) -test_predictions = np.argmax(test_predictions, axis=1) - -# Convert y_val and y_test from one-hot encoder to their original form -y_val_original = np.argmax(y_val, axis=1) -y_test_original = np.argmax(y_test, axis=1) -if Train_data_test: - y_train_original = np.argmax(y_train, axis=1) - -# Calculate accuracy on validation data -val_accuracy = accuracy_score(y_val_original, val_predictions) - -# Calculate accuracy on Train data -if Train_data_test: - Train_accuracy = accuracy_score(y_val_original, Train_predictions) - -# Calculate accuracy on test data -test_accuracy = accuracy_score(y_test_original, test_predictions) - -# Print acc -if Train_data_test: - print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}') -print(f'The accuracy of the model on validation data is {val_accuracy:.2%}') -print(f'The accuracy of the model on test data is {test_accuracy:.2%}') - -# Visualize the predictions on validation data as a grid of squares -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - plt.imshow(x_val[i]) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() -#Heatmap -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - img = x_val[i] - heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) - heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) - heatmap = np.uint8(255 * heatmap) - # Apply Adaptive Histogram Equalization - clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) # Create CLAHE object - # heatmap = clahe.apply(heatmap) - heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) - if RANGE_NOM: - superimposed_img = (heatmap / 255) * 0.7 + img - else: - superimposed_img = (heatmap / 255) * 0.5 + (img / 255) - #clip - superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] - plt.imshow(superimposed_img) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() - -# Define the list of labels -labels = ['NORMAL', 'PNEUMONIA'] - -# Create a confusion matrix for validation data -val_cm = confusion_matrix(y_val_original, val_predictions) - -# Create a confusion matrix for test data -test_cm = confusion_matrix(y_test_original, test_predictions) - -# Plot the confusion matrix as a heatmap for validation data -plt.figure(figsize=(8, 6)) -sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Validation Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Plot the confusion matrix as a heatmap for test data -plt.figure(figsize=(8, 6)) -sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Test Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Define the range of test data sizes to use -data_sizes = range(1, len(x_test), 4) -# Calculate the probability of a wrong prediction based on test accuracy -prob_wrong = 1 - test_accuracy - -# Create a list to store the number of incorrect predictions for each test data size -incorrect_predictions = [] - -# Generate predictions and track incorrect predictions for each data size -for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): - # Garbage Collection (memory) - gc.collect() - # Randomly select a subset of test data - indices = np.random.choice(len(x_test), size, replace=False) - x_test_subset = x_test[indices] - y_test_subset = y_test[indices] - - # Make predictions on the subset of test data - test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) - test_predictions = np.argmax(test_predictions, axis=1) - y_test_original_subset = np.argmax(y_test_subset, axis=1) - - # Calculate the number of incorrect predictions - incorrect_preds = np.sum(test_predictions != y_test_original_subset) - incorrect_predictions.append(incorrect_preds) - -# Plot the number of incorrect predictions vs. the number of data points -plt.figure(figsize=(10, 6)) -plt.plot(data_sizes, incorrect_predictions) -plt.xlabel('Number of Data Points') -plt.ylabel('Number of Incorrect Predictions') -# Add gridlines for the x and y axes -plt.grid(True) - -# Change the tick spacing for the x and y axes -plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) -plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) - -plt.title('Number of Incorrect Predictions vs. Number of Data Points') -plt.show() - -# Define the range of test data sizes to use -data_sizes = range(1, len(x_test), 1) - -# Calculate the probability of a wrong prediction based on test accuracy -prob_wrong = 1 - test_accuracy - -# Create a list to store the probability of getting at least one wrong answer for each test data size -probabilities = [] - -# Calculate the probability of getting at least one wrong answer for each data size -for size in data_sizes: - # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 - cdf = binom.cdf(0, size, prob_wrong) - # Subtract the CDF from 1 to get the probability of getting at least one wrong answer - prob = 1 - cdf - probabilities.append(prob) - -# Find the index of the first data point that has a probability greater than prob_L% -index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) - -# Limit the x-axis to the first data point that has a probability greater than prob_L% -data_sizes = data_sizes[:index+1] -probabilities = probabilities[:index+1] - -# Plot the probability vs. the number of data points -plt.figure(figsize=(10, 6)) -plt.plot(data_sizes, probabilities) -plt.xlabel('Number of Data Points') -plt.ylabel('Probability') - -# Add gridlines for the x and y axes -plt.grid(True) - -# Change the tick spacing for the x and y axes -plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 10)) -plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100)) - -plt.ylim(top=1.01) - -plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') -plt.show() - - +# Copyright (c) 2023 Aydin Hamedi +# +# This software is released under the MIT License. +# https://opensource.org/licenses/MIT + +# %% [markdown] +# # keras/TF model +# 
+#  Copyright (c) 2023 Aydin Hamedi
+#  
+#  This software is released under the MIT License.
+#  https://opensource.org/licenses/MIT
+#
+ +# %% [markdown] +# ## Pre Conf + +# %% +CPU_only = False # True to Force TF to use the cpu + +# %% [markdown] +# ## Pylibs + +# %% +import os +import sys +import time +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +if CPU_only: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +import cv2 +import glob +import keras +import pprint +import random +import shutil +import gzip +import glob +import pickle +import datetime +import subprocess +import gpu_control +import numpy as np +import pandas as pd +from tqdm import tqdm +import seaborn as sns +from hyperas import optim +# import tensorflow_addons as tfa +from keras_adabound import AdaBound +from importlib import reload +from keras.losses import categorical_crossentropy +import tensorflow as tf +from keras.models import Model +from scipy.ndimage import zoom +import matplotlib.pyplot as plt +from model_profiler import model_profiler +from keras_gradient_noise import add_gradient_noise +from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax +# from tensorflow_addons.optimizers import Yogi +from adabelief_tf import AdaBeliefOptimizer +from sklearn.preprocessing import LabelEncoder +from imblearn.over_sampling import SMOTE +from keras.regularizers import l2 +from keras.models import load_model +from matplotlib import pyplot as plt +from PIL import Image, ImageDraw, ImageFont +from keras import Sequential +from random import randint, choice, shuffle +from keras.callbacks import EarlyStopping +from keras.callbacks import TensorBoard +from keras.utils import to_categorical +from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.layers import Conv2D,\ + MaxPooling2D,\ + Flatten,\ + Dense,\ + Dropout,\ + BatchNormalization,\ + SeparableConv2D,\ + Input, Concatenate,\ + GlobalAveragePooling2D,\ + CuDNNLSTM, concatenate,\ + Reshape, Multiply +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from Utils.Other import * +# Other +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + + +# %% [markdown] +# ## Conf +# + +# %% [markdown] +# ### Data processing conf + +# %% +# Directory paths# Directory paths for training, test and validation image data +train_dir = 'Database\\Train\\Data\\train' +test_dir = 'Database\\Train\\Data\\test' +validation_dir = 'Database\\Train\\Data\\val' +img_res = [224, 224, 3] +# img_res = [324, 324, 3] +# img_res = [224, 224, 3] +# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) +interpolation_order_IFG = 2 +categorical_IMP = True +Make_EV_DATA = False +R_fill_mode = True +add_img_grain = True +Save_TS = True +Use_SMOTE = False # (⚠️Beta⚠️) +ADBD = 1 +OP_HDC = False +SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 +LNTS = 0 +Debug_OUT = False +adjust_brightness_Mode = True +RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️) +scale_data_NP_M = False # (⚠️deprecated⚠️) + +# %% [markdown] +# ### Training + +# %% +SAVE_TYPE = 'H5' +Use_mixed_float16 = False +#Other +if Use_mixed_float16: + tf.keras.mixed_precision.set_global_policy('mixed_float16') +else: + tf.keras.mixed_precision.set_global_policy('float32') + +print(tf.keras.mixed_precision.global_policy()) + +# %% [markdown] +# ## data processing +# + +# %% +#Z_SCORE_normalize +def Z_SCORE_normalize(arr): + arr = arr.astype('float32') + mean = np.mean(arr) + std_dev = np.std(arr) + arr = (arr - mean) / std_dev + return arr +#normalize_TO_RANGE +def normalize_TO_RANGE(arr, min_val, max_val): + arr = arr.astype('float32') + arr = (arr - arr.min()) / (arr.max() - arr.min()) + arr = arr * (max_val - min_val) + min_val + return arr +#scale_data +def scale_data_NP(data): + if scale_data_NP_M: + data = data.astype('float32') + data = (data - 127.5) / 127.5 + return data + else: + return data / 255 +#add_image_grain +def add_image_grain(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +#apply_clahe_rgb_array +def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): + # Create a CLAHE object + clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) + + # Iterate over each image in the array + for i in range(len(images)): + # Split the image into color channels + b, g, r = cv2.split(images[i]) + + # Convert the channels to the appropriate format + b = cv2.convertScaleAbs(b) + g = cv2.convertScaleAbs(g) + r = cv2.convertScaleAbs(r) + + # Apply adaptive histogram equalization to each channel + equalized_b = clahe.apply(b) + equalized_g = clahe.apply(g) + equalized_r = clahe.apply(r) + + # Merge the equalized channels back into an image + equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) + + # Replace the original image with the equalized image in the array + images[i] = equalized_image + + return images +#noise_func +def noise_func(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.05, 0.05) + intensityL1 = random.uniform(-0.04, 0.04) + else: + intensityL2 = random.uniform(-0.06, 0.06) + intensityL1 = random.uniform(-0.04, 0.04) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026 + new_image = add_image_grain(new_image, intensity=intensity) + return new_image +#shuffle_data +def shuffle_data(x, y): + indices = np.arange(x.shape[0]) + np.random.shuffle(indices) + x = x[indices] + y = y[indices] + return x, y +#save_images_to_dir +def save_images_to_dir(images, labels, dir_path): + # create the directory if it doesn't exist + if not os.path.exists(dir_path): + os.makedirs(dir_path) + # iterate over the images and labels + for i, (image, label) in enumerate(zip(images, labels)): + # get the class label + class_label = np.argmax(label) + # create the file path + file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') + # save the image to the file path + plt.imsave(file_path, image.squeeze()) + # compress the directory + shutil.make_archive(dir_path, 'gztar', dir_path) + # remove the original directory + shutil.rmtree(dir_path) +#Debug_img_Save +def Debug_img_Save(img, id = 'DEF'): + SITD = np.random.choice(img.shape[0], size=400, replace=False) + S_dir = f'Samples\\Debug\\{id}\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) + save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir) +# Create an ImageDataGenerator for the training set +if OP_HDC: + print_Color('Using OP_HDC IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.86, 1.1), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=interpolation_order_IFG, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +else: + print_Color('Using Def IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.26, + shear_range=0.25, + width_shift_range=0.25, + brightness_range=(0.78, 1.1), + height_shift_range=0.25, + channel_shift_range=100, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +train_datagen_SM = ImageDataGenerator( + horizontal_flip=False, + vertical_flip=False, + rotation_range=20, + zoom_range=0.07, + shear_range=0.07, + width_shift_range=0.07, + brightness_range=(0.99, 1.01), + height_shift_range=0.07, + channel_shift_range=0, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False +) +# Create an iterator for the training set +train_generator_SM = train_datagen_SM.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') +# Create an ImageDataGenerator for the validation set (OP) +if Make_EV_DATA: + val_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the validation set + val_generator = val_datagen.flow_from_directory( + validation_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), + class_mode='binary', + color_mode='rgb') + + # Create an ImageDataGenerator for the test set + test_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the test set + test_generator = test_datagen.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), + class_mode='binary', + color_mode='rgb') +# Load all images and labels into memory +print_Color('Loading all images and labels into memory...', ['yellow']) +x_train, y_train = next(iter(train_generator_SM)) +if Make_EV_DATA: + x_val, y_val = next(iter(val_generator)) + x_test, y_test = next(iter(test_generator)) +if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG +# fit parameters from data +# train_datagen.fit(x_train) +#to_categorical (TEMP) +if categorical_IMP: + print_Color('Making categorical data...', ['yellow']) + y_train = to_categorical(y_train, num_classes=2) + if Make_EV_DATA: + y_val = to_categorical(y_val, num_classes=2) + y_test = to_categorical(y_test, num_classes=2) +# Use_SMOTE +if Use_SMOTE: + print_Color('SMOTE...', ['yellow']) + # Convert y_train from one-hot encoding to label encoding + y_train_label_encoded = np.argmax(y_train, axis=1) + + # Print the original label distribution + unique, counts = np.unique(y_train_label_encoded, return_counts=True) + print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) + + # Use SMOTE to oversample the minority class + smote = SMOTE(random_state=42) + x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) + + # Print the resampled label distribution + unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) + print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) + + # Reshape x_train_res back to the original x_train shape + x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) + + # Convert y_train_res from label encoding back to one-hot encoding + y_train_res = to_categorical(y_train_res_label_encoded) + + # Calculate the ratio of two labels after resampling + pneumonia_count = np.sum(y_train_res[:, 1]) + total_count = y_train_res.shape[0] + label_ratio_res = pneumonia_count / total_count + label_ratio_percentage_res = label_ratio_res * 100 + + # Replace the original data with the resampled data + x_train = x_train_res + y_train = y_train_res + + # Delete the resampled data to free up memory + del x_train_res, y_train_res_label_encoded, y_train_res +# Generating augmented data +print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', + ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], + advanced_mode=True) +if ADBD > 0: + for i in range(ADBD): + # ADB_clip_limit Scheduler>>> + if i == 0: + ADB_clip_limit = 0.8 + else: + #V1>>> + CL_SLM = 2.4 + ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) + # Try it in win graphing calculator copy and paste: + # β”Œ-------------┬--┬---------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ + # β””-------------β”΄--β”΄---------------β”˜ + #V2>>> + # CL_SLM_2 = 1.4 + # CL_SLM_Start_2 = 2 + # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) + # Try it in win graphing calculator copy and paste: + # β”Œ-----------------┬--┬-------------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ + # β””-----------------β”΄--β”΄-------------------β”˜ + print(f'> Generating ADB[{i+1}/{ADBD}]...') + # prepare an iterators to scale images + train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) + + # get augmented data + x_train_augmented, y_train_augmented = train_iterator.next() + print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') + print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') + x_train_augmented = np.clip(x_train_augmented, 0, 255) + if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG + #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) + x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss + print(f'> └───Adding the Generated ADB...') + if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG + # append augmented data to original data + x_train = np.concatenate([x_train, x_train_augmented]) + y_train = np.concatenate([y_train, y_train_augmented]) + #free up memory + del y_train_augmented + del x_train_augmented +# normalizing +print_Color('Normalizing image data...', ['yellow']) +if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG +x_train = np.clip(x_train, 0, 255) +if RANGE_NOM: + x_train = scale_data_NP(x_train) +y_train = np.array(y_train) +if Make_EV_DATA: + x_test = np.clip(x_test, 0, 255) + x_val = np.clip(x_val, 0, 255) + if RANGE_NOM: + x_val = scale_data_NP(x_val) + y_val = np.array(y_val) + if RANGE_NOM: + x_test = scale_data_NP(x_test) + y_test = np.array(y_test) +if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG +# Check the data type of image data +print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) +# Check the range of image data +print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) +# Calculate the ratio of two labels +if categorical_IMP: + label_sums = np.sum(y_train, axis=0) + label_ratio = label_sums / (np.sum(y_train) + 1e-10) + label_ratio_percentage = label_ratio * 100 + print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', + ['normal', 'red', 'magenta', 'green'], advanced_mode=True) +print_Color('Setting LNTS...', ['yellow']) +# Get the total number of samples in the arrays +num_samples = x_train.shape[0] +print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +if LNTS != 0: + print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) + print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) + # Generate random indices to select LNTS samples + indices = np.random.choice(num_samples, size=LNTS, replace=False) + # Select the samples using the generated indices + x_selected = x_train[indices] + y_selected = y_train[indices] + x_train = x_selected + y_train = y_selected + #free up memory + del x_selected + del y_selected + del indices + #Debug + num_samples = x_train.shape[0] + print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +# Shuffle the training data +print_Color('shuffling data...', ['yellow']) +x_train, y_train = shuffle_data(x_train, y_train) +#save_images_to_dir +if Save_TS: + print_Color('Saving TS...', ['yellow']) + SITD = np.random.choice(num_samples, size=400, replace=False) + S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) + if RANGE_NOM: + if scale_data_NP_M: + save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) +print_Color('Done.', ['green']) + +# %% [markdown] +# ## Save EV Dataset + +# %% +np.save(f'Database\\Test\\Data\\x_val{SL_EX}.npy', x_val) +np.save(f'Database\\Test\\Data\\y_val{SL_EX}.npy', y_val) +np.save(f'Database\\Test\\Data\\x_test{SL_EX}.npy', x_test) +np.save(f'Database\\Test\\Data\\y_test{SL_EX}.npy', y_test) + +# %% [markdown] +# ## Load EV Dataset + +# %% +x_val = np.load(f'Database\\Test\\Data\\x_val{SL_EX}.npy') +y_val = np.load(f'Database\\Test\\Data\\y_val{SL_EX}.npy') +x_test = np.load(f'Database\\Test\\Data\\x_test{SL_EX}.npy') +y_test = np.load(f'Database\\Test\\Data\\y_test{SL_EX}.npy') + +# %% [markdown] +# ## Data Analyzation + +# %% +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns +from scipy.stats import zscore + +# Select a subset of your data +subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels +subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values +indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False) +indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False) +subset_pixels = x_train[indices_pixels] +subset_mean = x_train[indices_mean] + +# Reshape the data for calculating Z-scores +reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1]) +reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1]) + +# Calculate the mean intensity +mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1) +mean_intensity_mean = reshaped_data_mean.mean(axis=-1) + +# Stack the mean intensity with the reshaped data +data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)]) +data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)]) + +# Calculate Z-scores +z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0)) +z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0)) + +# Identify outliers +outliers_pixels = np.where(z_scores_pixels > 3) +outliers_mean = np.where(z_scores_mean > 3) + +# Create a 3D scatter plot for RGB channels +fig = plt.figure(figsize=(10, 20)) + +# Plot for individual pixels +ax = fig.add_subplot(211, projection='3d') +ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1) +ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red') +ax.set_title('Z-Score Scatter Plot for Individual Pixels') +ax.set_xlabel('Red') +ax.set_ylabel('Green') +ax.set_zlabel('Blue') + +# Plot for mean RGB values +ax = fig.add_subplot(212, projection='3d') +ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1) +ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red') +ax.set_title('Z-Score Scatter Plot for Mean RGB Values') +ax.set_xlabel('Red') +ax.set_ylabel('Green') +ax.set_zlabel('Blue') + +# Density plot of the mean intensity +plt.figure(figsize=(10, 5)) +sns.kdeplot(data=z_scores_pixels[:, -1], fill=True) +plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels') +plt.xlabel('Z-Score') + +sns.kdeplot(data=z_scores_mean[:, -1], fill=True) +plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values') +plt.xlabel('Z-Score') + +# Display the plot +plt.show() + +# %% [markdown] +# ## Creating the model +# + +# %% [markdown] +# ### Rev1 +# ``` +# recommended: ⚠️ +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: β‰…95.1 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(EfficientNetB7) +# ``` + +# %% +from keras.applications import EfficientNetB7 + +EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.1 +# ``` +# recommended: ❌ +# statuses: S.Ready (can improve) +# Working: ❌ +# Max fine tuned acc: β‰…93.2 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(ConvNeXtLarge) +# ``` + +# %% +from keras.applications import ConvNeXtLarge + +ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False) +# define new model +model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.2 +# ``` +# recommended: βœ… +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: 95.3 +# Max fine tuned acc TLRev2: 96.96 +# type: transfer learning>>>(EfficientNetB7::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +# FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENB7(input_shape=( + img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / + len(base_model.layers)) * 100 + print( + f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model.output) + Dense_L1 = Dense(512, activation='relu', + kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', + kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model( + inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + # OPT/compile + opt = SGD(momentum=0.9, nesterov=False) + # opt = Nadam() + # opt = Adamax() + # opt = RMSprop(momentum=0.9) + # opt = Adagrad() + # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.3 +# ``` +# recommended: ❌ +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: ⚠️ +# type: transfer learning>>>(EfficientNetB7|Xception::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +from keras.applications.xception import Xception + +#FUNC +def Combo_Model(freeze_layers1, freeze_layers2): + # Define a common input + common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) + + # Base model 1 + base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) + base_model1_out = base_model1(common_input) + + # Base model 2 + base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False) + # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) + base_model2_out = base_model2(common_input) + + print('Total base_model1 layers: ', len(base_model1.layers)) + print('Total base_model2 layers: ', len(base_model2.layers)) + + # Freeze the specified number of layers in both models + for layer in base_model1.layers[:freeze_layers1]: + layer.trainable = False + for layer in base_model2.layers[:freeze_layers2]: + layer.trainable = False + + # Unfreeze the rest in both models + for layer in base_model1.layers[freeze_layers1:]: + layer.trainable = True + for layer in base_model2.layers[freeze_layers2:]: + layer.trainable = True + + # Combine the output of the two base models + combined = concatenate([GlobalAveragePooling2D()(base_model1_out), GlobalAveragePooling2D()(base_model2_out)]) + + # adding CDL + Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined) + Dropout_L1 = Dropout(0.4)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + combo_model = Model(inputs=common_input, outputs=predictions) + print('Total model layers: ', len(combo_model.layers)) + + #OPT/compile + opt = SGD(momentum=0.9) + combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return combo_model + +print('Creating the model...') +# Main +freeze_layers_1 = 0 +freeze_layers_2 = 0 +model = Combo_Model(freeze_layers_1, freeze_layers_2) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.4 +# ``` +# recommended: ⚠️ +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: β‰…95.64 +# type: transfer learning>>>(EfficientNetV2XL) +# ``` + +# %% +from keras_efficientnet_v2 import EfficientNetV2XL + +EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4) +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +# opt = SGD(momentum=0.9) +opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False) +# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +freeze_layers = 0 +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta + +# %% +from efficientnet.keras import EfficientNetL2 as KENBL2 +#FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]), + weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5', + include_top=False, + drop_connect_rate=0) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 + print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model.output) + Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta2 + +# %% +from keras_efficientnet_v2 import EfficientNetV2S + +EfficientNet_M = EfficientNetV2S(input_shape=(img_res[0], img_res[1], img_res[2]), num_classes=2, dropout=0.5) +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +freeze_layers = 0 +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta3 + +# %% +from keras.applications import ConvNeXtXLarge +from keras.layers import Lambda +#FUNC +def Eff_B7_NS(): + # Add a Lambda layer at the beginning to scale the input + input = Input(shape=(img_res[0], img_res[1], img_res[2])) + x = Lambda(lambda image: image * 255)(input) + + base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x) + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model) + Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +model = Eff_B7_NS() +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### LR FINDER + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF/Other +LRF_OPT = SGD(momentum=0.9) +LFR_batch_size = 1 # or any other batch size that fits in your memory +LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) +# Instantiate LrFinder +lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) + +# Start range_test +lr_find.range_test(LRF_dataset) +lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) + +# %% [markdown] +# ### Model vis + +# %% +dot_img_file = 'model_1.png' +keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True) + +# %% [markdown] +# ## Loading the model + +# %% [markdown] +# ### Loading the full model + +# %% +import efficientnet.tfkeras +# Configuration +PRMC = False +freeze_from_opposite = False +Extra_EXT = '_T' +freeze_layers = 0 +randomly_frozen_layers = 0 +freeze_last_seven = True +# CEC_opt = Adagrad() +# CEC_opt = Yogi() +# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +CEC_opt = SGD(momentum=0.9, nesterov=False) +# CEC_opt = Adam() +# Main +try: + if SAVE_TYPE == 'TF': + model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) + else: + model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) +except (ImportError, IOError) as e: + print(f'\033[91mfailed to load the model ERROR:\n{e}') +else: + print('\033[92mLoading model done.') + if not PRMC: + print('Compiling the AI model...\033[0m') + + for layer in model.layers: + layer.trainable = True + + # Select random layers to freeze + frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) + + for i, layer in enumerate(model.layers): + if i in frozen_layer_indices: + layer.trainable = False + else: + if freeze_from_opposite and (i > len(model.layers) - freeze_layers): + layer.trainable = False + elif (not freeze_from_opposite) and i < freeze_layers: + layer.trainable = False + else: + layer.trainable = True + + for layer in model.layers[-7:]: + layer.trainable = not freeze_last_seven + + model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) + model.summary(show_trainable=True, expand_nested=True) + print('done.') + +# %% [markdown] +# ### Loading model weights + +# %% +model.load_weights('PAI_model_weights.h5') +print('done.') + +# %% [markdown] +# ### Reset FC + +# %% +for layer in model.layers[-7:]: + if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'): + weight_initializer = layer.kernel_initializer + bias_initializer = layer.bias_initializer + + old_weights, old_biases = layer.get_weights() + + layer.set_weights([ + weight_initializer(shape=old_weights.shape), + bias_initializer(shape=len(old_biases)) + ]) + + +# %% [markdown] +# ## Training + +# %% [markdown] +# #### Rev2 (THE BEST) +# ``` +# Working: βœ… +# Other: +# + Tensorboard works. +# + Perverts overfitting. +# + Lower memory usage. +# - Slow training. +# + Achieving higher acc. +# - Some models dont work. +# ``` + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +# CONF <--------------------------------------------------------------------------> +# Hyperparameters for training the model: +max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models. +subset_epoch = 6 # subset_epoch: Number of epochs to train each subset. +subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs. +PL_epoch = 24 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. +subset_size = 2048 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 4096. +Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size. +RES_Train = False # RES_Train: Resume training if True. +MAX_LR = 0.011 # MAX_LR: Maximum learning rate. +DEC_LR = 0.00006 # DEC_LR: Learning rate decay. +MIN_LR = 0.0005 # MIN_LR: Minimum learning rate. +RES_LR = 0.006 # RES_LR: Resuming learning rate. +OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True) +Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True. +Debug_OUTPUT_DPS_freq = 32 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch). +TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage. +SAVE_FULLM = True # SAVE_FULLM: Save full model if True. +USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True. +AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True. +AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs). +load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy. +load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'. +load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)). +load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True. +ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint. +ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf'). +Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage. +Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️). +EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️). +Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler. +Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work). +BEST_RSN = 'PAI_model_T' # Best model save name prefix. +ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow). +IMAGE_GEN_PATH = 'Data\\image_SUB_generator.pkl' +# CONF END <----------------------------------------------------------------------> +#Prep +if RES_Train: + MAX_LR = RES_LR + PL_epoch = 1 +#VAR +Total_SUB_epoch_C = 0 # TO FIX TensorBoard +CU_LR = MAX_LR +all_histories = [] +chosen_indices = [] +subset_sizes = [] +best_acc = 0 +best_loss = float('inf') +#Funcs +def normalize_TO_RANGE(arr, min_val, max_val): + arr = arr.astype('float32') + arr = (arr - arr.min()) / (arr.max() - arr.min()) + arr = arr * (max_val - min_val) + min_val + return arr + +def Z_SCORE_normalize(arr): + arr = arr.astype('float32') + mean = np.mean(arr) + std_dev = np.std(arr) + arr = (arr - mean) / std_dev + return arr + +def add_image_grain_TRLRev2(image, intensity = 0.01): + # Generate random noise array + noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \ + + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2 + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +# noise_func_TRLRev2 ([REV1 OLD]) +if not USE_REV2_DP: + def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.08, 0.08) + intensityL1 = random.uniform(-0.05, 0.05) + else: + intensityL2 = random.uniform(-0.09, 0.09) + intensityL1 = random.uniform(-0.06, 0.06) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 112) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.07) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +# noise_func_TRLRev2 ([REV2 NEW]) +else: + def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.07, 0.07) + intensityL1 = random.uniform(-0.06, 0.06) + else: + intensityL2 = random.uniform(-0.09, 0.09) + intensityL1 = random.uniform(-0.07, 0.07) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 112) + + for channel in range(3): # Iterate over each RGB channel + image_channel = image[:, :, channel] + new_image_channel = new_image[:, :, channel] + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image_channel.shape[0], block_size_L2): + for j in range(0, image_channel.shape[1], block_size_L2): + block = image_channel[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block + image_channel = new_image_channel + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image_channel.shape[0], block_size_L1): + for j in range(0, image_channel.shape[1], block_size_L1): + block = image_channel[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block + + new_image[:, :, channel] = new_image_channel + + if add_img_grain: + intensity = random.uniform(0, 0.05) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +#CONST +train_SUB_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.18, + shear_range=0.18, + width_shift_range=0.18, + brightness_range=(0.82, 1.18), + height_shift_range=0.18, + channel_shift_range=100, + featurewise_center=True, + featurewise_std_normalization=True, + zca_whitening=False, + interpolation_order=2, + fill_mode='nearest', + preprocessing_function=noise_func_TRLRev2 + ) +class TerminateOnHighTemp(tf.keras.callbacks.Callback): + def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): + super().__init__() + self.active = active + self.check_every_n_batches = check_every_n_batches + self.high_temp = high_temp + self.low_temp = low_temp + self.pause_time = pause_time + self.batch_counter = 0 + + def on_batch_end(self, batch, logs=None): + if not self.active: + return + self.batch_counter += 1 + if self.batch_counter % self.check_every_n_batches == 0: + temperature = gpu_control.get_temperature() + if temperature > self.high_temp: + print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) + time.sleep(self.pause_time) + while gpu_control.get_temperature() > self.low_temp: + time.sleep(4) + print_Color('Resuming training...', ['yellow']) +class ExtendedTensorBoard(TensorBoard): + def on_epoch_end(self, epoch, logs=None): + logs = logs or {} + logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr) + logs['momentum'] = self.model.optimizer.momentum + super().on_epoch_end(epoch, logs) +class DummyCallback(Callback): + pass +steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 +#callbacks>>> +# EarlyStopping +early_stopping = EarlyStopping(monitor='val_accuracy', + patience=EarlyStopping_P, + verbose=1, restore_best_weights=True, + mode='max' + ) if Use_ES_ONSUBT else DummyCallback() +# ModelCheckpoint +checkpoint_SUB = ModelCheckpoint(f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', + monitor=load_SUB_BRW_MODE, + save_best_only=True, mode=ModelCheckpoint_mode, + save_weights_only = True + ) if load_SUB_BRW else DummyCallback() +checkpoint_SUB.best = ModelCheckpoint_Reset_TO +# TerminateOnHighTemp +TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, + check_every_n_batches=6, + high_temp=72, + low_temp=58, + pause_time=60) +# TensorBoard +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +if Use_extended_tensorboard: + tensorboard_callback = ExtendedTensorBoard( + log_dir=log_dir, + write_images=False, # Uses a lot of memory + histogram_freq=1, + update_freq='epoch', + write_grads=True, + profile_batch='256,512' if Use_tensorboard_profiler else 0 + ) +else: + tensorboard_callback = TensorBoard( + log_dir=log_dir, + write_images=False, # Uses a lot of memory + histogram_freq=1, + update_freq='epoch', + write_grads=True, + profile_batch='256,512' if Use_tensorboard_profiler else 0 + ) +# OneCycleLr +if OneCycleLr_UFTS: + learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR, + steps_per_epoch=steps_per_epoch_train_SUB, + epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) +#PRES +# ... +#MAIN +print('Training the model...') +# INFOp +print_Color('\nSetup Verbose:', ['yellow']) +print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +#warnings +P_warning('RES_Train is True.') if RES_Train else None +print_Color('Setup Verbose END.', ['yellow']) +# MAIN LOOP +try: + for epoch in range(1, max_epoch): + # Start Epoch + STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' + C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT + if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1 + start_FULL_time = time.time() + if Auto_clear_cache: + subprocess.run(["Cache_clear.cmd"], shell=True) + # TSEC: Total-Subset-Epoch-Count + print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) + # DP + if not AdvSubsetC: + print_Color('Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + if AdvSubsetC: + if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0: + print_Color('└───Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + chosen_indices = [] # Reset chosen_indices + + available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices)) + + if len(available_indices) < subset_size: + #DEBUG + # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.') + # If there are not enough available indices, choose from the indices that were chosen the longest time ago + old_indices = chosen_indices[:subset_size - len(available_indices)] + subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False)) + + # Update the list of chosen indices and their sizes + chosen_indices = chosen_indices[len(old_indices):] + subset_indices + subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices) + else: + subset_indices = list(np.random.choice(available_indices, subset_size, replace=False)) + + # Add the chosen indices to the list of already chosen indices + chosen_indices += subset_indices + subset_sizes += [subset_size] * len(subset_indices) + else: + subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) + # Taking the subset + x_SUB_train = x_train[subset_indices] + y_SUB_train = y_train[subset_indices] + x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train) + assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}' + print_Color('Preparing train data...', ['yellow']) + # if epoch == 1: # OLD + # print_Color('- ImageDataGenerator fit...', ['yellow']) + # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) + # print_Color('- ImageDataGenerator fit done.', ['yellow']) + if epoch == 1 or ALWAYS_REFIT_IDG == 2: + if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG: + print_Color('- Loading fitted ImageDataGenerator...', ['yellow']) + train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) + else: + print_Color('- Fitting ImageDataGenerator...', ['yellow']) + IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12 + train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) + pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb')) + print_Color('- ImageDataGenerator fit done.', ['yellow']) + + print_Color('- Augmenting Image Data...', ['yellow']) + train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, + y_SUB_train, + shuffle=False, + batch_size=len(x_SUB_train) + ).next() + print_Color('- Normalizing Image Data...', ['yellow']) + x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) + # x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 1) / 255 + x_SUB_train = x_SUB_train / 255 + x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1) + y_SUB_train = train_SUB_augmented_images[1] + # DEBUG + if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1): + SITD = np.random.choice(subset_size, size=400, replace=False) + S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) + save_images_to_dir(x_SUB_train[SITD], y_SUB_train[SITD], S_dir) + # learning_rate_schedule_SUB + if PL_epoch == 0: + CU_LR = MIN_LR + elif epoch >= PL_epoch and CU_LR > MIN_LR: + if (CU_LR - DEC_LR) < MIN_LR: + CU_LR = MIN_LR + else: + CU_LR -= DEC_LR + if not OneCycleLr_UFTS: + learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR, + steps_per_epoch=steps_per_epoch_train_SUB, + epochs=C_subset_epoch) + #FV + print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + "~*~*") if not OneCycleLr_UFTS else "~*OneCycleLr_UFTS Is ON~*"}]~*...', + ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True) + print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + # Train + print_Color('Training on subset...', ['green']) + start_SUBO_time = time.time() + SUB_history = model.fit(x_SUB_train, + y_SUB_train, + epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C) + batch_size=Conf_batch_size_REV2, + validation_data=(x_test, y_test), + verbose='auto', + initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard + callbacks=[ + learning_rate_schedule_SUB, + TerminateOnHighTemp_CB, + checkpoint_SUB, + early_stopping, + tensorboard_callback + ] + ) + end_SUBO_time = time.time() + print_Color('Subset training done.', ['green']) + if load_SUB_BRW_LMODE == 1: + if max(SUB_history.history['val_accuracy']) > best_acc: + load_weights = True + elif min(SUB_history.history['val_loss']) < best_loss: + load_weights = True + else: + load_weights = False + else: + load_weights = True + + if load_SUB_BRW and load_weights: + print_Color('Loading the best weights...', ['yellow']) + # Get the filename of the best weights file + list_of_files = glob.glob('cache\\*.h5') + try: + best_weights_filename = max(list_of_files, key=os.path.getctime) + print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow']) + model.load_weights(best_weights_filename) + except Exception as Err: + print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red']) + elif load_SUB_BRW and (not load_weights): + # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history["val_accuracy"]):.4f}}}, loss{{{min(SUB_history.history["val_loss"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]', + # ['yellow']) # OLD + print_Color_V2(f'Not loading weights[BSR:acc{{{95.675647:.4f}}}, loss{{{0.0111:.4f}}}|BTR:acc{{{97.56456:.4f}}}, loss{{{0.002:.4f}}}]') + all_histories.append(SUB_history.history) + checkpoint_SUB.best = ModelCheckpoint_Reset_TO + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Evaluate the model on the test data + evaluation = model.evaluate(x_test, y_test, verbose=0) + + # Extract the loss and accuracy from the evaluation results + loss = evaluation[0] + acc = evaluation[1] + print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True) + print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True) + # If the accuracy is higher than the best_acc + if acc > best_acc: + print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.') + # Update the best_acc + best_acc = acc + if SAVE_FULLM: + # Save the model + if SAVE_TYPE == 'TF': + print_Color_V2(f'Saving full model tf format...') + model.save(BEST_RSN, save_format='tf') + else: + print_Color_V2(f'Saving full model H5 format...') + model.save(f'{BEST_RSN}.h5') + model.save_weights('PAI_model_weights.h5') + else: + print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.') + + # If the loss is higher than the best_loss + if loss < best_loss: + print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.') + + # Update the best_acc + best_loss = loss + + if SAVE_FULLM: + # Save the model + if SAVE_TYPE == 'TF': + print_Color_V2(f'Saving full model tf format...') + model.save(BEST_RSN + '_BL', save_format='tf') + else: + print_Color_V2(f'Saving full model H5 format...') + model.save(f'{BEST_RSN}_BL.h5') + model.save_weights('PAI_model_weights_BL.h5') + else: + print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Epoch end + end_time = time.time() + epoch_time = end_time - start_FULL_time + print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec') + epoch_SUB_time = end_SUBO_time - start_SUBO_time + print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec') + epoch_OTHERO_time = epoch_time - epoch_SUB_time + print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec') + print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) + Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard +except KeyboardInterrupt: + print('\nKeyboardInterrupt.') +# End +try: + history = {} + for key in all_histories[0].keys(): + # For each metric, concatenate the values from all histories + history[key] = np.concatenate([h[key] for h in all_histories]) +except Exception as Err: + print(f'Failed to make model `history` var.\nERROR: {Err}') + +print('Training done.\n') +# del vars +try: + del train_SUB_datagen + del train_SUB_augmented_images +except NameError: + pass + +# %% [markdown] +# #### Rev1 (⚠️deprecated⚠️) +# ``` +# Working: βœ… +# Other: +# + Tensorboard works. +# - Can cause overfitting. +# ``` + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF +Conf_batch_size = 8 +OneCycleLr_epoch = 20 +Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) +#TensorBoard conf +TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) +# Learning rate configuration +Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr +MAX_LR = 0.0174 +# First time +if Learning_rate_conf == 1: + learning_rate_start = 8e-04 + learning_rate_max = 5e-03 + learning_rate_min = 5e-05 + learning_rate_rampup_epochs = 5 + learning_rate_sustain_epochs = 1 + learning_rate_exp_decay = .3 + #TEMP + # learning_rate_start = 8e-04 + # learning_rate_max = 1e-02 + # learning_rate_min = 8e-04 + # learning_rate_rampup_epochs = 5 + # learning_rate_sustain_epochs = 3 + # learning_rate_exp_decay = .45 +# 2th time +if Learning_rate_conf == 2: + if Learning_rate_conf_SET2C == 1: + learning_rate_start = 4.10e-06 + learning_rate_max = 4.10e-06 + learning_rate_min = 4.10e-06 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 2: + learning_rate_start = 4e-07 + learning_rate_max = 4e-07 + learning_rate_min = 4e-07 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 3: + learning_rate_start = 5e-04 + learning_rate_max = 5e-04 + learning_rate_min = 5e-04 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 +# Function to build learning rate schedule +if Learning_rate_conf in [1,2]: + def build_learning_rate_fn(lr_start=learning_rate_start, + lr_max=learning_rate_max, + lr_min=learning_rate_min, + lr_rampup_epochs=learning_rate_rampup_epochs, + lr_sustain_epochs=learning_rate_sustain_epochs, + lr_exp_decay=learning_rate_exp_decay): + lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync + def learning_rate_fn(epoch): + if epoch < lr_rampup_epochs: + lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start + elif epoch < lr_rampup_epochs + lr_sustain_epochs: + lr = lr_max + else: + lr = (lr_max - lr_min) *\ + lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min + return lr + return learning_rate_fn + +# Calculate steps per epoch +steps_per_epoch_train = len(x_train) // Conf_batch_size + +# Set up callbacks +class EpochEndMON(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs=None): + optimizer = self.model.optimizer + if hasattr(optimizer, 'lr'): + lr = tf.keras.backend.get_value(optimizer.lr) + print(f'\nLearning rate for epoch {epoch+1} is {lr}') + if hasattr(optimizer, 'momentum'): + momentum = tf.keras.backend.get_value(optimizer.momentum) + print(f'Momentum for epoch {epoch+1} is {momentum}') + if logs: + val_loss = logs.get('val_loss') + val_acc = logs.get('val_accuracy') + print(f'Validation loss for epoch {epoch+1} is {val_loss}') + print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') + + print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') + +# Instantiate the callback +EpochEndMON_callback = EpochEndMON() +if Learning_rate_conf in [1,2]: + learning_rate_fn = build_learning_rate_fn() + learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) +else: + learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch) +if SAVE_TYPE == 'TF': + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +else: + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' +tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True) + +# Train the model +print('Log dir:', log_dir) +#MInfo +print('Input Shape:', model.input_shape) +print('Output Shape:', model.output_shape) +print('Loss Function:', model.loss) +print('Training the model...\n') +history = model.fit(x_train, + y_train, + epochs=256, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) +print('Training done.\n') + +# %% [markdown] +# ## Saving model weights +# + +# %% +Extra_EXT = '_T' +# Save the weights +print('Saving weights...') +model.save_weights('PAI_model_weights.h5') +print('Saving full model...') +if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') +else: + try: + model.save(f'PAI_model{Extra_EXT}.h5') + except ValueError: + print('failed to save in .h5 format!') + print('Saving full model in tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') + +# %% [markdown] +# ## Garbage Collection (memory) + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() + +# %% [markdown] +# ## Analyse model Training performance + +# %% +# Save history +save_list(history, 'history\\model_history.pkl.gz', compress=True) + +# %% +# load history +history = load_list('history\\model_history.pkl.gz', compressed=True) + +# %% +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns + +# Chunk size for 3D plot +chunk_size = 6 # Change this to your desired chunk size + +def convert_history(history): + if isinstance(history, tf.keras.callbacks.History): + return history.history + else: + return history + +def chunked_data(data, chunk_size): + return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)] + + +try: + EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' + history = convert_history(history) + + # Calculate deltas + delta_loss = np.diff(history['loss']) + delta_accuracy = np.diff(history['accuracy']) + + try: + delta_val_loss = np.diff(history['val_loss']) + delta_val_accuracy = np.diff(history['val_accuracy']) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') + + plt.figure(figsize=(16, 10)) + # Loss + plt.subplot(2, 2, 1) + plt.plot(history['loss'], label='loss') + try: + plt.plot(history['val_loss'], label='val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_loss.') + plt.title('Model Loss') + plt.ylabel('Loss') + plt.xlabel(EPM) + plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + # Density plot for loss + plt.subplot(2, 2, 2) + plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) + try: + plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss (density plot).') + plt.title('Density Plot for Loss') + plt.xlabel('Loss') + plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + + # Accuracy + plt.subplot(2, 2, 3) + plt.plot(history['accuracy'], label='accuracy') + try: + plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy.') + plt.title('Model Accuracy') + plt.ylabel('Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Density plot for accuracy + plt.subplot(2, 2, 4) + plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) + try: + plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy (density plot).') + plt.title('Density Plot for Accuracy') + plt.xlabel('Accuracy') + plt.grid(True) + + # Delta Loss + plt.figure(figsize=(14, 8)) + plt.subplot(2, 2, 1) + plt.plot(delta_loss, label='delta_loss') + try: + plt.plot(delta_val_loss, label='delta_val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_loss.') + plt.title('Delta Model Loss') + plt.ylabel('Delta Loss') + plt.ylim(top=1.5, bottom=-1.5) + plt.xlabel(EPM) + plt.grid(True) + # Delta Accuracy + plt.subplot(2, 2, 2) + plt.plot(delta_accuracy, label='delta_accuracy') + try: + plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_accuracy.') + plt.title('Delta Model Accuracy') + plt.ylabel('Delta Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Calculate chunked data + chunked_loss = chunked_data(history['val_loss'], chunk_size) + chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) + + # Clip the loss values to a maximum of max(history['val_loss'][10:]) + max_loss = max(history['val_loss'][10:]) + chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) + + # Create 3D surface plots for each chunk + fig = plt.figure(figsize=(14, 8)) + ax = fig.add_subplot(121, projection='3d') + X = np.arange(len(chunked_loss)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Loss') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Loss') + + ax = fig.add_subplot(122, projection='3d') + X = np.arange(len(chunked_accuracy)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Accuracy') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Accuracy') + + # Function to calculate the average of chunks + def chunked_average(values, chunk_size): + return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] + + avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) + avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) + + # Find the chunk with the highest average accuracy + max_acc_chunk_index = np.argmax(avg_accuracy_chunks) + max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] + + # Create a pile plot for accuracy + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy') + plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') + plt.xlabel('Chunk') + plt.ylabel('Average Accuracy') + plt.title('Average Validation Accuracy per Chunk') + plt.legend() + + # Create a pile plot for loss + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') + plt.xlabel('Chunk') + plt.ylabel('Average Loss') + plt.title('Average Validation Loss per Chunk') + plt.legend() + + # Function to calculate the average of each epoch across chunks, ignoring the first chunk + def average_across_chunks(values, chunk_size): + num_chunks = len(values) // chunk_size + avg_values = [] + for epoch in range(chunk_size): + epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] + avg_values.append(np.mean(epoch_values)) + return avg_values + + # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk + avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) + avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) + + # Create a bar plot for average accuracy and loss of each epoch across chunks + plt.figure(figsize=(12, 6)) + + # Create an index for each epoch + epoch_indices = np.arange(len(avg_accuracy_epochs)) + + # Plot accuracy and loss as bars + plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) + plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) + + # Add labels and title + plt.xlabel('Epoch (within chunk)') + plt.ylabel('Average Value') + plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') + plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers + plt.legend() + + plt.tight_layout() + plt.show() + +except (ValueError, NameError) as E: + print(f'\033[91mFailed to load model history.\nError: {E}') + +# %% [markdown] +# ## Analyse model Predicting performance + +# %% [markdown] +# ### Gradcam heatmap + +# %% [markdown] +# #### V2 + +# %% +def compute_heatmap(model, img_array, conv_layer_name, pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + + # Apply threshold and adjust sensitivity + heatmap = np.where(heatmap > threshold, heatmap, 0) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + + # Apply threshold and adjust sensitivity + heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + + return heatmap + +# %% [markdown] +# #### V3 + +# %% [markdown] +# ### Main test + +# %% +import seaborn as sns +from sklearn.metrics import confusion_matrix, accuracy_score +from scipy.stats import binom +from tqdm import tqdm +import efficientnet.tfkeras +import cv2 +import gc +# Garbage Collection (memory) +gc.collect() + +Extra_EXT = '_T' # _T or _T_BL +prob_L = 0.9995 +tick_spacing = 5 +Train_data_test = False +if SAVE_TYPE == 'TF': + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}') +else: + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}.h5') + +# Ensure the model's input_shape matches your data +assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' + +# Make predictions on validation data +val_predictions = model.predict(x_val) +val_predictions = np.argmax(val_predictions, axis=1) + +# Make predictions on Train data +if Train_data_test: + Train_predictions = model.predict(x_train) + Train_predictions = np.argmax(Train_predictions, axis=1) + +# Make predictions on test data +test_predictions = model.predict(x_test) +test_predictions = np.argmax(test_predictions, axis=1) + +# Convert y_val and y_test from one-hot encoder to their original form +y_val_original = np.argmax(y_val, axis=1) +y_test_original = np.argmax(y_test, axis=1) +if Train_data_test: + y_train_original = np.argmax(y_train, axis=1) + +# Calculate accuracy on validation data +val_accuracy = accuracy_score(y_val_original, val_predictions) + +# Calculate accuracy on Train data +if Train_data_test: + Train_accuracy = accuracy_score(y_val_original, Train_predictions) + +# Calculate accuracy on test data +test_accuracy = accuracy_score(y_test_original, test_predictions) + +# Print acc +if Train_data_test: + print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}') +print(f'The accuracy of the model on validation data is {val_accuracy:.2%}') +print(f'The accuracy of the model on test data is {test_accuracy:.2%}') + +# Visualize the predictions on validation data as a grid of squares +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + plt.imshow(x_val[i]) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() +#Heatmap +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + img = x_val[i] + heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) + heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) + heatmap = np.uint8(255 * heatmap) + # Apply Adaptive Histogram Equalization + clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) # Create CLAHE object + # heatmap = clahe.apply(heatmap) + heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + if RANGE_NOM: + superimposed_img = (heatmap / 255) * 0.7 + img + else: + superimposed_img = (heatmap / 255) * 0.5 + (img / 255) + #clip + superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] + plt.imshow(superimposed_img) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() + +# Define the list of labels +labels = ['NORMAL', 'PNEUMONIA'] + +# Create a confusion matrix for validation data +val_cm = confusion_matrix(y_val_original, val_predictions) + +# Create a confusion matrix for test data +test_cm = confusion_matrix(y_test_original, test_predictions) + +# Plot the confusion matrix as a heatmap for validation data +plt.figure(figsize=(8, 6)) +sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Validation Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Plot the confusion matrix as a heatmap for test data +plt.figure(figsize=(8, 6)) +sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Test Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Define the range of test data sizes to use +data_sizes = range(1, len(x_test), 4) +# Calculate the probability of a wrong prediction based on test accuracy +prob_wrong = 1 - test_accuracy + +# Create a list to store the number of incorrect predictions for each test data size +incorrect_predictions = [] + +# Generate predictions and track incorrect predictions for each data size +for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): + # Garbage Collection (memory) + gc.collect() + # Randomly select a subset of test data + indices = np.random.choice(len(x_test), size, replace=False) + x_test_subset = x_test[indices] + y_test_subset = y_test[indices] + + # Make predictions on the subset of test data + test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) + test_predictions = np.argmax(test_predictions, axis=1) + y_test_original_subset = np.argmax(y_test_subset, axis=1) + + # Calculate the number of incorrect predictions + incorrect_preds = np.sum(test_predictions != y_test_original_subset) + incorrect_predictions.append(incorrect_preds) + +# Plot the number of incorrect predictions vs. the number of data points +plt.figure(figsize=(10, 6)) +plt.plot(data_sizes, incorrect_predictions) +plt.xlabel('Number of Data Points') +plt.ylabel('Number of Incorrect Predictions') +# Add gridlines for the x and y axes +plt.grid(True) + +# Change the tick spacing for the x and y axes +plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) +plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) + +plt.title('Number of Incorrect Predictions vs. Number of Data Points') +plt.show() + +# Define the range of test data sizes to use +data_sizes = range(1, len(x_test), 1) + +# Calculate the probability of a wrong prediction based on test accuracy +prob_wrong = 1 - test_accuracy + +# Create a list to store the probability of getting at least one wrong answer for each test data size +probabilities = [] + +# Calculate the probability of getting at least one wrong answer for each data size +for size in data_sizes: + # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 + cdf = binom.cdf(0, size, prob_wrong) + # Subtract the CDF from 1 to get the probability of getting at least one wrong answer + prob = 1 - cdf + probabilities.append(prob) + +# Find the index of the first data point that has a probability greater than prob_L% +index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) + +# Limit the x-axis to the first data point that has a probability greater than prob_L% +data_sizes = data_sizes[:index+1] +probabilities = probabilities[:index+1] + +# Plot the probability vs. the number of data points +plt.figure(figsize=(10, 6)) +plt.plot(data_sizes, probabilities) +plt.xlabel('Number of Data Points') +plt.ylabel('Probability') + +# Add gridlines for the x and y axes +plt.grid(True) + +# Change the tick spacing for the x and y axes +plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 10)) +plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100)) + +plt.ylim(top=1.01) + +plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') +plt.show() + + diff --git a/Exports/V7/Python_EPO.py b/Exports/V7/Python_EPO.py index 0b4c7f3..5650a79 100644 --- a/Exports/V7/Python_EPO.py +++ b/Exports/V7/Python_EPO.py @@ -1,2490 +1,2490 @@ -# %% [markdown] -# # keras/TF model -# 
-#  Copyright (c) 2023 Aydin Hamedi
-#  
-#  This software is released under the MIT License.
-#  https://opensource.org/licenses/MIT
-#
- -# %% [markdown] -# ## Pre Conf - -# %% -CPU_only = False # True to Force TF to use the cpu - -# %% [markdown] -# ## Pylibs - -# %% -import io -import os -import sys -import time -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -if CPU_only: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' -import cv2 -import glob -import keras -import pprint -import random -import shutil -import gzip -import glob -import pickle -import datetime -import subprocess -import gpu_control -import numpy as np -import pandas as pd -from tqdm import tqdm -import seaborn as sns -from hyperas import optim -# import tensorflow_addons as tfa -from keras_adabound import AdaBound -from importlib import reload -from sklearn.metrics import confusion_matrix -from keras.losses import categorical_crossentropy -import tensorflow as tf -from keras.models import Model -from scipy.ndimage import zoom -import matplotlib.pyplot as plt -from model_profiler import model_profiler -from keras_gradient_noise import add_gradient_noise -from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax -# from tensorflow_addons.optimizers import Yogi -from adabelief_tf import AdaBeliefOptimizer -from sklearn.preprocessing import LabelEncoder -from imblearn.over_sampling import SMOTE -from keras.regularizers import l2 -from keras.models import load_model -from matplotlib import pyplot as plt -from PIL import Image, ImageDraw, ImageFont -from keras import Sequential -from random import randint, choice, shuffle -from keras.callbacks import EarlyStopping -from keras.callbacks import TensorBoard, LambdaCallback -import tensorflow_model_optimization as tfmot -from keras.utils import to_categorical -from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler -from sklearn.model_selection import train_test_split -from keras.preprocessing.image import ImageDataGenerator -from keras.layers import Conv2D,\ - MaxPooling2D,\ - Flatten,\ - Dense,\ - Dropout,\ - BatchNormalization,\ - SeparableConv2D,\ - Input, Concatenate,\ - GlobalAveragePooling2D,\ - CuDNNLSTM, concatenate,\ - Reshape, Multiply, \ - Conv1D, MaxPooling1D -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.Grad_cam import make_gradcam_heatmap -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from Utils.Other import * -# Other -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - -# %% [markdown] -# ## Conf -# - -# %% [markdown] -# ### Data processing conf - -# %% -# Directory paths# Directory paths for training, test and validation image data -train_dir = 'Database\\Train\\Data\\train' -test_dir = 'Database\\Train\\Data\\test' -validation_dir = 'Database\\Train\\Data\\val' -img_res = [224, 224, 3] -# img_res = [324, 324, 3] -# img_res = [224, 224, 3] -# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) -interpolation_order_IFG = 2 -categorical_IMP = True -Make_EV_DATA = False -R_fill_mode = True -add_img_grain = True -Save_TS = True -Use_SMOTE = False # (⚠️Beta⚠️) -ADBD = 0 -OP_HDC = False -SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 -LNTS = 0 -Debug_OUT = False -adjust_brightness_Mode = True -RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️) -scale_data_NP_M = False # (⚠️deprecated⚠️) - -# %% [markdown] -# ### Training - -# %% -SAVE_TYPE = 'H5' -Use_mixed_float16 = False -#Other -if Use_mixed_float16: - tf.keras.mixed_precision.set_global_policy('mixed_float16') -else: - tf.keras.mixed_precision.set_global_policy('float32') - -print(tf.keras.mixed_precision.global_policy()) - -# %% [markdown] -# ## data processing -# - -# %% -#Z_SCORE_normalize -def Z_SCORE_normalize(arr): - arr = arr.astype('float32') - mean = np.mean(arr) - std_dev = np.std(arr) - arr = (arr - mean) / std_dev - return arr -#normalize_TO_RANGE -def normalize_TO_RANGE(arr, min_val, max_val): - arr = arr.astype('float32') - arr = (arr - arr.min()) / (arr.max() - arr.min()) - arr = arr * (max_val - min_val) + min_val - return arr -#scale_data -def scale_data_NP(data): - if scale_data_NP_M: - data = data.astype('float32') - data = (data - 127.5) / 127.5 - return data - else: - return data / 255 -#add_image_grain -def add_image_grain(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -#apply_clahe_rgb_array -def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): - # Create a CLAHE object - clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) - - # Iterate over each image in the array - for i in range(len(images)): - # Split the image into color channels - b, g, r = cv2.split(images[i]) - - # Convert the channels to the appropriate format - b = cv2.convertScaleAbs(b) - g = cv2.convertScaleAbs(g) - r = cv2.convertScaleAbs(r) - - # Apply adaptive histogram equalization to each channel - equalized_b = clahe.apply(b) - equalized_g = clahe.apply(g) - equalized_r = clahe.apply(r) - - # Merge the equalized channels back into an image - equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) - - # Replace the original image with the equalized image in the array - images[i] = equalized_image - - return images -#noise_func -def noise_func(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.05, 0.05) - intensityL1 = random.uniform(-0.04, 0.04) - else: - intensityL2 = random.uniform(-0.06, 0.06) - intensityL1 = random.uniform(-0.04, 0.04) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026 - new_image = add_image_grain(new_image, intensity=intensity) - return new_image -#shuffle_data -def shuffle_data(x, y): - indices = np.arange(x.shape[0]) - np.random.shuffle(indices) - x = x[indices] - y = y[indices] - return x, y -#save_images_to_dir -def save_images_to_dir(images, labels, dir_path): - # create the directory if it doesn't exist - if not os.path.exists(dir_path): - os.makedirs(dir_path) - # iterate over the images and labels - for i, (image, label) in enumerate(zip(images, labels)): - # get the class label - class_label = np.argmax(label) - # create the file path - file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') - # save the image to the file path - plt.imsave(file_path, image.squeeze()) - # compress the directory - shutil.make_archive(dir_path, 'gztar', dir_path) - # remove the original directory - shutil.rmtree(dir_path) -#Debug_img_Save -def Debug_img_Save(img, id = 'DEF'): - SITD = np.random.choice(img.shape[0], size=400, replace=False) - S_dir = f'Samples\\Debug\\{id}\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) - save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir) -# Create an ImageDataGenerator for the training set -if OP_HDC: - print_Color('Using OP_HDC IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.86, 1.1), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=interpolation_order_IFG, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -else: - print_Color('Using Def IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.26, - shear_range=0.25, - width_shift_range=0.25, - brightness_range=(0.78, 1.1), - height_shift_range=0.25, - channel_shift_range=100, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -train_datagen_SM = ImageDataGenerator( - horizontal_flip=False, - vertical_flip=False, - rotation_range=20, - zoom_range=0.07, - shear_range=0.07, - width_shift_range=0.07, - brightness_range=(0.99, 1.01), - height_shift_range=0.07, - channel_shift_range=0, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False -) -# Create an iterator for the training set -train_generator_SM = train_datagen_SM.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') -# Create an ImageDataGenerator for the validation set (OP) -if Make_EV_DATA: - val_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the validation set - val_generator = val_datagen.flow_from_directory( - validation_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), - class_mode='binary', - color_mode='rgb') - - # Create an ImageDataGenerator for the test set - test_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the test set - test_generator = test_datagen.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), - class_mode='binary', - color_mode='rgb') -# Load all images and labels into memory -print_Color('Loading all images and labels into memory...', ['yellow']) -x_train, y_train = next(iter(train_generator_SM)) -if Make_EV_DATA: - x_val, y_val = next(iter(val_generator)) - x_test, y_test = next(iter(test_generator)) -if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG -# fit parameters from data -# train_datagen.fit(x_train) -#to_categorical (TEMP) -if categorical_IMP: - print_Color('Making categorical data...', ['yellow']) - y_train = to_categorical(y_train, num_classes=2) - if Make_EV_DATA: - y_val = to_categorical(y_val, num_classes=2) - y_test = to_categorical(y_test, num_classes=2) -# Use_SMOTE -if Use_SMOTE: - print_Color('SMOTE...', ['yellow']) - # Convert y_train from one-hot encoding to label encoding - y_train_label_encoded = np.argmax(y_train, axis=1) - - # Print the original label distribution - unique, counts = np.unique(y_train_label_encoded, return_counts=True) - print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) - - # Use SMOTE to oversample the minority class - smote = SMOTE(random_state=42) - x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) - - # Print the resampled label distribution - unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) - print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) - - # Reshape x_train_res back to the original x_train shape - x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) - - # Convert y_train_res from label encoding back to one-hot encoding - y_train_res = to_categorical(y_train_res_label_encoded) - - # Calculate the ratio of two labels after resampling - pneumonia_count = np.sum(y_train_res[:, 1]) - total_count = y_train_res.shape[0] - label_ratio_res = pneumonia_count / total_count - label_ratio_percentage_res = label_ratio_res * 100 - - # Replace the original data with the resampled data - x_train = x_train_res - y_train = y_train_res - - # Delete the resampled data to free up memory - del x_train_res, y_train_res_label_encoded, y_train_res -# Generating augmented data -print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', - ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], - advanced_mode=True) -if ADBD > 0: - for i in range(ADBD): - # ADB_clip_limit Scheduler>>> - if i == 0: - ADB_clip_limit = 0.8 - else: - #V1>>> - CL_SLM = 2.4 - ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) - # Try it in win graphing calculator copy and paste: - # β”Œ-------------┬--┬---------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ - # β””-------------β”΄--β”΄---------------β”˜ - #V2>>> - # CL_SLM_2 = 1.4 - # CL_SLM_Start_2 = 2 - # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) - # Try it in win graphing calculator copy and paste: - # β”Œ-----------------┬--┬-------------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ - # β””-----------------β”΄--β”΄-------------------β”˜ - print(f'> Generating ADB[{i+1}/{ADBD}]...') - # prepare an iterators to scale images - train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) - - # get augmented data - x_train_augmented, y_train_augmented = train_iterator.next() - print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') - print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') - x_train_augmented = np.clip(x_train_augmented, 0, 255) - if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG - #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) - x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss - print(f'> └───Adding the Generated ADB...') - if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG - # append augmented data to original data - x_train = np.concatenate([x_train, x_train_augmented]) - y_train = np.concatenate([y_train, y_train_augmented]) - #free up memory - del y_train_augmented - del x_train_augmented -# normalizing -print_Color('Normalizing image data...', ['yellow']) -if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG -x_train = np.clip(x_train, 0, 255) -if RANGE_NOM: - x_train = scale_data_NP(x_train) -y_train = np.array(y_train) -if Make_EV_DATA: - x_test = np.clip(x_test, 0, 255) - x_val = np.clip(x_val, 0, 255) - if RANGE_NOM: - x_val = scale_data_NP(x_val) - y_val = np.array(y_val) - if RANGE_NOM: - x_test = scale_data_NP(x_test) - y_test = np.array(y_test) -if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG -# Check the data type of image data -print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) -# Check the range of image data -print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) -# Calculate the ratio of two labels -if categorical_IMP: - label_sums = np.sum(y_train, axis=0) - label_ratio = label_sums / (np.sum(y_train) + 1e-10) - label_ratio_percentage = label_ratio * 100 - print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', - ['normal', 'red', 'magenta', 'green'], advanced_mode=True) -print_Color('Setting LNTS...', ['yellow']) -# Get the total number of samples in the arrays -num_samples = x_train.shape[0] -print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -if LNTS != 0: - print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) - print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) - # Generate random indices to select LNTS samples - indices = np.random.choice(num_samples, size=LNTS, replace=False) - # Select the samples using the generated indices - x_selected = x_train[indices] - y_selected = y_train[indices] - x_train = x_selected - y_train = y_selected - #free up memory - del x_selected - del y_selected - del indices - #Debug - num_samples = x_train.shape[0] - print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -# Shuffle the training data -print_Color('shuffling data...', ['yellow']) -x_train, y_train = shuffle_data(x_train, y_train) -#save_images_to_dir -if Save_TS: - print_Color('Saving TS...', ['yellow']) - SITD = np.random.choice(num_samples, size=400, replace=False) - S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) - if RANGE_NOM: - if scale_data_NP_M: - save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) -print_Color('Done.', ['green']) - -# %% [markdown] -# ## Save EV Dataset - -# %% -np.save(f'Database\\Test\\Data\\x_val{SL_EX}.npy', x_val) -np.save(f'Database\\Test\\Data\\y_val{SL_EX}.npy', y_val) -np.save(f'Database\\Test\\Data\\x_test{SL_EX}.npy', x_test) -np.save(f'Database\\Test\\Data\\y_test{SL_EX}.npy', y_test) - -# %% [markdown] -# ## Load EV Dataset - -# %% -x_val = np.load(f'Database\\Test\\Data\\x_val{SL_EX}.npy') -y_val = np.load(f'Database\\Test\\Data\\y_val{SL_EX}.npy') -x_test = np.load(f'Database\\Test\\Data\\x_test{SL_EX}.npy') -y_test = np.load(f'Database\\Test\\Data\\y_test{SL_EX}.npy') - -# %% [markdown] -# ## Data Analyzation - -# %% -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -import seaborn as sns -from scipy.stats import zscore - -# Select a subset of your data -subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels -subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values -indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False) -indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False) -subset_pixels = x_train[indices_pixels] -subset_mean = x_train[indices_mean] - -# Reshape the data for calculating Z-scores -reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1]) -reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1]) - -# Calculate the mean intensity -mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1) -mean_intensity_mean = reshaped_data_mean.mean(axis=-1) - -# Stack the mean intensity with the reshaped data -data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)]) -data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)]) - -# Calculate Z-scores -z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0)) -z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0)) - -# Identify outliers -outliers_pixels = np.where(z_scores_pixels > 3) -outliers_mean = np.where(z_scores_mean > 3) - -# Create a 3D scatter plot for RGB channels -fig = plt.figure(figsize=(10, 20)) - -# Plot for individual pixels -ax = fig.add_subplot(211, projection='3d') -ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1) -ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red') -ax.set_title('Z-Score Scatter Plot for Individual Pixels') -ax.set_xlabel('Red') -ax.set_ylabel('Green') -ax.set_zlabel('Blue') - -# Plot for mean RGB values -ax = fig.add_subplot(212, projection='3d') -ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1) -ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red') -ax.set_title('Z-Score Scatter Plot for Mean RGB Values') -ax.set_xlabel('Red') -ax.set_ylabel('Green') -ax.set_zlabel('Blue') - -# Density plot of the mean intensity -plt.figure(figsize=(10, 5)) -sns.kdeplot(data=z_scores_pixels[:, -1], fill=True) -plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels') -plt.xlabel('Z-Score') - -sns.kdeplot(data=z_scores_mean[:, -1], fill=True) -plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values') -plt.xlabel('Z-Score') - -# Display the plot -plt.show() - -# %% [markdown] -# ## Creating the model -# - -# %% [markdown] -# ### Rev1 -# ``` -# recommended: ⚠️ -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: β‰…95.1 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(EfficientNetB7) -# ``` - -# %% -from keras.applications import EfficientNetB7 - -EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.1 -# ``` -# recommended: ❌ -# statuses: S.Ready (can improve) -# Working: ❌ -# Max fine tuned acc: β‰…93.2 -# Max fine tuned acc TLRev2: N/A -# type: transfer learning>>>(ConvNeXtLarge) -# ``` - -# %% -from keras.applications import ConvNeXtLarge - -ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False) -# define new model -model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -model.summary() - - -# %% [markdown] -# ### Rev1.2 -# ``` -# recommended: βœ… -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: 95.3 -# Max fine tuned acc TLRev2: 97.12 -# type: transfer learning>>>(EfficientNetB7::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -# FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENB7(input_shape=( - img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / - len(base_model.layers)) * 100 - print( - f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL>>> - #GlobalAveragePooling2D - base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) - #Dense - Dense_L1 = Dense(512, activation='relu', - kernel_regularizer=l2(0.02), - name='FC_C_Dense-L1-512' - )(base_model_FT) - #Dropout - Dropout_L1 = Dropout(0.1, - name='FC_C_Dropout-L1-0.1' - )(Dense_L1) - #BatchNormalization - BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' - )(Dropout_L1) - #Dense - Dense_L2 = Dense(512, activation='relu', - kernel_regularizer=l2(0.01), - name='FC_C_Dense-L2-512' - )(BatchNorm_L2) - #BatchNormalization - BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' - )(Dense_L2) - #Dense - Dense_L3 = Dense(128, activation='relu', - name='FC_C_Dense-L3-128' - )(BatchNorm_L3) - #Dense - # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) - predictions = Dense(2, activation='softmax', - name='FC_OUTPUT_Dense-2')(Dense_L3) - # CDL<<< - model_EfficientNetB7_NS = Model( - inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - # OPT/compile - opt = SGD(momentum=0.9, nesterov=False) - # opt = Nadam() - # opt = Adamax() - # opt = RMSprop(momentum=0.9) - # opt = Adagrad() - # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.3 -# ``` -# recommended: ❌ -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: ⚠️ -# type: transfer learning>>>(EfficientNetB7|Xception::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -from keras.applications.xception import Xception - -#FUNC -def Combo_Model(freeze_layers1, freeze_layers2): - # Define a common input - common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) - - # Base model 1 - base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) - base_model1_out = base_model1(common_input) - - # Base model 2 - base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False) - # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) - base_model2_out = base_model2(common_input) - - print('Total base_model1 layers: ', len(base_model1.layers)) - print('Total base_model2 layers: ', len(base_model2.layers)) - - # Freeze the specified number of layers in both models - for layer in base_model1.layers[:freeze_layers1]: - layer.trainable = False - for layer in base_model2.layers[:freeze_layers2]: - layer.trainable = False - - # Unfreeze the rest in both models - for layer in base_model1.layers[freeze_layers1:]: - layer.trainable = True - for layer in base_model2.layers[freeze_layers2:]: - layer.trainable = True - - # Combine the output of the two base models - combined = concatenate([Dense(512, - activation='relu', - kernel_regularizer=l2(0.02) - )(GlobalAveragePooling2D()(base_model1_out)), - Dense(512, - activation='relu', - kernel_regularizer=l2(0.02) - )(GlobalAveragePooling2D()(base_model2_out))]) - - # adding CDL - Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined) - Dropout_L1 = Dropout(0.4)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - combo_model = Model(inputs=common_input, outputs=predictions) - print('Total model layers: ', len(combo_model.layers)) - - #OPT/compile - opt = SGD(momentum=0.9) - combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return combo_model - -print('Creating the model...') -# Main -freeze_layers_1 = 0 -freeze_layers_2 = 0 -model = Combo_Model(freeze_layers_1, freeze_layers_2) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.4 -# ``` -# recommended: ⚠️ -# statuses: Test -# Working: βœ… -# Max fine tuned acc: ⚠️ -# Max fine tuned acc TLRev2: β‰…95.64 -# type: transfer learning>>>(EfficientNetV2XL) -# ``` - -# %% -from keras_efficientnet_v2 import EfficientNetV2XL - -EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4) -# define new model -model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) - -# compile model -opt = SGD(momentum=0.9) -# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False) -# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -# opt = Adam() -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - -freeze_layers = 0 -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### Rev1.5 (The best one) -# ``` -# recommended: βœ… -# statuses: Ready -# Working: βœ… -# Max fine tuned acc: 95.3 -# Max fine tuned acc TLRev2: 97.12 -# type: transfer learning>>>(EfficientNetB4::CCL) -# ``` - -# %% -from efficientnet.keras import EfficientNetB4 as KENB4 -# FUNC -def Eff_B4_NS(freeze_layers): - base_model = KENB4(input_shape=( - img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / - len(base_model.layers)) * 100 - print( - f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL>>> - #GlobalAveragePooling2D - base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) - #Dense - Dense_L1 = Dense(512, activation='relu', - kernel_regularizer=l2(0.02), - name='FC_C_Dense-L1-512' - )(base_model_FT) - #Dropout - Dropout_L1 = Dropout(0.1, - name='FC_C_Dropout-L1-0.1' - )(Dense_L1) - #BatchNormalization - BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' - )(Dropout_L1) - #Dense - Dense_L2 = Dense(512, activation='relu', - kernel_regularizer=l2(0.01), - name='FC_C_Dense-L2-512' - )(BatchNorm_L2) - #BatchNormalization - BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' - )(Dense_L2) - #Dense - Dense_L3 = Dense(128, activation='relu', - name='FC_C_Dense-L3-128' - )(BatchNorm_L3) - #Dense - # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) - predictions = Dense(2, activation='softmax', - name='FC_OUTPUT_Dense-2')(Dense_L3) - # CDL<<< - model_EfficientNetB7_NS = Model( - inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - # OPT/compile - opt = SGD(momentum=0.9, nesterov=False) - # opt = Nadam() - # opt = Adamax() - # opt = RMSprop(momentum=0.9) - # opt = Adagrad() - # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B4_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta - -# %% -from efficientnet.keras import EfficientNetL2 as KENBL2 -#FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]), - weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5', - include_top=False, - drop_connect_rate=0) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 - print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model.output) - Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta2 - -# %% -from efficientnet.keras import EfficientNetB7 as KENB7 -# FUNC -def Eff_B7_NS(freeze_layers): - base_model = KENB7(input_shape=( - img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / - len(base_model.layers)) * 100 - print( - f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL>>> - #GlobalAveragePooling2D - base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) - #Dense - Dense_L1 = Dense(512, activation='relu', - kernel_regularizer=l2(0.02), - name='FC_C_Dense-L1-512' - )(base_model_FT) - #Dropout - Dropout_L1 = Dropout(0.1, - name='FC_C_Dropout-L1-0.1' - )(Dense_L1) - #BatchNormalization - BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-Pooling-L1' - )(Dropout_L1) - #Dense - Dense_L2 = Dense(512, activation='relu', - kernel_regularizer=l2(0.01), - name='FC_C_Dense-L2-512' - )(BatchNorm_L2) - #BatchNormalization - BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-Pooling-L2' - )(Dense_L2) - #Dense - Dense_L3 = Dense(128, activation='relu', - name='FC_C_Dense-L3-128' - )(BatchNorm_L3) - #Dense - # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) - predictions = Dense(2, activation='softmax', - name='FC_OUTPUT_Dense-2')(Dense_L3) - # CDL<<< - model_EfficientNetB7_NS = Model( - inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - # OPT/compile - opt = SGD(momentum=0.9, nesterov=False) - # opt = Nadam() - # opt = Adamax() - # opt = RMSprop(momentum=0.9) - # opt = Adagrad() - # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B7_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta3 - -# %% -from keras.applications import ConvNeXtXLarge -from keras.layers import Lambda -#FUNC -def Eff_B7_NS(): - # Add a Lambda layer at the beginning to scale the input - input = Input(shape=(img_res[0], img_res[1], img_res[2])) - x = Lambda(lambda image: image * 255)(input) - - base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x) - # adding CDL - base_model_FT = GlobalAveragePooling2D()(base_model) - Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) - Dropout_L1 = Dropout(0.1)(Dense_L1) - BatchNorm_L2 = BatchNormalization()(Dropout_L1) - Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) - BatchNorm_L3 = BatchNormalization()(Dense_L2) - Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) - predictions = Dense(2, activation='softmax')(Dense_L3) - - model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - #OPT/compile - opt = SGD(momentum=0.9) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -model = Eff_B7_NS() -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### V(T) Beta4 - -# %% -from efficientnet.keras import EfficientNetB4 as KENB4 -# FUNC -def Eff_B4_NS(freeze_layers): - base_model = KENB4(input_shape=( - img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) - print('Total layers in the base model: ', len(base_model.layers)) - print(f'Freezing {freeze_layers} layers in the base model...') - # Freeze the specified number of layers - for layer in base_model.layers[:freeze_layers]: - layer.trainable = False - - # Unfreeze the rest - for layer in base_model.layers[freeze_layers:]: - layer.trainable = True - - # Calculate the percentage of the model that is frozen - frozen_percentage = ((freeze_layers + 1e-10) / - len(base_model.layers)) * 100 - print( - f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') - # adding CDL>>> - #GlobalAveragePooling2D - base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) - #Dense - Dense_L1 = Dense(512, activation='relu', - kernel_regularizer=l2(0.02), - name='FC_C_Dense-L1-512' - )(base_model_FT) - #Dropout - Dropout_L1 = Dropout(0.1, - name='FC_C_Dropout-L1-0.1' - )(Dense_L1) - #BatchNormalization - BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' - )(Dropout_L1) - #Dense - Dense_L2 = Dense(512, activation='relu', - kernel_regularizer=l2(0.01), - name='FC_C_Dense-L2-512' - )(BatchNorm_L2) - #BatchNormalization - BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' - )(Dense_L2) - #Dense - Dense_L3 = Dense(128, activation='relu', - name='FC_C_Dense-L3-128' - )(BatchNorm_L3) - #Dense - # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) - predictions = Dense(2, activation='softmax', - name='FC_OUTPUT_Dense-2')(Dense_L3) - # CDL<<< - model_EfficientNetB7_NS = Model( - inputs=base_model.input, outputs=predictions) - print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) - # OPT/compile - opt = SGD(momentum=0.9, nesterov=False) - # opt = Nadam() - # opt = Adamax() - # opt = RMSprop(momentum=0.9) - # opt = Adagrad() - # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) - # opt = Yogi() - model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy - - return model_EfficientNetB7_NS - -print('Creating the model...') -# Main -freeze_layers = 0 -model = Eff_B4_NS(freeze_layers) -model.summary(show_trainable=True, expand_nested=True) -print('done.') - -# %% [markdown] -# ### LR FINDER - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF/Other -LRF_OPT = SGD(momentum=0.9) -LFR_batch_size = 1 # or any other batch size that fits in your memory -LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) -# Instantiate LrFinder -lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) - -# Start range_test -lr_find.range_test(LRF_dataset) -lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) - -# %% [markdown] -# ### Model vis - -# %% -dot_img_file = 'model_1.png' -keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True) - -# %% [markdown] -# ### Model Save (Beta) - -# %% -# Copyright (c) 2024 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT -import json -import numpy as np -from keras.models import model_from_json -from keras.optimizers import get as get_optimizer - -def save_model(model, optimizer, filename): - """ - Save a Keras model's architecture and weights into a single gzipped file. - - Args: - model (tf.keras.Model): The Keras model to save. - optimizer (str): The name of the Keras optimizer to use. - filename (str): The filename to use for the saved file. - """ - # Save the architecture, weights and optimizer into a dictionary - model_dict = { - 'architecture': model.to_json(), - 'weights': [w.tolist() for w in model.get_weights()], - 'optimizer': optimizer.get_config()['name'] - } - - # Write the dictionary to a gzipped file - with gzip.GzipFile(f'{filename}.gz', 'w') as f: - f.write(json.dumps(model_dict).encode('utf-8')) - -def load_model(filename): - """ - Load a Keras model's architecture and weights from a gzipped file. - - Args: - filename (str): The filename of the saved file. - - Returns: - tf.keras.Model: The loaded Keras model. - """ - # Read the dictionary from the gzipped file - with gzip.GzipFile(f'{filename}.gz', 'r') as f: - model_dict = json.loads(f.read().decode('utf-8')) - - # Create a model from the architecture - model = model_from_json(model_dict['architecture']) - - # Set the model's weights - model.set_weights([np.array(w) for w in model_dict['weights']]) - - # Get the optimizer - optimizer = get_optimizer(model_dict['optimizer']) - - # Compile the model with the loaded optimizer - model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) - - return model - -save_model(model, SGD(), 'PAI_model_REV2') - -# %% [markdown] -# ## Loading the model - -# %% [markdown] -# ### Loading the full model - -# %% -import efficientnet.tfkeras -# Configuration -PRMC = False -freeze_from_opposite = False -Extra_EXT = '_T' -freeze_layers = 0 -randomly_frozen_layers = 0 -freeze_last_seven = False -# CEC_opt = Adagrad() -# CEC_opt = Yogi() -# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) -CEC_opt = SGD(momentum=0.9, nesterov=False) -# CEC_opt = Adam() -# Main -try: - if SAVE_TYPE == 'TF': - model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) - else: - model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) -except (ImportError, IOError) as e: - print(f'\033[91mfailed to load the model ERROR:\n{e}') -else: - print('\033[92mLoading model done.') - if not PRMC: - print('Compiling the AI model...\033[0m') - - for layer in model.layers: - layer.trainable = True - - # Select random layers to freeze - frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) - - for i, layer in enumerate(model.layers): - if i in frozen_layer_indices: - layer.trainable = False - else: - if freeze_from_opposite and (i > len(model.layers) - freeze_layers): - layer.trainable = False - elif (not freeze_from_opposite) and i < freeze_layers: - layer.trainable = False - else: - layer.trainable = True - - for layer in model.layers[-7:]: - layer.trainable = not freeze_last_seven - - model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) - model.summary(show_trainable=True, expand_nested=True) - print('done.') - -# %% [markdown] -# ### Loading model weights - -# %% -model.load_weights('PAI_model_weights.h5') -print('done.') - -# %% [markdown] -# ### Reset FC - -# %% -for layer in model.layers[-7:]: - if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'): - weight_initializer = layer.kernel_initializer - bias_initializer = layer.bias_initializer - - old_weights, old_biases = layer.get_weights() - - layer.set_weights([ - weight_initializer(shape=old_weights.shape), - bias_initializer(shape=len(old_biases)) - ]) - - -# %% [markdown] -# ## Training - -# %% [markdown] -# #### Rev2 (THE BEST) -# ``` -# Working: βœ… -# Other: -# + Tensorboard works. -# + Perverts overfitting. -# + Lower memory usage. -# - Slow training. -# + Achieving higher acc. -# - Some models dont work. -# ``` -# - TODO: -# - add Pruning - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -# CONF <--------------------------------------------------------------------------> -# Hyperparameters for training the model: -max_epoch = 384 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models. -subset_epoch = 6 # subset_epoch: Number of epochs to train each subset. -subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs. -PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. Common values: 8, 16, 26, 32, 64, 128. -subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192. -Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size. -RES_Train = False # RES_Train: Resume training if True. -MAX_LR = 0.011 # MAX_LR: Maximum learning rate. -DEC_LR = 0.00003 # DEC_LR: Learning rate decay. -MIN_LR = 0.0005 # MIN_LR: Minimum learning rate. -RES_LR = 0.006 # RES_LR: Resuming learning rate. -OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True) -Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True. -Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch). -TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage. -SAVE_FULLM = True # SAVE_FULLM: Save full model if True. -USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True. -AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True. -AdvSubsetC_SHR = 42 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs). -load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy. -load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'. -load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)). -load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True. -ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint. -ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf'). -Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage. -Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️). -EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️). -Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler. -Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work). -Use_tensorBoard_img = True # Use_tensorBoard_img: Enable tensorboard image logging. -Show_confusion_matrix_tensorBoard = False # Show_confusion_matrix_tensorBoard: Show confusion matrix on tensorboard. -BEST_RSN = 'PAI_model_T' # Best model save name prefix. (Uses a lot of memory and storage). -ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow). -IMAGE_GEN_PATH = 'Data\\image_SUB_generator.pkl' -# CONF END <----------------------------------------------------------------------> -#Prep -if RES_Train: - MAX_LR = RES_LR - PL_epoch = 1 -#VAR -Total_SUB_epoch_C = 0 # TO FIX TensorBoard -CU_LR = MAX_LR -all_histories = [] -chosen_indices = [] -subset_sizes = [] -best_acc = 0 -best_loss = float('inf') -#Funcs -def normalize_TO_RANGE(arr, min_val, max_val): - arr = arr.astype('float32') - arr = (arr - arr.min()) / (arr.max() - arr.min()) - arr = arr * (max_val - min_val) + min_val - return arr - -def Z_SCORE_normalize(arr): - arr = arr.astype('float32') - mean = np.mean(arr) - std_dev = np.std(arr) - arr = (arr - mean) / std_dev - return arr - -def add_image_grain_TRLRev2(image, intensity = 0.01): - # Generate random noise array - noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \ - + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2 - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -# noise_func_TRLRev2 ([REV1 OLD]) -if not USE_REV2_DP: - def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.08, 0.08) - intensityL1 = random.uniform(-0.05, 0.05) - else: - intensityL2 = random.uniform(-0.09, 0.09) - intensityL1 = random.uniform(-0.06, 0.06) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 112) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.07) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -# noise_func_TRLRev2 ([REV2 NEW]) -else: - def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.07, 0.07) - intensityL1 = random.uniform(-0.06, 0.06) - else: - intensityL2 = random.uniform(-0.09, 0.09) - intensityL1 = random.uniform(-0.07, 0.07) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 112) - - for channel in range(3): # Iterate over each RGB channel - image_channel = image[:, :, channel] - new_image_channel = new_image[:, :, channel] - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image_channel.shape[0], block_size_L2): - for j in range(0, image_channel.shape[1], block_size_L2): - block = image_channel[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block - image_channel = new_image_channel - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image_channel.shape[0], block_size_L1): - for j in range(0, image_channel.shape[1], block_size_L1): - block = image_channel[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block - - new_image[:, :, channel] = new_image_channel - - if add_img_grain: - intensity = random.uniform(0, 0.05) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -#CONST -train_SUB_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.18, - shear_range=0.18, - width_shift_range=0.18, - brightness_range=(0.82, 1.18), - height_shift_range=0.18, - channel_shift_range=100, - featurewise_center=True, - featurewise_std_normalization=True, - zca_whitening=False, - interpolation_order=2, - fill_mode='nearest', - preprocessing_function=noise_func_TRLRev2 - ) -class TerminateOnHighTemp(tf.keras.callbacks.Callback): - def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): - super().__init__() - self.active = active - self.check_every_n_batches = check_every_n_batches - self.high_temp = high_temp - self.low_temp = low_temp - self.pause_time = pause_time - self.batch_counter = 0 - - def on_batch_end(self, batch, logs=None): - if not self.active: - return - self.batch_counter += 1 - if self.batch_counter % self.check_every_n_batches == 0: - temperature = gpu_control.get_temperature() - if temperature > self.high_temp: - print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) - time.sleep(self.pause_time) - while gpu_control.get_temperature() > self.low_temp: - time.sleep(4) - print_Color('Resuming training...', ['yellow']) -class ExtendedTensorBoard(TensorBoard): - def on_epoch_end(self, epoch, logs=None): - logs = logs or {} - logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr) - logs['momentum'] = self.model.optimizer.momentum - super().on_epoch_end(epoch, logs) -class DummyCallback(Callback): - pass -# Define a function to plot the confusion matrix -def plot_confusion_matrix_TensorBoard(epoch, logs): - # Use the model to predict the values from the test dataset. - test_pred_raw = model.predict(x_test, verbose=0) - test_pred = np.argmax(test_pred_raw, axis=1) # Convert predictions from one-hot encoded to binary - - # Convert true labels from one-hot encoded to binary - y_true = np.argmax(y_test, axis=1) - - # Calculate the confusion matrix. - cm = confusion_matrix(y_true, test_pred) - - # Log the confusion matrix as an image summary. - figure = plt.figure(figsize=(8, 8)) - sns.heatmap(cm, annot=True, fmt="d", cmap=plt.cm.Blues) - buf = io.BytesIO() - plt.savefig(buf, format='png') - plt.close(figure) - buf.seek(0) - # Convert PNG buffer to TF image - image = tf.image.decode_png(buf.getvalue(), channels=4) - # Add the batch dimension - image = tf.expand_dims(image, 0) - # Add image summary - with file_writer.as_default(): - tf.summary.image("Confusion Matrix", image, step=epoch) -steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 -#callbacks>>> -# EarlyStopping -early_stopping = EarlyStopping(monitor='val_accuracy', - patience=EarlyStopping_P, - verbose=1, restore_best_weights=True, - mode='max' - ) if Use_ES_ONSUBT else DummyCallback() -# ModelCheckpoint -checkpoint_SUB = ModelCheckpoint(f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', - monitor=load_SUB_BRW_MODE, - save_best_only=True, mode=ModelCheckpoint_mode, - save_weights_only = True - ) if load_SUB_BRW else DummyCallback() -checkpoint_SUB.best = ModelCheckpoint_Reset_TO -# TerminateOnHighTemp -TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, - check_every_n_batches=6, - high_temp=73, - low_temp=58, - pause_time=60) -# confusion_matrix_callback -confusion_matrix_callback = LambdaCallback(on_epoch_end=plot_confusion_matrix_TensorBoard) if Show_confusion_matrix_tensorBoard else DummyCallback() -# TensorBoard -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -file_writer = tf.summary.create_file_writer(log_dir) -if Use_extended_tensorboard: - tensorboard_callback = ExtendedTensorBoard( - log_dir=log_dir, - write_images=Use_tensorBoard_img, - histogram_freq=1, - update_freq='epoch', - write_grads=True, - profile_batch='256,512' if Use_tensorboard_profiler else 0 - ) -else: - tensorboard_callback = TensorBoard( - log_dir=log_dir, - write_images=Use_tensorBoard_img, - histogram_freq=1, - update_freq='epoch', - write_grads=True, - profile_batch='256,512' if Use_tensorboard_profiler else 0 - ) -# OneCycleLr -if OneCycleLr_UFTS: - learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR, - steps_per_epoch=steps_per_epoch_train_SUB, - epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) -#PRES -# ... -#MAIN -print('Training the model...') -# INFOp -print_Color('\nSetup Verbose:', ['yellow']) -print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) -#warnings -P_warning('RES_Train is True.') if RES_Train else None -print_Color('Setup Verbose END.', ['yellow']) -# MAIN LOOP -try: - for epoch in range(1, max_epoch): - # Start Epoch - STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' - C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT - if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1 - start_FULL_time = time.time() - if Auto_clear_cache: - subprocess.run(["Cache_clear.cmd"], shell=True) - # TSEC: Total-Subset-Epoch-Count - print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) - # DP - if not AdvSubsetC: - print_Color('Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - if AdvSubsetC: - if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0: - print_Color('└───Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - chosen_indices = [] # Reset chosen_indices - - available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices)) - - if len(available_indices) < subset_size: - #DEBUG - # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.') - # If there are not enough available indices, choose from the indices that were chosen the longest time ago - old_indices = chosen_indices[:subset_size - len(available_indices)] - subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False)) - - # Update the list of chosen indices and their sizes - chosen_indices = chosen_indices[len(old_indices):] + subset_indices - subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices) - else: - subset_indices = list(np.random.choice(available_indices, subset_size, replace=False)) - - # Add the chosen indices to the list of already chosen indices - chosen_indices += subset_indices - subset_sizes += [subset_size] * len(subset_indices) - else: - subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) - # Taking the subset - x_SUB_train = x_train[subset_indices] - y_SUB_train = y_train[subset_indices] - x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train) - assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}' - print_Color('Preparing train data...', ['yellow']) - # if epoch == 1: # OLD - # print_Color('- ImageDataGenerator fit...', ['yellow']) - # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) - # print_Color('- ImageDataGenerator fit done.', ['yellow']) - if epoch == 1 or ALWAYS_REFIT_IDG == 2: - if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG: - print_Color('- Loading fitted ImageDataGenerator...', ['yellow']) - train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) - else: - print_Color('- Fitting ImageDataGenerator...', ['yellow']) - IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12 - train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) - pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb')) - print_Color('- ImageDataGenerator fit done.', ['yellow']) - - print_Color('- Augmenting Image Data...', ['yellow']) - train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, - y_SUB_train, - shuffle=False, - batch_size=len(x_SUB_train) - ).next() - print_Color('- Normalizing Image Data...', ['yellow']) - x_SUB_train = normalize_TO_RANGE(train_SUB_augmented_images[0], 0, 255) - x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 0.5) / 255 - # x_SUB_train = x_SUB_train / 255 - x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1) - y_SUB_train = train_SUB_augmented_images[1] - # DEBUG - if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1): - SITD = np.random.choice(subset_size, size=400, replace=False) - S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) - save_images_to_dir(np.clip(x_SUB_train[SITD], 0, 1), y_SUB_train[SITD], S_dir) - # learning_rate_schedule_SUB - if PL_epoch == 0: - CU_LR = MIN_LR - elif epoch >= PL_epoch and CU_LR > MIN_LR: - if (CU_LR - DEC_LR) < MIN_LR: - CU_LR = MIN_LR - else: - CU_LR -= DEC_LR - if not OneCycleLr_UFTS: - learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR, - steps_per_epoch=steps_per_epoch_train_SUB, - epochs=C_subset_epoch) - #FV - print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + "~*~*") if not OneCycleLr_UFTS else "~*OneCycleLr_UFTS Is ON~*"}]~*...', - ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True) - print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - # Train - print_Color('Training on subset...', ['green']) - start_SUBO_time = time.time() - SUB_history = model.fit(x_SUB_train, - y_SUB_train, - epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C) - batch_size=Conf_batch_size_REV2, - validation_data=(x_test, y_test), - verbose='auto', - initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard - callbacks=[ - learning_rate_schedule_SUB, - TerminateOnHighTemp_CB, - checkpoint_SUB, - early_stopping, - tensorboard_callback, - confusion_matrix_callback - ] - ) - end_SUBO_time = time.time() - print_Color('Subset training done.', ['green']) - if load_SUB_BRW_LMODE == 1: - if max(SUB_history.history['val_accuracy']) > best_acc: - load_weights = True - elif min(SUB_history.history['val_loss']) < best_loss: - load_weights = True - else: - load_weights = False - else: - load_weights = True - - if load_SUB_BRW and load_weights: - print_Color('Loading the best weights...', ['yellow']) - # Get the filename of the best weights file - list_of_files = glob.glob('cache\\*.h5') - try: - best_weights_filename = max(list_of_files, key=os.path.getctime) - print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow']) - model.load_weights(best_weights_filename) - except Exception as Err: - print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red']) - elif load_SUB_BRW and (not load_weights): - print_Color_V2(f'Not loading weights[BSR:acc{{{max(SUB_history.history["val_accuracy"]):.4f}}}, loss{{{min(SUB_history.history["val_loss"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]') - all_histories.append(SUB_history.history) - checkpoint_SUB.best = ModelCheckpoint_Reset_TO - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Evaluate the model on the test data - evaluation = model.evaluate(x_test, y_test, verbose=0) - - # Extract the loss and accuracy from the evaluation results - loss = evaluation[0] - acc = evaluation[1] - print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True) - print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True) - # If the accuracy is higher than the best_acc - if acc > best_acc: - print_Color_V2(f'Improved model accuracy from{best_acc:10f} to {acc:10f}. Saving model.') - # Update the best_acc - best_acc = acc - if SAVE_FULLM: - # Save the model - if SAVE_TYPE == 'TF': - print_Color_V2(f'Saving full model tf format...') - model.save(BEST_RSN, save_format='tf') - else: - print_Color_V2(f'Saving full model H5 format...') - model.save(f'{BEST_RSN}.h5') - model.save_weights('PAI_model_weights.h5') - else: - print_Color_V2(f'Model accuracy did not improve from {best_acc:.10f}. Not saving model.') - - # If the loss is higher than the best_loss - if loss < best_loss: - print_Color_V2(f'Improved model loss from {best_loss:.10f} to {loss:.10f}. Saving model.') - - # Update the best_acc - best_loss = loss - - if SAVE_FULLM: - # Save the model - if SAVE_TYPE == 'TF': - print_Color_V2(f'Saving full model tf format...') - model.save(BEST_RSN + '_BL', save_format='tf') - else: - print_Color_V2(f'Saving full model H5 format...') - model.save(f'{BEST_RSN}_BL.h5') - model.save_weights('PAI_model_weights_BL.h5') - else: - print_Color_V2(f'Model loss did not improve from {best_loss:.10f}. Not saving model.') - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Epoch end - end_time = time.time() - epoch_time = end_time - start_FULL_time - print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec') - epoch_SUB_time = end_SUBO_time - start_SUBO_time - print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec') - epoch_OTHERO_time = epoch_time - epoch_SUB_time - print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec') - print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) - Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard -except KeyboardInterrupt: - print('\nKeyboardInterrupt.') -# End -try: - history = {} - for key in all_histories[0].keys(): - # For each metric, concatenate the values from all histories - history[key] = np.concatenate([h[key] for h in all_histories]) -except Exception as Err: - print(f'Failed to make model `history` var.\nERROR: {Err}') - -print('Training done.\n') -# del vars -try: - del train_SUB_datagen - del train_SUB_augmented_images -except NameError: - pass - -# %% [markdown] -# #### Rev1 (⚠️deprecated⚠️) -# ``` -# Working: βœ… -# Other: -# + Tensorboard works. -# - Can cause overfitting. -# ``` - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF -Conf_batch_size = 8 -OneCycleLr_epoch = 20 -Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) -#TensorBoard conf -TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) -# Learning rate configuration -Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr -MAX_LR = 0.0174 -# First time -if Learning_rate_conf == 1: - learning_rate_start = 8e-04 - learning_rate_max = 5e-03 - learning_rate_min = 5e-05 - learning_rate_rampup_epochs = 5 - learning_rate_sustain_epochs = 1 - learning_rate_exp_decay = .3 - #TEMP - # learning_rate_start = 8e-04 - # learning_rate_max = 1e-02 - # learning_rate_min = 8e-04 - # learning_rate_rampup_epochs = 5 - # learning_rate_sustain_epochs = 3 - # learning_rate_exp_decay = .45 -# 2th time -if Learning_rate_conf == 2: - if Learning_rate_conf_SET2C == 1: - learning_rate_start = 4.10e-06 - learning_rate_max = 4.10e-06 - learning_rate_min = 4.10e-06 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 2: - learning_rate_start = 4e-07 - learning_rate_max = 4e-07 - learning_rate_min = 4e-07 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 3: - learning_rate_start = 5e-04 - learning_rate_max = 5e-04 - learning_rate_min = 5e-04 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 -# Function to build learning rate schedule -if Learning_rate_conf in [1,2]: - def build_learning_rate_fn(lr_start=learning_rate_start, - lr_max=learning_rate_max, - lr_min=learning_rate_min, - lr_rampup_epochs=learning_rate_rampup_epochs, - lr_sustain_epochs=learning_rate_sustain_epochs, - lr_exp_decay=learning_rate_exp_decay): - lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync - def learning_rate_fn(epoch): - if epoch < lr_rampup_epochs: - lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start - elif epoch < lr_rampup_epochs + lr_sustain_epochs: - lr = lr_max - else: - lr = (lr_max - lr_min) *\ - lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min - return lr - return learning_rate_fn - -# Calculate steps per epoch -steps_per_epoch_train = len(x_train) // Conf_batch_size - -# Set up callbacks -class EpochEndMON(tf.keras.callbacks.Callback): - def on_epoch_end(self, epoch, logs=None): - optimizer = self.model.optimizer - if hasattr(optimizer, 'lr'): - lr = tf.keras.backend.get_value(optimizer.lr) - print(f'\nLearning rate for epoch {epoch+1} is {lr}') - if hasattr(optimizer, 'momentum'): - momentum = tf.keras.backend.get_value(optimizer.momentum) - print(f'Momentum for epoch {epoch+1} is {momentum}') - if logs: - val_loss = logs.get('val_loss') - val_acc = logs.get('val_accuracy') - print(f'Validation loss for epoch {epoch+1} is {val_loss}') - print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') - - print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') - -# Instantiate the callback -EpochEndMON_callback = EpochEndMON() -if Learning_rate_conf in [1,2]: - learning_rate_fn = build_learning_rate_fn() - learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) -else: - learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch) -if SAVE_TYPE == 'TF': - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -else: - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' -tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True) - -# Train the model -print('Log dir:', log_dir) -#MInfo -print('Input Shape:', model.input_shape) -print('Output Shape:', model.output_shape) -print('Loss Function:', model.loss) -print('Training the model...\n') -history = model.fit(x_train, - y_train, - epochs=256, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) -print('Training done.\n') - -# %% [markdown] -# ## Saving model weights -# - -# %% -Extra_EXT = '_T' -# Save the weights -print('Saving weights...') -model.save_weights('PAI_model_weights.h5') -print('Saving full model...') -if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') -else: - try: - model.save(f'PAI_model{Extra_EXT}.h5') - except ValueError: - print('failed to save in .h5 format!') - print('Saving full model in tf format...') - model.save(f'PAI_model{Extra_EXT}', save_format='tf') - -# %% [markdown] -# ## Garbage Collection (memory) - -# %% -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() - -# %% [markdown] -# ## Analyse model Training performance - -# %% -# Save history -save_list(history, 'history\\model_history.pkl.gz', compress=True) - -# %% -# load history -history = load_list('history\\model_history.pkl.gz', compressed=True) - -# %% -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -import seaborn as sns - -# Chunk size for 3D plot -chunk_size = 6 # Change this to your desired chunk size - -def convert_history(history): - if isinstance(history, tf.keras.callbacks.History): - return history.history - else: - return history - -def chunked_data(data, chunk_size): - return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)] - - -try: - EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' - history = convert_history(history) - - # Calculate deltas - delta_loss = np.diff(history['loss']) - delta_accuracy = np.diff(history['accuracy']) - - try: - delta_val_loss = np.diff(history['val_loss']) - delta_val_accuracy = np.diff(history['val_accuracy']) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') - - plt.figure(figsize=(16, 10)) - # Loss - plt.subplot(2, 2, 1) - plt.plot(history['loss'], label='loss') - try: - plt.plot(history['val_loss'], label='val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_loss.') - plt.title('Model Loss') - plt.ylabel('Loss') - plt.xlabel(EPM) - plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - # Density plot for loss - plt.subplot(2, 2, 2) - plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) - try: - plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss (density plot).') - plt.title('Density Plot for Loss') - plt.xlabel('Loss') - plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - - # Accuracy - plt.subplot(2, 2, 3) - plt.plot(history['accuracy'], label='accuracy') - try: - plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy.') - plt.title('Model Accuracy') - plt.ylabel('Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Density plot for accuracy - plt.subplot(2, 2, 4) - plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) - try: - plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy (density plot).') - plt.title('Density Plot for Accuracy') - plt.xlabel('Accuracy') - plt.grid(True) - - # Delta Loss - plt.figure(figsize=(14, 8)) - plt.subplot(2, 2, 1) - plt.plot(delta_loss, label='delta_loss') - try: - plt.plot(delta_val_loss, label='delta_val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_loss.') - plt.title('Delta Model Loss') - plt.ylabel('Delta Loss') - plt.ylim(top=1.5, bottom=-1.5) - plt.xlabel(EPM) - plt.grid(True) - # Delta Accuracy - plt.subplot(2, 2, 2) - plt.plot(delta_accuracy, label='delta_accuracy') - try: - plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_accuracy.') - plt.title('Delta Model Accuracy') - plt.ylabel('Delta Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Calculate chunked data - chunked_loss = chunked_data(history['val_loss'], chunk_size) - chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) - - # Clip the loss values to a maximum of max(history['val_loss'][10:]) - max_loss = max(history['val_loss'][10:]) - chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) - - # Create 3D surface plots for each chunk - fig = plt.figure(figsize=(14, 8)) - ax = fig.add_subplot(121, projection='3d') - X = np.arange(len(chunked_loss)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Loss') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Loss') - - ax = fig.add_subplot(122, projection='3d') - X = np.arange(len(chunked_accuracy)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Accuracy') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Accuracy') - - # Function to calculate the average of chunks - def chunked_average(values, chunk_size): - return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] - - avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) - avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) - - # Find the chunk with the highest average accuracy - max_acc_chunk_index = np.argmax(avg_accuracy_chunks) - max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] - - # Create a pile plot for accuracy - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy') - plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') - plt.xlabel('Chunk') - plt.ylabel('Average Accuracy') - plt.title('Average Validation Accuracy per Chunk') - plt.legend() - - # Create a pile plot for loss - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') - plt.xlabel('Chunk') - plt.ylabel('Average Loss') - plt.title('Average Validation Loss per Chunk') - plt.legend() - - # Function to calculate the average of each epoch across chunks, ignoring the first chunk - def average_across_chunks(values, chunk_size): - num_chunks = len(values) // chunk_size - avg_values = [] - for epoch in range(chunk_size): - epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] - avg_values.append(np.mean(epoch_values)) - return avg_values - - # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk - avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) - avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) - - # Create a bar plot for average accuracy and loss of each epoch across chunks - plt.figure(figsize=(12, 6)) - - # Create an index for each epoch - epoch_indices = np.arange(len(avg_accuracy_epochs)) - - # Plot accuracy and loss as bars - plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) - plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) - - # Add labels and title - plt.xlabel('Epoch (within chunk)') - plt.ylabel('Average Value') - plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') - plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers - plt.legend() - - plt.tight_layout() - plt.show() - -except (ValueError, NameError) as E: - print(f'\033[91mFailed to load model history.\nError: {E}') - -# %% [markdown] -# ## Analyse model Predicting performance - -# %% -import seaborn as sns -from sklearn.metrics import confusion_matrix, accuracy_score -from scipy.stats import binom -from tqdm import tqdm -import efficientnet.tfkeras -import cv2 -import gc -# Garbage Collection (memory) -gc.collect() - -Extra_EXT = '_T' # _T or _T_BL -Train_data_test = False -if SAVE_TYPE == 'TF': - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}') -else: - # Load the pre-trained model - model = load_model(f'PAI_model{Extra_EXT}.h5') - -# Ensure the model's input_shape matches your data -assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' - -# Make predictions on validation data -val_predictions = model.predict(x_val) -val_predictions = np.argmax(val_predictions, axis=1) - -# Make predictions on Train data -if Train_data_test: - Train_predictions = model.predict(x_train) - Train_predictions = np.argmax(Train_predictions, axis=1) - -# Make predictions on test data -test_predictions = model.predict(x_test) -test_predictions = np.argmax(test_predictions, axis=1) - -# Convert y_val and y_test from one-hot encoder to their original form -y_val_original = np.argmax(y_val, axis=1) -y_test_original = np.argmax(y_test, axis=1) -if Train_data_test: - y_train_original = np.argmax(y_train, axis=1) - -# Calculate accuracy on validation data -val_accuracy = accuracy_score(y_val_original, val_predictions) - -# Calculate accuracy on Train data -if Train_data_test: - Train_accuracy = accuracy_score(y_val_original, Train_predictions) - -# Calculate accuracy on test data -test_accuracy = accuracy_score(y_test_original, test_predictions) - -# Print acc -if Train_data_test: - print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}({Train_accuracy:.5%})') -print(f'The accuracy of the model on validation data is {val_accuracy:.2%}({val_accuracy:.5%})') -print(f'The accuracy of the model on test data is {test_accuracy:.2%}({test_accuracy:.5%})') - -# Visualize the predictions on validation data as a grid of squares -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - plt.imshow(x_val[i]) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() -#Heatmap -plt.figure(figsize=(12, 6)) -for i in range(10): - plt.subplot(2, 5, i+1) - img = x_val[i] - heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) - heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) - heatmap = np.uint8(255 * heatmap) - # Apply Adaptive Histogram Equalization - clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object - heatmap = clahe.apply(heatmap) - heatmap = cv2.applyColorMap(np.max(heatmap) - heatmap, cv2.COLORMAP_JET) - if RANGE_NOM: - superimposed_img = (heatmap / 255) * 0.4 + img - else: - superimposed_img = (heatmap / 255) * 0.4 + (img / 255) - #clip - superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] - plt.imshow(superimposed_img) - plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') - plt.axis('off') -plt.tight_layout() -plt.show() - -# Define the list of labels -labels = ['NORMAL', 'PNEUMONIA'] - -# Create a confusion matrix for validation data -val_cm = confusion_matrix(y_val_original, val_predictions) - -# Create a confusion matrix for test data -test_cm = confusion_matrix(y_test_original, test_predictions) - -# Plot the confusion matrix as a heatmap for validation data -plt.figure(figsize=(8, 6)) -sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Validation Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Plot the confusion matrix as a heatmap for test data -plt.figure(figsize=(8, 6)) -sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) -plt.title('Confusion Matrix - Test Data') -plt.xlabel('Predicted') -plt.ylabel('True') -plt.show() - -# Define the range of test data sizes to use -data_sizes = range(1, len(x_test), 4) -# Calculate the probability of a wrong prediction based on test accuracy -prob_wrong = 1 - test_accuracy - -# Create a list to store the number of incorrect predictions for each test data size -incorrect_predictions = [] - -# Generate predictions and track incorrect predictions for each data size -for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): - # Garbage Collection (memory) - gc.collect() - # Randomly select a subset of test data - indices = np.random.choice(len(x_test), size, replace=False) - x_test_subset = x_test[indices] - y_test_subset = y_test[indices] - - # Make predictions on the subset of test data - test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) - test_predictions = np.argmax(test_predictions, axis=1) - y_test_original_subset = np.argmax(y_test_subset, axis=1) - - # Calculate the number of incorrect predictions - incorrect_preds = np.sum(test_predictions != y_test_original_subset) - incorrect_predictions.append(incorrect_preds) - -# Plot the number of incorrect predictions vs. the number of data points -plt.figure(figsize=(10, 6)) -plt.plot(data_sizes, incorrect_predictions) -plt.xlabel('Number of Data Points') -plt.ylabel('Number of Incorrect Predictions') -# Add gridlines for the x and y axes -plt.grid(True) - -# Change the tick spacing for the x and y axes -plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) -plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) - -plt.title('Number of Incorrect Predictions vs. Number of Data Points') -plt.show() -# Deprecated⚠️------------------------------>>> -# prob_L = 0.9995 -# # Define the range of test data sizes to use -# data_sizes = range(1, len(x_test), 1) - -# # Calculate the probability of a wrong prediction based on test accuracy -# prob_wrong = 1 - test_accuracy - -# # Create a list to store the probability of getting at least one wrong answer for each test data size -# probabilities = [] - -# # Calculate the probability of getting at least one wrong answer for each data size -# for size in data_sizes: -# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 -# cdf = binom.cdf(0, size, prob_wrong) -# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer -# prob = 1 - cdf -# probabilities.append(prob) - -# # Find the index of the first data point that has a probability greater than prob_L% -# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) - -# # Limit the x-axis to the first data point that has a probability greater than prob_L% -# data_sizes = data_sizes[:index+1] -# probabilities = probabilities[:index+1] - -# # Plot the probability vs. the number of data points -# plt.figure(figsize=(10, 6)) -# plt.plot(data_sizes, probabilities) -# plt.xlabel('Number of Data Points') -# plt.ylabel('Probability') - -# # Add gridlines for the x and y axes -# plt.grid(True) - -# # Change the tick spacing for the x and y axes -# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10)) -# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100)) - -# plt.ylim(top=1.01) - -# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') -# plt.show() -# Deprecated⚠️------------------------------<<< - - +# %% [markdown] +# # keras/TF model +# 
+#  Copyright (c) 2023 Aydin Hamedi
+#  
+#  This software is released under the MIT License.
+#  https://opensource.org/licenses/MIT
+#
+ +# %% [markdown] +# ## Pre Conf + +# %% +CPU_only = False # True to Force TF to use the cpu + +# %% [markdown] +# ## Pylibs + +# %% +import io +import os +import sys +import time +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' +if CPU_only: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' +import cv2 +import glob +import keras +import pprint +import random +import shutil +import gzip +import glob +import pickle +import datetime +import subprocess +import gpu_control +import numpy as np +import pandas as pd +from tqdm import tqdm +import seaborn as sns +from hyperas import optim +# import tensorflow_addons as tfa +from keras_adabound import AdaBound +from importlib import reload +from sklearn.metrics import confusion_matrix +from keras.losses import categorical_crossentropy +import tensorflow as tf +from keras.models import Model +from scipy.ndimage import zoom +import matplotlib.pyplot as plt +from model_profiler import model_profiler +from keras_gradient_noise import add_gradient_noise +from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax +# from tensorflow_addons.optimizers import Yogi +from adabelief_tf import AdaBeliefOptimizer +from sklearn.preprocessing import LabelEncoder +from imblearn.over_sampling import SMOTE +from keras.regularizers import l2 +from keras.models import load_model +from matplotlib import pyplot as plt +from PIL import Image, ImageDraw, ImageFont +from keras import Sequential +from random import randint, choice, shuffle +from keras.callbacks import EarlyStopping +from keras.callbacks import TensorBoard, LambdaCallback +import tensorflow_model_optimization as tfmot +from keras.utils import to_categorical +from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.layers import Conv2D,\ + MaxPooling2D,\ + Flatten,\ + Dense,\ + Dropout,\ + BatchNormalization,\ + SeparableConv2D,\ + Input, Concatenate,\ + GlobalAveragePooling2D,\ + CuDNNLSTM, concatenate,\ + Reshape, Multiply, \ + Conv1D, MaxPooling1D +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.Grad_cam import make_gradcam_heatmap +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from Utils.Other import * +# Other +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + +# %% [markdown] +# ## Conf +# + +# %% [markdown] +# ### Data processing conf + +# %% +# Directory paths# Directory paths for training, test and validation image data +train_dir = 'Database\\Train\\Data\\train' +test_dir = 'Database\\Train\\Data\\test' +validation_dir = 'Database\\Train\\Data\\val' +img_res = [224, 224, 3] +# img_res = [324, 324, 3] +# img_res = [224, 224, 3] +# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!) +interpolation_order_IFG = 2 +categorical_IMP = True +Make_EV_DATA = False +R_fill_mode = True +add_img_grain = True +Save_TS = True +Use_SMOTE = False # (⚠️Beta⚠️) +ADBD = 0 +OP_HDC = False +SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1 +LNTS = 0 +Debug_OUT = False +adjust_brightness_Mode = True +RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️) +scale_data_NP_M = False # (⚠️deprecated⚠️) + +# %% [markdown] +# ### Training + +# %% +SAVE_TYPE = 'H5' +Use_mixed_float16 = False +#Other +if Use_mixed_float16: + tf.keras.mixed_precision.set_global_policy('mixed_float16') +else: + tf.keras.mixed_precision.set_global_policy('float32') + +print(tf.keras.mixed_precision.global_policy()) + +# %% [markdown] +# ## data processing +# + +# %% +#Z_SCORE_normalize +def Z_SCORE_normalize(arr): + arr = arr.astype('float32') + mean = np.mean(arr) + std_dev = np.std(arr) + arr = (arr - mean) / std_dev + return arr +#normalize_TO_RANGE +def normalize_TO_RANGE(arr, min_val, max_val): + arr = arr.astype('float32') + arr = (arr - arr.min()) / (arr.max() - arr.min()) + arr = arr * (max_val - min_val) + min_val + return arr +#scale_data +def scale_data_NP(data): + if scale_data_NP_M: + data = data.astype('float32') + data = (data - 127.5) / 127.5 + return data + else: + return data / 255 +#add_image_grain +def add_image_grain(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +#apply_clahe_rgb_array +def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): + # Create a CLAHE object + clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) + + # Iterate over each image in the array + for i in range(len(images)): + # Split the image into color channels + b, g, r = cv2.split(images[i]) + + # Convert the channels to the appropriate format + b = cv2.convertScaleAbs(b) + g = cv2.convertScaleAbs(g) + r = cv2.convertScaleAbs(r) + + # Apply adaptive histogram equalization to each channel + equalized_b = clahe.apply(b) + equalized_g = clahe.apply(g) + equalized_r = clahe.apply(r) + + # Merge the equalized channels back into an image + equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) + + # Replace the original image with the equalized image in the array + images[i] = equalized_image + + return images +#noise_func +def noise_func(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.05, 0.05) + intensityL1 = random.uniform(-0.04, 0.04) + else: + intensityL2 = random.uniform(-0.06, 0.06) + intensityL1 = random.uniform(-0.04, 0.04) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026 + new_image = add_image_grain(new_image, intensity=intensity) + return new_image +#shuffle_data +def shuffle_data(x, y): + indices = np.arange(x.shape[0]) + np.random.shuffle(indices) + x = x[indices] + y = y[indices] + return x, y +#save_images_to_dir +def save_images_to_dir(images, labels, dir_path): + # create the directory if it doesn't exist + if not os.path.exists(dir_path): + os.makedirs(dir_path) + # iterate over the images and labels + for i, (image, label) in enumerate(zip(images, labels)): + # get the class label + class_label = np.argmax(label) + # create the file path + file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') + # save the image to the file path + plt.imsave(file_path, image.squeeze()) + # compress the directory + shutil.make_archive(dir_path, 'gztar', dir_path) + # remove the original directory + shutil.rmtree(dir_path) +#Debug_img_Save +def Debug_img_Save(img, id = 'DEF'): + SITD = np.random.choice(img.shape[0], size=400, replace=False) + S_dir = f'Samples\\Debug\\{id}\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) + save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir) +# Create an ImageDataGenerator for the training set +if OP_HDC: + print_Color('Using OP_HDC IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.86, 1.1), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=interpolation_order_IFG, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +else: + print_Color('Using Def IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.26, + shear_range=0.25, + width_shift_range=0.25, + brightness_range=(0.78, 1.1), + height_shift_range=0.25, + channel_shift_range=100, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +train_datagen_SM = ImageDataGenerator( + horizontal_flip=False, + vertical_flip=False, + rotation_range=20, + zoom_range=0.07, + shear_range=0.07, + width_shift_range=0.07, + brightness_range=(0.99, 1.01), + height_shift_range=0.07, + channel_shift_range=0, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False +) +# Create an iterator for the training set +train_generator_SM = train_datagen_SM.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') +# Create an ImageDataGenerator for the validation set (OP) +if Make_EV_DATA: + val_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the validation set + val_generator = val_datagen.flow_from_directory( + validation_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), + class_mode='binary', + color_mode='rgb') + + # Create an ImageDataGenerator for the test set + test_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the test set + test_generator = test_datagen.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), + class_mode='binary', + color_mode='rgb') +# Load all images and labels into memory +print_Color('Loading all images and labels into memory...', ['yellow']) +x_train, y_train = next(iter(train_generator_SM)) +if Make_EV_DATA: + x_val, y_val = next(iter(val_generator)) + x_test, y_test = next(iter(test_generator)) +if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG +# fit parameters from data +# train_datagen.fit(x_train) +#to_categorical (TEMP) +if categorical_IMP: + print_Color('Making categorical data...', ['yellow']) + y_train = to_categorical(y_train, num_classes=2) + if Make_EV_DATA: + y_val = to_categorical(y_val, num_classes=2) + y_test = to_categorical(y_test, num_classes=2) +# Use_SMOTE +if Use_SMOTE: + print_Color('SMOTE...', ['yellow']) + # Convert y_train from one-hot encoding to label encoding + y_train_label_encoded = np.argmax(y_train, axis=1) + + # Print the original label distribution + unique, counts = np.unique(y_train_label_encoded, return_counts=True) + print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) + + # Use SMOTE to oversample the minority class + smote = SMOTE(random_state=42) + x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) + + # Print the resampled label distribution + unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) + print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) + + # Reshape x_train_res back to the original x_train shape + x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) + + # Convert y_train_res from label encoding back to one-hot encoding + y_train_res = to_categorical(y_train_res_label_encoded) + + # Calculate the ratio of two labels after resampling + pneumonia_count = np.sum(y_train_res[:, 1]) + total_count = y_train_res.shape[0] + label_ratio_res = pneumonia_count / total_count + label_ratio_percentage_res = label_ratio_res * 100 + + # Replace the original data with the resampled data + x_train = x_train_res + y_train = y_train_res + + # Delete the resampled data to free up memory + del x_train_res, y_train_res_label_encoded, y_train_res +# Generating augmented data +print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', + ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], + advanced_mode=True) +if ADBD > 0: + for i in range(ADBD): + # ADB_clip_limit Scheduler>>> + if i == 0: + ADB_clip_limit = 0.8 + else: + #V1>>> + CL_SLM = 2.4 + ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) + # Try it in win graphing calculator copy and paste: + # β”Œ-------------┬--┬---------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ + # β””-------------β”΄--β”΄---------------β”˜ + #V2>>> + # CL_SLM_2 = 1.4 + # CL_SLM_Start_2 = 2 + # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) + # Try it in win graphing calculator copy and paste: + # β”Œ-----------------┬--┬-------------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ + # β””-----------------β”΄--β”΄-------------------β”˜ + print(f'> Generating ADB[{i+1}/{ADBD}]...') + # prepare an iterators to scale images + train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) + + # get augmented data + x_train_augmented, y_train_augmented = train_iterator.next() + print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') + print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') + x_train_augmented = np.clip(x_train_augmented, 0, 255) + if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG + #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) + x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss + print(f'> └───Adding the Generated ADB...') + if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG + # append augmented data to original data + x_train = np.concatenate([x_train, x_train_augmented]) + y_train = np.concatenate([y_train, y_train_augmented]) + #free up memory + del y_train_augmented + del x_train_augmented +# normalizing +print_Color('Normalizing image data...', ['yellow']) +if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG +x_train = np.clip(x_train, 0, 255) +if RANGE_NOM: + x_train = scale_data_NP(x_train) +y_train = np.array(y_train) +if Make_EV_DATA: + x_test = np.clip(x_test, 0, 255) + x_val = np.clip(x_val, 0, 255) + if RANGE_NOM: + x_val = scale_data_NP(x_val) + y_val = np.array(y_val) + if RANGE_NOM: + x_test = scale_data_NP(x_test) + y_test = np.array(y_test) +if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG +# Check the data type of image data +print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) +# Check the range of image data +print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) +# Calculate the ratio of two labels +if categorical_IMP: + label_sums = np.sum(y_train, axis=0) + label_ratio = label_sums / (np.sum(y_train) + 1e-10) + label_ratio_percentage = label_ratio * 100 + print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', + ['normal', 'red', 'magenta', 'green'], advanced_mode=True) +print_Color('Setting LNTS...', ['yellow']) +# Get the total number of samples in the arrays +num_samples = x_train.shape[0] +print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +if LNTS != 0: + print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) + print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) + # Generate random indices to select LNTS samples + indices = np.random.choice(num_samples, size=LNTS, replace=False) + # Select the samples using the generated indices + x_selected = x_train[indices] + y_selected = y_train[indices] + x_train = x_selected + y_train = y_selected + #free up memory + del x_selected + del y_selected + del indices + #Debug + num_samples = x_train.shape[0] + print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +# Shuffle the training data +print_Color('shuffling data...', ['yellow']) +x_train, y_train = shuffle_data(x_train, y_train) +#save_images_to_dir +if Save_TS: + print_Color('Saving TS...', ['yellow']) + SITD = np.random.choice(num_samples, size=400, replace=False) + S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) + if RANGE_NOM: + if scale_data_NP_M: + save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) +print_Color('Done.', ['green']) + +# %% [markdown] +# ## Save EV Dataset + +# %% +np.save(f'Database\\Test\\Data\\x_val{SL_EX}.npy', x_val) +np.save(f'Database\\Test\\Data\\y_val{SL_EX}.npy', y_val) +np.save(f'Database\\Test\\Data\\x_test{SL_EX}.npy', x_test) +np.save(f'Database\\Test\\Data\\y_test{SL_EX}.npy', y_test) + +# %% [markdown] +# ## Load EV Dataset + +# %% +x_val = np.load(f'Database\\Test\\Data\\x_val{SL_EX}.npy') +y_val = np.load(f'Database\\Test\\Data\\y_val{SL_EX}.npy') +x_test = np.load(f'Database\\Test\\Data\\x_test{SL_EX}.npy') +y_test = np.load(f'Database\\Test\\Data\\y_test{SL_EX}.npy') + +# %% [markdown] +# ## Data Analyzation + +# %% +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns +from scipy.stats import zscore + +# Select a subset of your data +subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels +subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values +indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False) +indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False) +subset_pixels = x_train[indices_pixels] +subset_mean = x_train[indices_mean] + +# Reshape the data for calculating Z-scores +reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1]) +reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1]) + +# Calculate the mean intensity +mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1) +mean_intensity_mean = reshaped_data_mean.mean(axis=-1) + +# Stack the mean intensity with the reshaped data +data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)]) +data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)]) + +# Calculate Z-scores +z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0)) +z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0)) + +# Identify outliers +outliers_pixels = np.where(z_scores_pixels > 3) +outliers_mean = np.where(z_scores_mean > 3) + +# Create a 3D scatter plot for RGB channels +fig = plt.figure(figsize=(10, 20)) + +# Plot for individual pixels +ax = fig.add_subplot(211, projection='3d') +ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1) +ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red') +ax.set_title('Z-Score Scatter Plot for Individual Pixels') +ax.set_xlabel('Red') +ax.set_ylabel('Green') +ax.set_zlabel('Blue') + +# Plot for mean RGB values +ax = fig.add_subplot(212, projection='3d') +ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1) +ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red') +ax.set_title('Z-Score Scatter Plot for Mean RGB Values') +ax.set_xlabel('Red') +ax.set_ylabel('Green') +ax.set_zlabel('Blue') + +# Density plot of the mean intensity +plt.figure(figsize=(10, 5)) +sns.kdeplot(data=z_scores_pixels[:, -1], fill=True) +plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels') +plt.xlabel('Z-Score') + +sns.kdeplot(data=z_scores_mean[:, -1], fill=True) +plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values') +plt.xlabel('Z-Score') + +# Display the plot +plt.show() + +# %% [markdown] +# ## Creating the model +# + +# %% [markdown] +# ### Rev1 +# ``` +# recommended: ⚠️ +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: β‰…95.1 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(EfficientNetB7) +# ``` + +# %% +from keras.applications import EfficientNetB7 + +EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax') +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.1 +# ``` +# recommended: ❌ +# statuses: S.Ready (can improve) +# Working: ❌ +# Max fine tuned acc: β‰…93.2 +# Max fine tuned acc TLRev2: N/A +# type: transfer learning>>>(ConvNeXtLarge) +# ``` + +# %% +from keras.applications import ConvNeXtLarge + +ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False) +# define new model +model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +model.summary() + + +# %% [markdown] +# ### Rev1.2 +# ``` +# recommended: βœ… +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: 95.3 +# Max fine tuned acc TLRev2: 97.12 +# type: transfer learning>>>(EfficientNetB7::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +# FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENB7(input_shape=( + img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / + len(base_model.layers)) * 100 + print( + f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL>>> + #GlobalAveragePooling2D + base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) + #Dense + Dense_L1 = Dense(512, activation='relu', + kernel_regularizer=l2(0.02), + name='FC_C_Dense-L1-512' + )(base_model_FT) + #Dropout + Dropout_L1 = Dropout(0.1, + name='FC_C_Dropout-L1-0.1' + )(Dense_L1) + #BatchNormalization + BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' + )(Dropout_L1) + #Dense + Dense_L2 = Dense(512, activation='relu', + kernel_regularizer=l2(0.01), + name='FC_C_Dense-L2-512' + )(BatchNorm_L2) + #BatchNormalization + BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' + )(Dense_L2) + #Dense + Dense_L3 = Dense(128, activation='relu', + name='FC_C_Dense-L3-128' + )(BatchNorm_L3) + #Dense + # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) + predictions = Dense(2, activation='softmax', + name='FC_OUTPUT_Dense-2')(Dense_L3) + # CDL<<< + model_EfficientNetB7_NS = Model( + inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + # OPT/compile + opt = SGD(momentum=0.9, nesterov=False) + # opt = Nadam() + # opt = Adamax() + # opt = RMSprop(momentum=0.9) + # opt = Adagrad() + # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.3 +# ``` +# recommended: ❌ +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: ⚠️ +# type: transfer learning>>>(EfficientNetB7|Xception::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +from keras.applications.xception import Xception + +#FUNC +def Combo_Model(freeze_layers1, freeze_layers2): + # Define a common input + common_input = Input(shape=(img_res[0], img_res[1], img_res[2])) + + # Base model 1 + base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + # base_model1.load_weights('models\Ready\Other\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True) + base_model1_out = base_model1(common_input) + + # Base model 2 + base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False) + # base_model1.load_weights('models\Ready\Other\Xception_PRET.h5', by_name=True, skip_mismatch=True) + base_model2_out = base_model2(common_input) + + print('Total base_model1 layers: ', len(base_model1.layers)) + print('Total base_model2 layers: ', len(base_model2.layers)) + + # Freeze the specified number of layers in both models + for layer in base_model1.layers[:freeze_layers1]: + layer.trainable = False + for layer in base_model2.layers[:freeze_layers2]: + layer.trainable = False + + # Unfreeze the rest in both models + for layer in base_model1.layers[freeze_layers1:]: + layer.trainable = True + for layer in base_model2.layers[freeze_layers2:]: + layer.trainable = True + + # Combine the output of the two base models + combined = concatenate([Dense(512, + activation='relu', + kernel_regularizer=l2(0.02) + )(GlobalAveragePooling2D()(base_model1_out)), + Dense(512, + activation='relu', + kernel_regularizer=l2(0.02) + )(GlobalAveragePooling2D()(base_model2_out))]) + + # adding CDL + Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined) + Dropout_L1 = Dropout(0.4)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + combo_model = Model(inputs=common_input, outputs=predictions) + print('Total model layers: ', len(combo_model.layers)) + + #OPT/compile + opt = SGD(momentum=0.9) + combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return combo_model + +print('Creating the model...') +# Main +freeze_layers_1 = 0 +freeze_layers_2 = 0 +model = Combo_Model(freeze_layers_1, freeze_layers_2) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.4 +# ``` +# recommended: ⚠️ +# statuses: Test +# Working: βœ… +# Max fine tuned acc: ⚠️ +# Max fine tuned acc TLRev2: β‰…95.64 +# type: transfer learning>>>(EfficientNetV2XL) +# ``` + +# %% +from keras_efficientnet_v2 import EfficientNetV2XL + +EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4) +# define new model +model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs) + +# compile model +opt = SGD(momentum=0.9) +# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False) +# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +# opt = Adam() +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + +freeze_layers = 0 +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### Rev1.5 (The best one) +# ``` +# recommended: βœ… +# statuses: Ready +# Working: βœ… +# Max fine tuned acc: 95.3 +# Max fine tuned acc TLRev2: 97.12 +# type: transfer learning>>>(EfficientNetB4::CCL) +# ``` + +# %% +from efficientnet.keras import EfficientNetB4 as KENB4 +# FUNC +def Eff_B4_NS(freeze_layers): + base_model = KENB4(input_shape=( + img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / + len(base_model.layers)) * 100 + print( + f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL>>> + #GlobalAveragePooling2D + base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) + #Dense + Dense_L1 = Dense(512, activation='relu', + kernel_regularizer=l2(0.02), + name='FC_C_Dense-L1-512' + )(base_model_FT) + #Dropout + Dropout_L1 = Dropout(0.1, + name='FC_C_Dropout-L1-0.1' + )(Dense_L1) + #BatchNormalization + BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' + )(Dropout_L1) + #Dense + Dense_L2 = Dense(512, activation='relu', + kernel_regularizer=l2(0.01), + name='FC_C_Dense-L2-512' + )(BatchNorm_L2) + #BatchNormalization + BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' + )(Dense_L2) + #Dense + Dense_L3 = Dense(128, activation='relu', + name='FC_C_Dense-L3-128' + )(BatchNorm_L3) + #Dense + # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) + predictions = Dense(2, activation='softmax', + name='FC_OUTPUT_Dense-2')(Dense_L3) + # CDL<<< + model_EfficientNetB7_NS = Model( + inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + # OPT/compile + opt = SGD(momentum=0.9, nesterov=False) + # opt = Nadam() + # opt = Adamax() + # opt = RMSprop(momentum=0.9) + # opt = Adagrad() + # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B4_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta + +# %% +from efficientnet.keras import EfficientNetL2 as KENBL2 +#FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]), + weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5', + include_top=False, + drop_connect_rate=0) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100 + print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model.output) + Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta2 + +# %% +from efficientnet.keras import EfficientNetB7 as KENB7 +# FUNC +def Eff_B7_NS(freeze_layers): + base_model = KENB7(input_shape=( + img_res[0], img_res[1], img_res[2]), weights=None, include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / + len(base_model.layers)) * 100 + print( + f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL>>> + #GlobalAveragePooling2D + base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) + #Dense + Dense_L1 = Dense(512, activation='relu', + kernel_regularizer=l2(0.02), + name='FC_C_Dense-L1-512' + )(base_model_FT) + #Dropout + Dropout_L1 = Dropout(0.1, + name='FC_C_Dropout-L1-0.1' + )(Dense_L1) + #BatchNormalization + BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-Pooling-L1' + )(Dropout_L1) + #Dense + Dense_L2 = Dense(512, activation='relu', + kernel_regularizer=l2(0.01), + name='FC_C_Dense-L2-512' + )(BatchNorm_L2) + #BatchNormalization + BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-Pooling-L2' + )(Dense_L2) + #Dense + Dense_L3 = Dense(128, activation='relu', + name='FC_C_Dense-L3-128' + )(BatchNorm_L3) + #Dense + # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) + predictions = Dense(2, activation='softmax', + name='FC_OUTPUT_Dense-2')(Dense_L3) + # CDL<<< + model_EfficientNetB7_NS = Model( + inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + # OPT/compile + opt = SGD(momentum=0.9, nesterov=False) + # opt = Nadam() + # opt = Adamax() + # opt = RMSprop(momentum=0.9) + # opt = Adagrad() + # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B7_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta3 + +# %% +from keras.applications import ConvNeXtXLarge +from keras.layers import Lambda +#FUNC +def Eff_B7_NS(): + # Add a Lambda layer at the beginning to scale the input + input = Input(shape=(img_res[0], img_res[1], img_res[2])) + x = Lambda(lambda image: image * 255)(input) + + base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x) + # adding CDL + base_model_FT = GlobalAveragePooling2D()(base_model) + Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT) + Dropout_L1 = Dropout(0.1)(Dense_L1) + BatchNorm_L2 = BatchNormalization()(Dropout_L1) + Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2) + BatchNorm_L3 = BatchNormalization()(Dense_L2) + Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3) + predictions = Dense(2, activation='softmax')(Dense_L3) + + model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + #OPT/compile + opt = SGD(momentum=0.9) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +model = Eff_B7_NS() +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### V(T) Beta4 + +# %% +from efficientnet.keras import EfficientNetB4 as KENB4 +# FUNC +def Eff_B4_NS(freeze_layers): + base_model = KENB4(input_shape=( + img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False) + print('Total layers in the base model: ', len(base_model.layers)) + print(f'Freezing {freeze_layers} layers in the base model...') + # Freeze the specified number of layers + for layer in base_model.layers[:freeze_layers]: + layer.trainable = False + + # Unfreeze the rest + for layer in base_model.layers[freeze_layers:]: + layer.trainable = True + + # Calculate the percentage of the model that is frozen + frozen_percentage = ((freeze_layers + 1e-10) / + len(base_model.layers)) * 100 + print( + f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%') + # adding CDL>>> + #GlobalAveragePooling2D + base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output) + #Dense + Dense_L1 = Dense(512, activation='relu', + kernel_regularizer=l2(0.02), + name='FC_C_Dense-L1-512' + )(base_model_FT) + #Dropout + Dropout_L1 = Dropout(0.1, + name='FC_C_Dropout-L1-0.1' + )(Dense_L1) + #BatchNormalization + BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1' + )(Dropout_L1) + #Dense + Dense_L2 = Dense(512, activation='relu', + kernel_regularizer=l2(0.01), + name='FC_C_Dense-L2-512' + )(BatchNorm_L2) + #BatchNormalization + BatchNorm_L3 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L2' + )(Dense_L2) + #Dense + Dense_L3 = Dense(128, activation='relu', + name='FC_C_Dense-L3-128' + )(BatchNorm_L3) + #Dense + # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3) + predictions = Dense(2, activation='softmax', + name='FC_OUTPUT_Dense-2')(Dense_L3) + # CDL<<< + model_EfficientNetB7_NS = Model( + inputs=base_model.input, outputs=predictions) + print('Total model layers: ', len(model_EfficientNetB7_NS.layers)) + # OPT/compile + opt = SGD(momentum=0.9, nesterov=False) + # opt = Nadam() + # opt = Adamax() + # opt = RMSprop(momentum=0.9) + # opt = Adagrad() + # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False) + # opt = Yogi() + model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy + + return model_EfficientNetB7_NS + +print('Creating the model...') +# Main +freeze_layers = 0 +model = Eff_B4_NS(freeze_layers) +model.summary(show_trainable=True, expand_nested=True) +print('done.') + +# %% [markdown] +# ### LR FINDER + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF/Other +LRF_OPT = SGD(momentum=0.9) +LFR_batch_size = 1 # or any other batch size that fits in your memory +LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size) +# Instantiate LrFinder +lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy) + +# Start range_test +lr_find.range_test(LRF_dataset) +lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True) + +# %% [markdown] +# ### Model vis + +# %% +dot_img_file = 'model_1.png' +keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True) + +# %% [markdown] +# ### Model Save (Beta) + +# %% +# Copyright (c) 2024 Aydin Hamedi +# +# This software is released under the MIT License. +# https://opensource.org/licenses/MIT +import json +import numpy as np +from keras.models import model_from_json +from keras.optimizers import get as get_optimizer + +def save_model(model, optimizer, filename): + """ + Save a Keras model's architecture and weights into a single gzipped file. + + Args: + model (tf.keras.Model): The Keras model to save. + optimizer (str): The name of the Keras optimizer to use. + filename (str): The filename to use for the saved file. + """ + # Save the architecture, weights and optimizer into a dictionary + model_dict = { + 'architecture': model.to_json(), + 'weights': [w.tolist() for w in model.get_weights()], + 'optimizer': optimizer.get_config()['name'] + } + + # Write the dictionary to a gzipped file + with gzip.GzipFile(f'{filename}.gz', 'w') as f: + f.write(json.dumps(model_dict).encode('utf-8')) + +def load_model(filename): + """ + Load a Keras model's architecture and weights from a gzipped file. + + Args: + filename (str): The filename of the saved file. + + Returns: + tf.keras.Model: The loaded Keras model. + """ + # Read the dictionary from the gzipped file + with gzip.GzipFile(f'{filename}.gz', 'r') as f: + model_dict = json.loads(f.read().decode('utf-8')) + + # Create a model from the architecture + model = model_from_json(model_dict['architecture']) + + # Set the model's weights + model.set_weights([np.array(w) for w in model_dict['weights']]) + + # Get the optimizer + optimizer = get_optimizer(model_dict['optimizer']) + + # Compile the model with the loaded optimizer + model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) + + return model + +save_model(model, SGD(), 'PAI_model_REV2') + +# %% [markdown] +# ## Loading the model + +# %% [markdown] +# ### Loading the full model + +# %% +import efficientnet.tfkeras +# Configuration +PRMC = False +freeze_from_opposite = False +Extra_EXT = '_T' +freeze_layers = 0 +randomly_frozen_layers = 0 +freeze_last_seven = False +# CEC_opt = Adagrad() +# CEC_opt = Yogi() +# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3) +CEC_opt = SGD(momentum=0.9, nesterov=False) +# CEC_opt = Adam() +# Main +try: + if SAVE_TYPE == 'TF': + model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC) + else: + model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC) +except (ImportError, IOError) as e: + print(f'\033[91mfailed to load the model ERROR:\n{e}') +else: + print('\033[92mLoading model done.') + if not PRMC: + print('Compiling the AI model...\033[0m') + + for layer in model.layers: + layer.trainable = True + + # Select random layers to freeze + frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers) + + for i, layer in enumerate(model.layers): + if i in frozen_layer_indices: + layer.trainable = False + else: + if freeze_from_opposite and (i > len(model.layers) - freeze_layers): + layer.trainable = False + elif (not freeze_from_opposite) and i < freeze_layers: + layer.trainable = False + else: + layer.trainable = True + + for layer in model.layers[-7:]: + layer.trainable = not freeze_last_seven + + model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy']) + model.summary(show_trainable=True, expand_nested=True) + print('done.') + +# %% [markdown] +# ### Loading model weights + +# %% +model.load_weights('PAI_model_weights.h5') +print('done.') + +# %% [markdown] +# ### Reset FC + +# %% +for layer in model.layers[-7:]: + if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'): + weight_initializer = layer.kernel_initializer + bias_initializer = layer.bias_initializer + + old_weights, old_biases = layer.get_weights() + + layer.set_weights([ + weight_initializer(shape=old_weights.shape), + bias_initializer(shape=len(old_biases)) + ]) + + +# %% [markdown] +# ## Training + +# %% [markdown] +# #### Rev2 (THE BEST) +# ``` +# Working: βœ… +# Other: +# + Tensorboard works. +# + Perverts overfitting. +# + Lower memory usage. +# - Slow training. +# + Achieving higher acc. +# - Some models dont work. +# ``` +# - TODO: +# - add Pruning + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +# CONF <--------------------------------------------------------------------------> +# Hyperparameters for training the model: +max_epoch = 384 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models. +subset_epoch = 6 # subset_epoch: Number of epochs to train each subset. +subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs. +PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only. Common values: 8, 16, 26, 32, 64, 128. +subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192. +Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size. +RES_Train = False # RES_Train: Resume training if True. +MAX_LR = 0.011 # MAX_LR: Maximum learning rate. +DEC_LR = 0.00003 # DEC_LR: Learning rate decay. +MIN_LR = 0.0005 # MIN_LR: Minimum learning rate. +RES_LR = 0.006 # RES_LR: Resuming learning rate. +OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True) +Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True. +Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch). +TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage. +SAVE_FULLM = True # SAVE_FULLM: Save full model if True. +USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True. +AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True. +AdvSubsetC_SHR = 42 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs). +load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy. +load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'. +load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)). +load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True. +ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint. +ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf'). +Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage. +Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️). +EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️). +Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler. +Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work). +Use_tensorBoard_img = True # Use_tensorBoard_img: Enable tensorboard image logging. +Show_confusion_matrix_tensorBoard = False # Show_confusion_matrix_tensorBoard: Show confusion matrix on tensorboard. +BEST_RSN = 'PAI_model_T' # Best model save name prefix. (Uses a lot of memory and storage). +ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow). +IMAGE_GEN_PATH = 'Data\\image_SUB_generator.pkl' +# CONF END <----------------------------------------------------------------------> +#Prep +if RES_Train: + MAX_LR = RES_LR + PL_epoch = 1 +#VAR +Total_SUB_epoch_C = 0 # TO FIX TensorBoard +CU_LR = MAX_LR +all_histories = [] +chosen_indices = [] +subset_sizes = [] +best_acc = 0 +best_loss = float('inf') +#Funcs +def normalize_TO_RANGE(arr, min_val, max_val): + arr = arr.astype('float32') + arr = (arr - arr.min()) / (arr.max() - arr.min()) + arr = arr * (max_val - min_val) + min_val + return arr + +def Z_SCORE_normalize(arr): + arr = arr.astype('float32') + mean = np.mean(arr) + std_dev = np.std(arr) + arr = (arr - mean) / std_dev + return arr + +def add_image_grain_TRLRev2(image, intensity = 0.01): + # Generate random noise array + noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \ + + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2 + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +# noise_func_TRLRev2 ([REV1 OLD]) +if not USE_REV2_DP: + def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.08, 0.08) + intensityL1 = random.uniform(-0.05, 0.05) + else: + intensityL2 = random.uniform(-0.09, 0.09) + intensityL1 = random.uniform(-0.06, 0.06) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 112) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.07) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +# noise_func_TRLRev2 ([REV2 NEW]) +else: + def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.07, 0.07) + intensityL1 = random.uniform(-0.06, 0.06) + else: + intensityL2 = random.uniform(-0.09, 0.09) + intensityL1 = random.uniform(-0.07, 0.07) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 112) + + for channel in range(3): # Iterate over each RGB channel + image_channel = image[:, :, channel] + new_image_channel = new_image[:, :, channel] + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image_channel.shape[0], block_size_L2): + for j in range(0, image_channel.shape[1], block_size_L2): + block = image_channel[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block + image_channel = new_image_channel + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image_channel.shape[0], block_size_L1): + for j in range(0, image_channel.shape[1], block_size_L1): + block = image_channel[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block + + new_image[:, :, channel] = new_image_channel + + if add_img_grain: + intensity = random.uniform(0, 0.05) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +#CONST +train_SUB_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.18, + shear_range=0.18, + width_shift_range=0.18, + brightness_range=(0.82, 1.18), + height_shift_range=0.18, + channel_shift_range=100, + featurewise_center=True, + featurewise_std_normalization=True, + zca_whitening=False, + interpolation_order=2, + fill_mode='nearest', + preprocessing_function=noise_func_TRLRev2 + ) +class TerminateOnHighTemp(tf.keras.callbacks.Callback): + def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60): + super().__init__() + self.active = active + self.check_every_n_batches = check_every_n_batches + self.high_temp = high_temp + self.low_temp = low_temp + self.pause_time = pause_time + self.batch_counter = 0 + + def on_batch_end(self, batch, logs=None): + if not self.active: + return + self.batch_counter += 1 + if self.batch_counter % self.check_every_n_batches == 0: + temperature = gpu_control.get_temperature() + if temperature > self.high_temp: + print_Color(f'\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False) + time.sleep(self.pause_time) + while gpu_control.get_temperature() > self.low_temp: + time.sleep(4) + print_Color('Resuming training...', ['yellow']) +class ExtendedTensorBoard(TensorBoard): + def on_epoch_end(self, epoch, logs=None): + logs = logs or {} + logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr) + logs['momentum'] = self.model.optimizer.momentum + super().on_epoch_end(epoch, logs) +class DummyCallback(Callback): + pass +# Define a function to plot the confusion matrix +def plot_confusion_matrix_TensorBoard(epoch, logs): + # Use the model to predict the values from the test dataset. + test_pred_raw = model.predict(x_test, verbose=0) + test_pred = np.argmax(test_pred_raw, axis=1) # Convert predictions from one-hot encoded to binary + + # Convert true labels from one-hot encoded to binary + y_true = np.argmax(y_test, axis=1) + + # Calculate the confusion matrix. + cm = confusion_matrix(y_true, test_pred) + + # Log the confusion matrix as an image summary. + figure = plt.figure(figsize=(8, 8)) + sns.heatmap(cm, annot=True, fmt="d", cmap=plt.cm.Blues) + buf = io.BytesIO() + plt.savefig(buf, format='png') + plt.close(figure) + buf.seek(0) + # Convert PNG buffer to TF image + image = tf.image.decode_png(buf.getvalue(), channels=4) + # Add the batch dimension + image = tf.expand_dims(image, 0) + # Add image summary + with file_writer.as_default(): + tf.summary.image("Confusion Matrix", image, step=epoch) +steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 +#callbacks>>> +# EarlyStopping +early_stopping = EarlyStopping(monitor='val_accuracy', + patience=EarlyStopping_P, + verbose=1, restore_best_weights=True, + mode='max' + ) if Use_ES_ONSUBT else DummyCallback() +# ModelCheckpoint +checkpoint_SUB = ModelCheckpoint(f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', + monitor=load_SUB_BRW_MODE, + save_best_only=True, mode=ModelCheckpoint_mode, + save_weights_only = True + ) if load_SUB_BRW else DummyCallback() +checkpoint_SUB.best = ModelCheckpoint_Reset_TO +# TerminateOnHighTemp +TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M, + check_every_n_batches=6, + high_temp=73, + low_temp=58, + pause_time=60) +# confusion_matrix_callback +confusion_matrix_callback = LambdaCallback(on_epoch_end=plot_confusion_matrix_TensorBoard) if Show_confusion_matrix_tensorBoard else DummyCallback() +# TensorBoard +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +file_writer = tf.summary.create_file_writer(log_dir) +if Use_extended_tensorboard: + tensorboard_callback = ExtendedTensorBoard( + log_dir=log_dir, + write_images=Use_tensorBoard_img, + histogram_freq=1, + update_freq='epoch', + write_grads=True, + profile_batch='256,512' if Use_tensorboard_profiler else 0 + ) +else: + tensorboard_callback = TensorBoard( + log_dir=log_dir, + write_images=Use_tensorBoard_img, + histogram_freq=1, + update_freq='epoch', + write_grads=True, + profile_batch='256,512' if Use_tensorboard_profiler else 0 + ) +# OneCycleLr +if OneCycleLr_UFTS: + learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR, + steps_per_epoch=steps_per_epoch_train_SUB, + epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) +#PRES +# ... +#MAIN +print('Training the model...') +# INFOp +print_Color('\nSetup Verbose:', ['yellow']) +print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True) +#warnings +P_warning('RES_Train is True.') if RES_Train else None +print_Color('Setup Verbose END.', ['yellow']) +# MAIN LOOP +try: + for epoch in range(1, max_epoch): + # Start Epoch + STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' + C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT + if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1 + start_FULL_time = time.time() + if Auto_clear_cache: + subprocess.run(["Cache_clear.cmd"], shell=True) + # TSEC: Total-Subset-Epoch-Count + print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True) + # DP + if not AdvSubsetC: + print_Color('Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + if AdvSubsetC: + if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0: + print_Color('└───Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + chosen_indices = [] # Reset chosen_indices + + available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices)) + + if len(available_indices) < subset_size: + #DEBUG + # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.') + # If there are not enough available indices, choose from the indices that were chosen the longest time ago + old_indices = chosen_indices[:subset_size - len(available_indices)] + subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False)) + + # Update the list of chosen indices and their sizes + chosen_indices = chosen_indices[len(old_indices):] + subset_indices + subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices) + else: + subset_indices = list(np.random.choice(available_indices, subset_size, replace=False)) + + # Add the chosen indices to the list of already chosen indices + chosen_indices += subset_indices + subset_sizes += [subset_size] * len(subset_indices) + else: + subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) + # Taking the subset + x_SUB_train = x_train[subset_indices] + y_SUB_train = y_train[subset_indices] + x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train) + assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}' + print_Color('Preparing train data...', ['yellow']) + # if epoch == 1: # OLD + # print_Color('- ImageDataGenerator fit...', ['yellow']) + # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) + # print_Color('- ImageDataGenerator fit done.', ['yellow']) + if epoch == 1 or ALWAYS_REFIT_IDG == 2: + if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG: + print_Color('- Loading fitted ImageDataGenerator...', ['yellow']) + train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) + else: + print_Color('- Fitting ImageDataGenerator...', ['yellow']) + IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12 + train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6) + pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb')) + print_Color('- ImageDataGenerator fit done.', ['yellow']) + + print_Color('- Augmenting Image Data...', ['yellow']) + train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, + y_SUB_train, + shuffle=False, + batch_size=len(x_SUB_train) + ).next() + print_Color('- Normalizing Image Data...', ['yellow']) + x_SUB_train = normalize_TO_RANGE(train_SUB_augmented_images[0], 0, 255) + x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 0.5) / 255 + # x_SUB_train = x_SUB_train / 255 + x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1) + y_SUB_train = train_SUB_augmented_images[1] + # DEBUG + if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1): + SITD = np.random.choice(subset_size, size=400, replace=False) + S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True) + save_images_to_dir(np.clip(x_SUB_train[SITD], 0, 1), y_SUB_train[SITD], S_dir) + # learning_rate_schedule_SUB + if PL_epoch == 0: + CU_LR = MIN_LR + elif epoch >= PL_epoch and CU_LR > MIN_LR: + if (CU_LR - DEC_LR) < MIN_LR: + CU_LR = MIN_LR + else: + CU_LR -= DEC_LR + if not OneCycleLr_UFTS: + learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR, + steps_per_epoch=steps_per_epoch_train_SUB, + epochs=C_subset_epoch) + #FV + print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + "~*~*") if not OneCycleLr_UFTS else "~*OneCycleLr_UFTS Is ON~*"}]~*...', + ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True) + print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + # Train + print_Color('Training on subset...', ['green']) + start_SUBO_time = time.time() + SUB_history = model.fit(x_SUB_train, + y_SUB_train, + epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C) + batch_size=Conf_batch_size_REV2, + validation_data=(x_test, y_test), + verbose='auto', + initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard + callbacks=[ + learning_rate_schedule_SUB, + TerminateOnHighTemp_CB, + checkpoint_SUB, + early_stopping, + tensorboard_callback, + confusion_matrix_callback + ] + ) + end_SUBO_time = time.time() + print_Color('Subset training done.', ['green']) + if load_SUB_BRW_LMODE == 1: + if max(SUB_history.history['val_accuracy']) > best_acc: + load_weights = True + elif min(SUB_history.history['val_loss']) < best_loss: + load_weights = True + else: + load_weights = False + else: + load_weights = True + + if load_SUB_BRW and load_weights: + print_Color('Loading the best weights...', ['yellow']) + # Get the filename of the best weights file + list_of_files = glob.glob('cache\\*.h5') + try: + best_weights_filename = max(list_of_files, key=os.path.getctime) + print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow']) + model.load_weights(best_weights_filename) + except Exception as Err: + print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red']) + elif load_SUB_BRW and (not load_weights): + print_Color_V2(f'Not loading weights[BSR:acc{{{max(SUB_history.history["val_accuracy"]):.4f}}}, loss{{{min(SUB_history.history["val_loss"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]') + all_histories.append(SUB_history.history) + checkpoint_SUB.best = ModelCheckpoint_Reset_TO + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Evaluate the model on the test data + evaluation = model.evaluate(x_test, y_test, verbose=0) + + # Extract the loss and accuracy from the evaluation results + loss = evaluation[0] + acc = evaluation[1] + print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True) + print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True) + # If the accuracy is higher than the best_acc + if acc > best_acc: + print_Color_V2(f'Improved model accuracy from{best_acc:10f} to {acc:10f}. Saving model.') + # Update the best_acc + best_acc = acc + if SAVE_FULLM: + # Save the model + if SAVE_TYPE == 'TF': + print_Color_V2(f'Saving full model tf format...') + model.save(BEST_RSN, save_format='tf') + else: + print_Color_V2(f'Saving full model H5 format...') + model.save(f'{BEST_RSN}.h5') + model.save_weights('PAI_model_weights.h5') + else: + print_Color_V2(f'Model accuracy did not improve from {best_acc:.10f}. Not saving model.') + + # If the loss is higher than the best_loss + if loss < best_loss: + print_Color_V2(f'Improved model loss from {best_loss:.10f} to {loss:.10f}. Saving model.') + + # Update the best_acc + best_loss = loss + + if SAVE_FULLM: + # Save the model + if SAVE_TYPE == 'TF': + print_Color_V2(f'Saving full model tf format...') + model.save(BEST_RSN + '_BL', save_format='tf') + else: + print_Color_V2(f'Saving full model H5 format...') + model.save(f'{BEST_RSN}_BL.h5') + model.save_weights('PAI_model_weights_BL.h5') + else: + print_Color_V2(f'Model loss did not improve from {best_loss:.10f}. Not saving model.') + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Epoch end + end_time = time.time() + epoch_time = end_time - start_FULL_time + print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec') + epoch_SUB_time = end_SUBO_time - start_SUBO_time + print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec') + epoch_OTHERO_time = epoch_time - epoch_SUB_time + print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec') + print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) + Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard +except KeyboardInterrupt: + print('\nKeyboardInterrupt.') +# End +try: + history = {} + for key in all_histories[0].keys(): + # For each metric, concatenate the values from all histories + history[key] = np.concatenate([h[key] for h in all_histories]) +except Exception as Err: + print(f'Failed to make model `history` var.\nERROR: {Err}') + +print('Training done.\n') +# del vars +try: + del train_SUB_datagen + del train_SUB_augmented_images +except NameError: + pass + +# %% [markdown] +# #### Rev1 (⚠️deprecated⚠️) +# ``` +# Working: βœ… +# Other: +# + Tensorboard works. +# - Can cause overfitting. +# ``` + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF +Conf_batch_size = 8 +OneCycleLr_epoch = 20 +Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) +#TensorBoard conf +TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) +# Learning rate configuration +Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr +MAX_LR = 0.0174 +# First time +if Learning_rate_conf == 1: + learning_rate_start = 8e-04 + learning_rate_max = 5e-03 + learning_rate_min = 5e-05 + learning_rate_rampup_epochs = 5 + learning_rate_sustain_epochs = 1 + learning_rate_exp_decay = .3 + #TEMP + # learning_rate_start = 8e-04 + # learning_rate_max = 1e-02 + # learning_rate_min = 8e-04 + # learning_rate_rampup_epochs = 5 + # learning_rate_sustain_epochs = 3 + # learning_rate_exp_decay = .45 +# 2th time +if Learning_rate_conf == 2: + if Learning_rate_conf_SET2C == 1: + learning_rate_start = 4.10e-06 + learning_rate_max = 4.10e-06 + learning_rate_min = 4.10e-06 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 2: + learning_rate_start = 4e-07 + learning_rate_max = 4e-07 + learning_rate_min = 4e-07 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 3: + learning_rate_start = 5e-04 + learning_rate_max = 5e-04 + learning_rate_min = 5e-04 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 +# Function to build learning rate schedule +if Learning_rate_conf in [1,2]: + def build_learning_rate_fn(lr_start=learning_rate_start, + lr_max=learning_rate_max, + lr_min=learning_rate_min, + lr_rampup_epochs=learning_rate_rampup_epochs, + lr_sustain_epochs=learning_rate_sustain_epochs, + lr_exp_decay=learning_rate_exp_decay): + lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync + def learning_rate_fn(epoch): + if epoch < lr_rampup_epochs: + lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start + elif epoch < lr_rampup_epochs + lr_sustain_epochs: + lr = lr_max + else: + lr = (lr_max - lr_min) *\ + lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min + return lr + return learning_rate_fn + +# Calculate steps per epoch +steps_per_epoch_train = len(x_train) // Conf_batch_size + +# Set up callbacks +class EpochEndMON(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs=None): + optimizer = self.model.optimizer + if hasattr(optimizer, 'lr'): + lr = tf.keras.backend.get_value(optimizer.lr) + print(f'\nLearning rate for epoch {epoch+1} is {lr}') + if hasattr(optimizer, 'momentum'): + momentum = tf.keras.backend.get_value(optimizer.momentum) + print(f'Momentum for epoch {epoch+1} is {momentum}') + if logs: + val_loss = logs.get('val_loss') + val_acc = logs.get('val_accuracy') + print(f'Validation loss for epoch {epoch+1} is {val_loss}') + print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') + + print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') + +# Instantiate the callback +EpochEndMON_callback = EpochEndMON() +if Learning_rate_conf in [1,2]: + learning_rate_fn = build_learning_rate_fn() + learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) +else: + learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch) +if SAVE_TYPE == 'TF': + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +else: + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True) +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' +tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True) + +# Train the model +print('Log dir:', log_dir) +#MInfo +print('Input Shape:', model.input_shape) +print('Output Shape:', model.output_shape) +print('Loss Function:', model.loss) +print('Training the model...\n') +history = model.fit(x_train, + y_train, + epochs=256, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) +print('Training done.\n') + +# %% [markdown] +# ## Saving model weights +# + +# %% +Extra_EXT = '_T' +# Save the weights +print('Saving weights...') +model.save_weights('PAI_model_weights.h5') +print('Saving full model...') +if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') +else: + try: + model.save(f'PAI_model{Extra_EXT}.h5') + except ValueError: + print('failed to save in .h5 format!') + print('Saving full model in tf format...') + model.save(f'PAI_model{Extra_EXT}', save_format='tf') + +# %% [markdown] +# ## Garbage Collection (memory) + +# %% +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() + +# %% [markdown] +# ## Analyse model Training performance + +# %% +# Save history +save_list(history, 'history\\model_history.pkl.gz', compress=True) + +# %% +# load history +history = load_list('history\\model_history.pkl.gz', compressed=True) + +# %% +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns + +# Chunk size for 3D plot +chunk_size = 6 # Change this to your desired chunk size + +def convert_history(history): + if isinstance(history, tf.keras.callbacks.History): + return history.history + else: + return history + +def chunked_data(data, chunk_size): + return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)] + + +try: + EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' + history = convert_history(history) + + # Calculate deltas + delta_loss = np.diff(history['loss']) + delta_accuracy = np.diff(history['accuracy']) + + try: + delta_val_loss = np.diff(history['val_loss']) + delta_val_accuracy = np.diff(history['val_accuracy']) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') + + plt.figure(figsize=(16, 10)) + # Loss + plt.subplot(2, 2, 1) + plt.plot(history['loss'], label='loss') + try: + plt.plot(history['val_loss'], label='val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_loss.') + plt.title('Model Loss') + plt.ylabel('Loss') + plt.xlabel(EPM) + plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + # Density plot for loss + plt.subplot(2, 2, 2) + plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) + try: + plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss (density plot).') + plt.title('Density Plot for Loss') + plt.xlabel('Loss') + plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + + # Accuracy + plt.subplot(2, 2, 3) + plt.plot(history['accuracy'], label='accuracy') + try: + plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy.') + plt.title('Model Accuracy') + plt.ylabel('Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Density plot for accuracy + plt.subplot(2, 2, 4) + plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) + try: + plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy (density plot).') + plt.title('Density Plot for Accuracy') + plt.xlabel('Accuracy') + plt.grid(True) + + # Delta Loss + plt.figure(figsize=(14, 8)) + plt.subplot(2, 2, 1) + plt.plot(delta_loss, label='delta_loss') + try: + plt.plot(delta_val_loss, label='delta_val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_loss.') + plt.title('Delta Model Loss') + plt.ylabel('Delta Loss') + plt.ylim(top=1.5, bottom=-1.5) + plt.xlabel(EPM) + plt.grid(True) + # Delta Accuracy + plt.subplot(2, 2, 2) + plt.plot(delta_accuracy, label='delta_accuracy') + try: + plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_accuracy.') + plt.title('Delta Model Accuracy') + plt.ylabel('Delta Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Calculate chunked data + chunked_loss = chunked_data(history['val_loss'], chunk_size) + chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) + + # Clip the loss values to a maximum of max(history['val_loss'][10:]) + max_loss = max(history['val_loss'][10:]) + chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) + + # Create 3D surface plots for each chunk + fig = plt.figure(figsize=(14, 8)) + ax = fig.add_subplot(121, projection='3d') + X = np.arange(len(chunked_loss)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Loss') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Loss') + + ax = fig.add_subplot(122, projection='3d') + X = np.arange(len(chunked_accuracy)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Accuracy') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Accuracy') + + # Function to calculate the average of chunks + def chunked_average(values, chunk_size): + return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] + + avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) + avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) + + # Find the chunk with the highest average accuracy + max_acc_chunk_index = np.argmax(avg_accuracy_chunks) + max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] + + # Create a pile plot for accuracy + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy') + plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') + plt.xlabel('Chunk') + plt.ylabel('Average Accuracy') + plt.title('Average Validation Accuracy per Chunk') + plt.legend() + + # Create a pile plot for loss + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') + plt.xlabel('Chunk') + plt.ylabel('Average Loss') + plt.title('Average Validation Loss per Chunk') + plt.legend() + + # Function to calculate the average of each epoch across chunks, ignoring the first chunk + def average_across_chunks(values, chunk_size): + num_chunks = len(values) // chunk_size + avg_values = [] + for epoch in range(chunk_size): + epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] + avg_values.append(np.mean(epoch_values)) + return avg_values + + # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk + avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) + avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) + + # Create a bar plot for average accuracy and loss of each epoch across chunks + plt.figure(figsize=(12, 6)) + + # Create an index for each epoch + epoch_indices = np.arange(len(avg_accuracy_epochs)) + + # Plot accuracy and loss as bars + plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) + plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) + + # Add labels and title + plt.xlabel('Epoch (within chunk)') + plt.ylabel('Average Value') + plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') + plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers + plt.legend() + + plt.tight_layout() + plt.show() + +except (ValueError, NameError) as E: + print(f'\033[91mFailed to load model history.\nError: {E}') + +# %% [markdown] +# ## Analyse model Predicting performance + +# %% +import seaborn as sns +from sklearn.metrics import confusion_matrix, accuracy_score +from scipy.stats import binom +from tqdm import tqdm +import efficientnet.tfkeras +import cv2 +import gc +# Garbage Collection (memory) +gc.collect() + +Extra_EXT = '_T' # _T or _T_BL +Train_data_test = False +if SAVE_TYPE == 'TF': + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}') +else: + # Load the pre-trained model + model = load_model(f'PAI_model{Extra_EXT}.h5') + +# Ensure the model's input_shape matches your data +assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.' + +# Make predictions on validation data +val_predictions = model.predict(x_val) +val_predictions = np.argmax(val_predictions, axis=1) + +# Make predictions on Train data +if Train_data_test: + Train_predictions = model.predict(x_train) + Train_predictions = np.argmax(Train_predictions, axis=1) + +# Make predictions on test data +test_predictions = model.predict(x_test) +test_predictions = np.argmax(test_predictions, axis=1) + +# Convert y_val and y_test from one-hot encoder to their original form +y_val_original = np.argmax(y_val, axis=1) +y_test_original = np.argmax(y_test, axis=1) +if Train_data_test: + y_train_original = np.argmax(y_train, axis=1) + +# Calculate accuracy on validation data +val_accuracy = accuracy_score(y_val_original, val_predictions) + +# Calculate accuracy on Train data +if Train_data_test: + Train_accuracy = accuracy_score(y_val_original, Train_predictions) + +# Calculate accuracy on test data +test_accuracy = accuracy_score(y_test_original, test_predictions) + +# Print acc +if Train_data_test: + print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}({Train_accuracy:.5%})') +print(f'The accuracy of the model on validation data is {val_accuracy:.2%}({val_accuracy:.5%})') +print(f'The accuracy of the model on test data is {test_accuracy:.2%}({test_accuracy:.5%})') + +# Visualize the predictions on validation data as a grid of squares +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + plt.imshow(x_val[i]) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() +#Heatmap +plt.figure(figsize=(12, 6)) +for i in range(10): + plt.subplot(2, 5, i+1) + img = x_val[i] + heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) + heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0])) + heatmap = np.uint8(255 * heatmap) + # Apply Adaptive Histogram Equalization + clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object + heatmap = clahe.apply(heatmap) + heatmap = cv2.applyColorMap(np.max(heatmap) - heatmap, cv2.COLORMAP_JET) + if RANGE_NOM: + superimposed_img = (heatmap / 255) * 0.4 + img + else: + superimposed_img = (heatmap / 255) * 0.4 + (img / 255) + #clip + superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1] + plt.imshow(superimposed_img) + plt.title(f'True: {y_val_original[i]}\nPredicted: {val_predictions[i]}') + plt.axis('off') +plt.tight_layout() +plt.show() + +# Define the list of labels +labels = ['NORMAL', 'PNEUMONIA'] + +# Create a confusion matrix for validation data +val_cm = confusion_matrix(y_val_original, val_predictions) + +# Create a confusion matrix for test data +test_cm = confusion_matrix(y_test_original, test_predictions) + +# Plot the confusion matrix as a heatmap for validation data +plt.figure(figsize=(8, 6)) +sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Validation Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Plot the confusion matrix as a heatmap for test data +plt.figure(figsize=(8, 6)) +sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels) +plt.title('Confusion Matrix - Test Data') +plt.xlabel('Predicted') +plt.ylabel('True') +plt.show() + +# Define the range of test data sizes to use +data_sizes = range(1, len(x_test), 4) +# Calculate the probability of a wrong prediction based on test accuracy +prob_wrong = 1 - test_accuracy + +# Create a list to store the number of incorrect predictions for each test data size +incorrect_predictions = [] + +# Generate predictions and track incorrect predictions for each data size +for size in tqdm(data_sizes, desc='Predicting', unit='dpb'): + # Garbage Collection (memory) + gc.collect() + # Randomly select a subset of test data + indices = np.random.choice(len(x_test), size, replace=False) + x_test_subset = x_test[indices] + y_test_subset = y_test[indices] + + # Make predictions on the subset of test data + test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False) + test_predictions = np.argmax(test_predictions, axis=1) + y_test_original_subset = np.argmax(y_test_subset, axis=1) + + # Calculate the number of incorrect predictions + incorrect_preds = np.sum(test_predictions != y_test_original_subset) + incorrect_predictions.append(incorrect_preds) + +# Plot the number of incorrect predictions vs. the number of data points +plt.figure(figsize=(10, 6)) +plt.plot(data_sizes, incorrect_predictions) +plt.xlabel('Number of Data Points') +plt.ylabel('Number of Incorrect Predictions') +# Add gridlines for the x and y axes +plt.grid(True) + +# Change the tick spacing for the x and y axes +plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50)) +plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3)) + +plt.title('Number of Incorrect Predictions vs. Number of Data Points') +plt.show() +# Deprecated⚠️------------------------------>>> +# prob_L = 0.9995 +# # Define the range of test data sizes to use +# data_sizes = range(1, len(x_test), 1) + +# # Calculate the probability of a wrong prediction based on test accuracy +# prob_wrong = 1 - test_accuracy + +# # Create a list to store the probability of getting at least one wrong answer for each test data size +# probabilities = [] + +# # Calculate the probability of getting at least one wrong answer for each data size +# for size in data_sizes: +# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0 +# cdf = binom.cdf(0, size, prob_wrong) +# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer +# prob = 1 - cdf +# probabilities.append(prob) + +# # Find the index of the first data point that has a probability greater than prob_L% +# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities)) + +# # Limit the x-axis to the first data point that has a probability greater than prob_L% +# data_sizes = data_sizes[:index+1] +# probabilities = probabilities[:index+1] + +# # Plot the probability vs. the number of data points +# plt.figure(figsize=(10, 6)) +# plt.plot(data_sizes, probabilities) +# plt.xlabel('Number of Data Points') +# plt.ylabel('Probability') + +# # Add gridlines for the x and y axes +# plt.grid(True) + +# # Change the tick spacing for the x and y axes +# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10)) +# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100)) + +# plt.ylim(top=1.01) + +# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points') +# plt.show() +# Deprecated⚠️------------------------------<<< + + diff --git a/Interface/CLI/CLI.cmd b/Interface/CLI/CLI.cmd index 5923e07..8c4d88d 100644 --- a/Interface/CLI/CLI.cmd +++ b/Interface/CLI/CLI.cmd @@ -1,170 +1,170 @@ -@echo off -REM Conf: -setlocal enabledelayedexpansion -TITLE Pneumonia-Detection-Ai-CLI -set python_min_VER=10 -set DEBUG=0 -set arg=%1 -set PV_filepath="Data\\Python Ver.tmp" -set PUE_filepath="Data\\Use_Python_Embed.tmp" -set Python_Embed_URL="https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases/download/Other-Data-V1/Python.Embed.3.10.11.exe" -set Python_Embed_Name="Python.Embed.3.10.11.exe" -set python_path=python -set pip_path=pip - -REM Check if the fast start flag is used -if "%arg%"=="-f" ( - goto :FAST_START -) - -REM Check if Python is installed -"%python_path%" --version 2>nul >nul -if errorlevel 1 goto :errorNoPython -:errorNoPython_C - -@REM Geting the Python path and Python install time -for /f "delims=" %%p in ('where "%python_path%" 2^>^&1 ^| findstr /v "INFO:"') do ( - set "python_path_env=%%p" -) -if not defined python_path_env ( - set "python_path_env=%python_path%" -) -for %%A in ("%python_path_env%") do ( - set Python_INSTALLTIME=%%~tA -) - -REM Check if the Python version file exists and matches the current Python version -for /F "delims=" %%i IN ('"%python_path%" --version 2^>^&1') DO set current_python_version=%%i -set "current_python_version=%current_python_version% %Python_INSTALLTIME%" -if not exist %PV_filepath% ( - goto :PASS_PVF_CHECK -) -set /p file_python_version=<%PV_filepath% -if "%file_python_version%"=="%current_python_version% " ( - goto :FAST_START -) - -:PASS_PVF_CHECK -REM Write the current Python version to the file -echo Checking Python version... -REM Ensure Python version is %python_min_VER% or higher -for /F "tokens=2 delims=." %%i IN ('"%python_path%" --version 2^>^&1') DO set python_version_major=%%i -if %python_version_major% LSS %python_min_VER% ( - echo Warning: Please update your Python version to 3.%python_min_VER%.x or higher! - pause - exit /B -) - -REM Check if the required packages are installed -echo Checking the required packages... -for /F "usebackq delims==" %%i in ("Data\requirements.txt") do ( - call :check_install %%i -) -REM Write the current Python version + Python install time to the file -echo %current_python_version% > %PV_filepath% -@REM Pause for user input -echo Press any key to load the CLI... -pause > nul - -:FAST_START -REM Print the appropriate loading message -if "%arg%"=="-f" ( - echo Loading the CLI fast... -) else ( - echo Loading the CLI... -) - -:restart -REM Clear the terminal and start the Python CLI script -timeout /t 1 >nul -cls -"%python_path%" "Data\CLI_main.py" - -REM Prompt to restart or quit the CLI -set /p restart="Do you want to restart the CLI or quit the CLI (y/n)? " -if /i "%restart%"=="y" ( - goto :restart -) else ( - goto :EOF -) - -REM Check it 🚧Beta🚧 --- [>>> -:errorNoPython -REM Handle the error if Python is not installed -if exist %PUE_filepath% ( - for /D %%X in ("Data\\Python Embed*") do ( - if exist "%%X\python.exe" ( - if exist "%%X\\Scripts\\pip.exe" ( - set "python_path=%%X\\python.exe" - set "pip_path=%%X\\Scripts\\pip.exe" - echo `Conf file` > %PUE_filepath% - goto :errorNoPython_C - ) - ) - ) - echo Error: Failed to find embedded Python. - echo Error: Python is not installed - del %PUE_filepath% >nul >nul - del %PV_filepath% >nul >nul - pause - goto :EOF -) -echo Error: Python is not installed -set /p UserInput="Do you want to use the embedded Python (y/n)? " -if /I "!UserInput!"=="y" ( - for /D %%X in ("Data\\Python Embed*") do ( - if exist "%%X\python.exe" ( - if exist "%%X\\Scripts\\pip.exe" ( - set "python_path=%%X\\python.exe" - set "pip_path=%%X\\Scripts\\pip.exe" - echo `Conf file` > %PUE_filepath% - goto :errorNoPython_C - ) - ) - ) - echo Error: Failed to find embedded Python. - set /p downloadPython="Do you want to download the embedded Python (y/n)? " - if /I "!downloadPython!"=="y" ( - REM Download the file using curl - echo Downloading the embedded Python... - - curl -L -o %Python_Embed_Name% %Python_Embed_URL% - - REM Extract the file to the Data folder - echo Extracting the embedded Python... - "%Python_Embed_Name%" -o"%cd%\\Data" -y - - REM Delete the original file - echo Deleting the original file... - del "%Python_Embed_Name%" - - REM Restarting the CLI luncher... - echo Restarting the CLI luncher (in 8 seconds^^^)... - timeout /t 8 >nul - start "" "%~f0" - exit - ) -) -pause -goto :EOF -REM Check it 🚧Beta🚧 --- <<<] - -:check_install -REM Check if a package is installed and offer to install it if not -set userinput=Y -"%pip_path%" show %1 >nul -if ERRORLEVEL 1 ( - echo Package %1 not found. Do you want to automatically install it? [Y/n] - set /p userinput="Answer: " - if /I "%userinput%"=="Y" ( - echo Installing package %1 - "%pip_path%" install %1 - if ERRORLEVEL 1 ( - echo Failed to install package %1. - exit /B - ) - ) -) else if "%DEBUG%"=="1" ( - echo Package %1 is already installed. -) -GOTO:EOF +@echo off +REM Conf: +setlocal enabledelayedexpansion +TITLE Pneumonia-Detection-Ai-CLI +set python_min_VER=10 +set DEBUG=0 +set arg=%1 +set PV_filepath="Data\\Python Ver.tmp" +set PUE_filepath="Data\\Use_Python_Embed.tmp" +set Python_Embed_URL="https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases/download/Other-Data-V1/Python.Embed.3.10.11.exe" +set Python_Embed_Name="Python.Embed.3.10.11.exe" +set python_path=python +set pip_path=pip + +REM Check if the fast start flag is used +if "%arg%"=="-f" ( + goto :FAST_START +) + +REM Check if Python is installed +"%python_path%" --version 2>nul >nul +if errorlevel 1 goto :errorNoPython +:errorNoPython_C + +@REM Geting the Python path and Python install time +for /f "delims=" %%p in ('where "%python_path%" 2^>^&1 ^| findstr /v "INFO:"') do ( + set "python_path_env=%%p" +) +if not defined python_path_env ( + set "python_path_env=%python_path%" +) +for %%A in ("%python_path_env%") do ( + set Python_INSTALLTIME=%%~tA +) + +REM Check if the Python version file exists and matches the current Python version +for /F "delims=" %%i IN ('"%python_path%" --version 2^>^&1') DO set current_python_version=%%i +set "current_python_version=%current_python_version% %Python_INSTALLTIME%" +if not exist %PV_filepath% ( + goto :PASS_PVF_CHECK +) +set /p file_python_version=<%PV_filepath% +if "%file_python_version%"=="%current_python_version% " ( + goto :FAST_START +) + +:PASS_PVF_CHECK +REM Write the current Python version to the file +echo Checking Python version... +REM Ensure Python version is %python_min_VER% or higher +for /F "tokens=2 delims=." %%i IN ('"%python_path%" --version 2^>^&1') DO set python_version_major=%%i +if %python_version_major% LSS %python_min_VER% ( + echo Warning: Please update your Python version to 3.%python_min_VER%.x or higher! + pause + exit /B +) + +REM Check if the required packages are installed +echo Checking the required packages... +for /F "usebackq delims==" %%i in ("Data\requirements.txt") do ( + call :check_install %%i +) +REM Write the current Python version + Python install time to the file +echo %current_python_version% > %PV_filepath% +@REM Pause for user input +echo Press any key to load the CLI... +pause > nul + +:FAST_START +REM Print the appropriate loading message +if "%arg%"=="-f" ( + echo Loading the CLI fast... +) else ( + echo Loading the CLI... +) + +:restart +REM Clear the terminal and start the Python CLI script +timeout /t 1 >nul +cls +"%python_path%" "Data\CLI_main.py" + +REM Prompt to restart or quit the CLI +set /p restart="Do you want to restart the CLI or quit the CLI (y/n)? " +if /i "%restart%"=="y" ( + goto :restart +) else ( + goto :EOF +) + +REM Check it 🚧Beta🚧 --- [>>> +:errorNoPython +REM Handle the error if Python is not installed +if exist %PUE_filepath% ( + for /D %%X in ("Data\\Python Embed*") do ( + if exist "%%X\python.exe" ( + if exist "%%X\\Scripts\\pip.exe" ( + set "python_path=%%X\\python.exe" + set "pip_path=%%X\\Scripts\\pip.exe" + echo `Conf file` > %PUE_filepath% + goto :errorNoPython_C + ) + ) + ) + echo Error: Failed to find embedded Python. + echo Error: Python is not installed + del %PUE_filepath% >nul >nul + del %PV_filepath% >nul >nul + pause + goto :EOF +) +echo Error: Python is not installed +set /p UserInput="Do you want to use the embedded Python (y/n)? " +if /I "!UserInput!"=="y" ( + for /D %%X in ("Data\\Python Embed*") do ( + if exist "%%X\python.exe" ( + if exist "%%X\\Scripts\\pip.exe" ( + set "python_path=%%X\\python.exe" + set "pip_path=%%X\\Scripts\\pip.exe" + echo `Conf file` > %PUE_filepath% + goto :errorNoPython_C + ) + ) + ) + echo Error: Failed to find embedded Python. + set /p downloadPython="Do you want to download the embedded Python (y/n)? " + if /I "!downloadPython!"=="y" ( + REM Download the file using curl + echo Downloading the embedded Python... + + curl -L -o %Python_Embed_Name% %Python_Embed_URL% + + REM Extract the file to the Data folder + echo Extracting the embedded Python... + "%Python_Embed_Name%" -o"%cd%\\Data" -y + + REM Delete the original file + echo Deleting the original file... + del "%Python_Embed_Name%" + + REM Restarting the CLI luncher... + echo Restarting the CLI luncher (in 8 seconds^^^)... + timeout /t 8 >nul + start "" "%~f0" + exit + ) +) +pause +goto :EOF +REM Check it 🚧Beta🚧 --- <<<] + +:check_install +REM Check if a package is installed and offer to install it if not +set userinput=Y +"%pip_path%" show %1 >nul +if ERRORLEVEL 1 ( + echo Package %1 not found. Do you want to automatically install it? [Y/n] + set /p userinput="Answer: " + if /I "%userinput%"=="Y" ( + echo Installing package %1 + "%pip_path%" install %1 + if ERRORLEVEL 1 ( + echo Failed to install package %1. + exit /B + ) + ) +) else if "%DEBUG%"=="1" ( + echo Package %1 is already installed. +) +GOTO:EOF diff --git a/Interface/CLI/Data/CLI_main.py b/Interface/CLI/Data/CLI_main.py index 0bc6de2..e2ab616 100644 --- a/Interface/CLI/Data/CLI_main.py +++ b/Interface/CLI/Data/CLI_main.py @@ -1,743 +1,743 @@ -# Copyright (c) 2023 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT - -# start L1 -print('Loading the CLI...', end='\r') -# pylib -import os -import re -import cv2 -import sys -import difflib -import inspect -import traceback -import subprocess -import requests -from tqdm import tqdm -import cpuinfo -from loguru import logger -import efficientnet.tfkeras -from tkinter import filedialog -from datetime import datetime -from PIL import Image -import tensorflow as tf -from keras.models import load_model -from keras.preprocessing.image import ImageDataGenerator -from keras.utils import to_categorical -import numpy as np -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.Grad_cam import make_gradcam_heatmap -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from Utils.Other import * -# global vars>>> -# CONST SYS -CLI_Ver = '0.8.9.3 (CLI)' -Model_dir = 'Data/PAI_model' # without file extention -Database_dir = 'Data/dataset.npy' -IMG_AF = ('JPEG', 'PNG', 'BMP', 'TIFF', 'JPG') -Github_repo_Releases_Model_name = 'PAI_model_T.h5' -Github_repo_Releases_Model_light_name = 'PAI_model_light_T.h5' -Github_repo_Releases_URL = 'https://api.github.com/repos/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest' -Model_FORMAT = 'H5_SF' # TF_dir/H5_SF -IMG_RES = (224, 224, 3) -train_epochs_def = 4 -SHOW_CSAA_OS = False -# normal global -img_array = None -Debug_m = False -label = None -model = None -# Other -logger.remove() -logger.add('Data\\logs\\SYS_LOG_{time}.log', - backtrace=True, diagnose=True, compression='zip') -logger.info('CLI Start...\n') -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) -# HF>>> -# check_args -def check_arg(arg_list: list, arg_str: str, return_arg: bool = False, bool_OUTPUT_ONLY: bool = False): - """ - This function checks if a specific argument exists in a list of arguments. - - Parameters: - arg_list (list): A list of arguments. - arg_str (str): The argument to check for. - return_arg (bool, optional): If True, returns the string after the argument if it exists. Defaults to False. - - Returns: - bool/str: Returns True if the argument exists and return_arg is False. - Returns the string after the argument if return_arg is True and the argument exists. - Returns specific error codes in case of errors. - - Error Codes: - - '![IER:01]': This error is returned when the provided argument list (arg_list) is empty or contains only 'none' or ''. - It indicates that there are no arguments to check against. - - '![IER:02]': This error is returned when the argument to check for (arg_str) is an empty string. - It indicates that there is no argument specified to look for in the argument list. - - '![IER:03]': This error is returned when the argument to check for (arg_str) is found in the argument list (arg_list), - but there is no string after the argument and return_arg is set to True. - It indicates that the function was expected to return the string following the argument, but there was none. - - '![IER:04]': This error is returned when the argument to check for (arg_str) is not found in the argument list (arg_list). - It indicates that the specified argument does not exist in the provided argument list. - - Note: If the bool_OUTPUT_ONLY parameter is set to True, the function will return False instead of these error codes. - """ - - # Error handling - if arg_list == [] or arg_list == ['none'] or arg_list == ['']: - return False if bool_OUTPUT_ONLY else '![IER:01]' - if arg_str == '': - return False if bool_OUTPUT_ONLY else '![IER:02]' - - for item in arg_list: - if item.startswith('-'): - if item[1] == arg_str: - if len(item) == 2 and return_arg: - return False if bool_OUTPUT_ONLY else '![IER:03]' - return True if not return_arg else item[2:] - - return False if bool_OUTPUT_ONLY else '![IER:04]' - - -check_arg_ERROR_LIST_USAGE = ['![IER:02]'] -check_arg_ERROR_LIST_RT = ['![IER:03]'] - -# open_file_GUI -def open_file_GUI(): - """Opens a file selection dialog GUI to allow the user to select an image file. - - Builds a filetypes filter from the IMG_AF global variable, joins the extensions - together into a filter string, converts to lowercase. Opens the file dialog, - and returns the selected file path if one was chosen. - - Returns: - str: The path to the selected image file, or None if no file was chosen. - """ - formats = ";*.".join(IMG_AF) - formats = "*." + formats.lower() - file_path = filedialog.askopenfilename( - filetypes=[("Image Files", formats)]) - if file_path: - return file_path - -# Debug - -# Debug -def Debug(ID, DEBUG_IF, SFL: bool = True, Force: bool = False, SFCS: bool = True): - """ - This function is used for debugging purposes. It prints out various information about the data passed to it. - - Args: - ID (Any): The identifier for the data. This could be any type, but is typically a string. - DEBUG_IF (Any): The data that needs to be debugged. This could be any type. - SFL (bool, optional): A flag to determine if the stack frame location should be included in the debug information. Defaults to True. - Force (bool, optional): A flag to force the debug information to be printed even if the global Debug_m is set to False. Defaults to False. - SFCS (bool, optional): A flag to determine if the function call stack should be included in the debug information. Defaults to True. - - Returns: - None - """ - try: - if Debug_m or Force: - frame_info = inspect.currentframe() - stack_trace = traceback.format_stack() - stack_trace_formated = '' - for line in stack_trace[:-1]: - stack_trace_formated += '--> [!>>>' + line - location = f'{inspect.stack()[1].filename}:{frame_info.f_back.f_lineno}' if SFL else f'L:{frame_info.f_back.f_lineno}' - Debug_data = \ - f'\n~*--> ~*DEBUG INFO id: ~*[{str(ID)}]~*, ' \ - f'Location: ~*[{location}]~*, ' \ - f'time: ~*[{datetime.now().strftime("%Y/%m/%d | %H:%M:%S")}]\n~*--> ~*' \ - f'Data: ~*{str(DEBUG_IF)}\n~*--> ~*' \ - f'Data Type: ~*{type(DEBUG_IF)}\n~*--> ~*' \ - f'Memory Address: ~*DEC>>>~*{id(DEBUG_IF)}~* | HEX>>>~*{hex(id(DEBUG_IF))}~* | BIN>>>~*{bin(id(DEBUG_IF))}\n' - if SFCS: - Debug_data += f'~*--> ~*Function Call Stack: ~*↓\n~*{stack_trace_formated}\n' - print_Color(Debug_data, - ['red', 'magenta', 'green', 'magenta', 'yellow', 'magenta', 'yellow', - 'red', 'magenta', 'yellow', 'red', 'magenta', 'yellow', 'red', 'magenta', - 'cyan', 'yellow', 'cyan', 'yellow', 'cyan', 'yellow', 'red', 'magenta', 'green', - 'yellow'] if SFCS else - ['red', 'magenta', 'green', 'magenta', 'yellow', 'magenta', 'yellow', - 'red', 'magenta', 'yellow', 'red', 'magenta', 'yellow', 'red', 'magenta', - 'cyan', 'yellow', 'cyan', 'yellow', 'cyan', 'yellow'], - advanced_mode=True) - except NameError: - print_Color( - '~*[`Debug` func] --> ERROR: ~*carate a global var named `Debug_m` for turning on and off the Debug func.', - ['red', 'yellow'], advanced_mode=True) - -# download_file_from_github -def download_file_from_github(url: str, file_name: str, save_as: str, chunk_size: int): - """Downloads a file from a GitHub release API URL to a local path. - - Args: - url (str): The GitHub API URL for the release to download from. - file_name (str): The name of the file to download from the release. - save_as (str): The local path to save the downloaded file to. - chunk_size (int): The chunk size to use when streaming the download. - """ - response = requests.get(url) - data = response.json() - logger.debug(f'download_file_from_github:data(json) {data}') - # Get the name of the latest release - release_name = data['name'] - print(f'Latest release: {release_name}') - - # Get the assets of the latest release - assets = data['assets'] - - # Find the required asset in the assets - for asset in assets: - if asset['name'] == file_name: - download_url = asset['browser_download_url'] - break - if 'download_url' in locals(): - # Download the file with a progress bar - response = requests.get(download_url, stream=True) - file_size = int(response.headers['Content-Length']) - progress_bar = tqdm(total=file_size, unit='b', unit_scale=True) - - with open(save_as, 'wb') as f: - for chunk in response.iter_content(chunk_size=chunk_size): - progress_bar.update(len(chunk)) - f.write(chunk) - - progress_bar.close() - - if file_size != 0 and progress_bar.n != file_size: - print_Color('~*ERROR: ~*Something went wrong while downloading the file.', ['red', 'yellow'], advanced_mode=True) - logger.warning('download_file_from_github>>ERROR: Something went wrong while downloading the file.') - else: - print(f"File '{save_as}' downloaded successfully.") - logger.debug(f"download_file_from_github>>Debug: File '{save_as}' downloaded successfully.") - else: - print_Color('~*ERROR: ~*Something went wrong while finding the file.', ['red', 'yellow'], advanced_mode=True) - logger.warning('download_file_from_github>>ERROR: Something went wrong while finding the file.') - -# CF>>> -# CI_help -# change show_lines and SSUH to change the style -def CI_help(SSUH: bool = True, show_lines: bool = True): - """Prints a help message listing available commands. - - This function prints a formatted help message showing the available - commands and their descriptions. It takes two boolean arguments: - - SSUH: Whether to print section headers and formatting. - show_lines: Whether to show line graphics. - - It first prints a header and list of main commands if SSUH is True. - Then it prints a header and list of other commands. - - The commands are printed from the cmd_descriptions and - cmd_descriptions_other dictionaries, with some simple formatting. - """ - # main - if SSUH: - print_Color(f'{("β”Œβ”€ " if show_lines else "")}~*Main (you can run them in order for simple usage):', - ['cyan'], advanced_mode=True) - for i, (cmd, desc) in enumerate(cmd_descriptions.items(), start=1): - if i == len(cmd_descriptions): - print_Color(f'{("β”‚ └─ " if show_lines else "")}~*{i}. {cmd}: ~*{desc}', - ['yellow', 'normal'], advanced_mode=True) - else: - print_Color(f'{("β”‚ β”œβ”€ " if show_lines else "")}~*{i}. {cmd}: ~*{desc}', - ['yellow', 'normal'], advanced_mode=True) - # other - print_Color(f'{("└─ " if show_lines else "")}~*Other:', - ['cyan'], advanced_mode=True) - for i, (cmd_other, desc_other) in enumerate(cmd_descriptions_other.items(), start=1): - if i == len(cmd_descriptions_other): - print_Color(f'{(" └─ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ - 'yellow', 'normal'], advanced_mode=True) - else: - print_Color(f'{(" β”œβ”€ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ - 'yellow', 'normal'], advanced_mode=True) - else: - print_Color(f'~*commands:', ['cyan'], advanced_mode=True) - # main - for i, (cmd, desc) in enumerate(cmd_descriptions.items(), start=1): - if i == len(cmd_descriptions): - print_Color(f'{("└─ " if show_lines else "")}~*{cmd}: ~*{desc}', - ['yellow', 'normal'], advanced_mode=True) - else: - print_Color(f'{("β”œβ”€ " if show_lines else "")}~*{cmd}: ~*{desc}', - ['yellow', 'normal'], advanced_mode=True) - # others - for i, (cmd_other, desc_other) in enumerate(cmd_descriptions_other.items(), start=1): - if i == len(cmd_descriptions_other): - print_Color(f'{("└─ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ - 'yellow', 'normal'], advanced_mode=True) - else: - print_Color(f'{("β”œβ”€ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ - 'yellow', 'normal'], advanced_mode=True) - -# CI_atmd -def CI_atmd(): - # global var import - global img_array - global label - # check for a image with a label - if label is not None: - # Check if the dataset file exists - if os.path.exists(Database_dir): - # Load the dataset file - print_Color('loading the existing dataset...', ['normal']) - logger.debug(f'CI_atmd>>Debug: loading the existing dataset...') - dataset = np.load(Database_dir, allow_pickle=True).item() - else: - # Create a new dataset file if it doesn't exist - dataset = {'images': [], 'labels': []} - - # Add the image array to the dataset - dataset['images'].append(img_array) - dataset['labels'].append(label) - label_UF = np.argmax(label) - label_class = 'PNEUMONIA' if label_UF == 1 else 'NORMAL' - label_class_color = 'red' if label_UF == 1 else 'green' - # Save the dataset file - np.save(Database_dir, dataset) - # Display the length of the dataset - print(f"Dataset length: {len(dataset['images'])}") - logger.debug(f'CI_atmd>>Debug: Dataset length: {len(dataset["images"])}') - print_Color(f"Saved label: ~*{label_class}", - [label_class_color], advanced_mode=True) - print_Color('The image and its label are saved.', ['green']) - label = None - else: - print_Color('~*ERROR: ~*a image with a label doesnt exist.', - ['red', 'yellow'], advanced_mode=True) - logger.warning('CI_atmd>>ERROR: A image with a label doesnt exist.') - -# CI_tmwd -def CI_tmwd(argv_Split: list = ['none']): - Debug('FUNC[CI_tmwd] ARGV INPUT', argv_Split) - # global var import - global model - # argv - train_epochs = check_arg(argv_Split, 'e', return_arg=True) - Debug('FUNC[CI_tmwd] check_arg `-e`', train_epochs) - if train_epochs in check_arg_ERROR_LIST_USAGE: - IEH('Func[main>>CI_tmwd],P:[check_arg]>>[get `-e`],Error[check_arg.error in check_arg_ERROR_LIST_USAGE]', - DEV=False) - if train_epochs in check_arg_ERROR_LIST_RT or train_epochs.isalpha(): - print_Color(f'~*WARNING: ~*Invalid arg for -e. Using default value {train_epochs_def}.', ['red', 'yellow'], - advanced_mode=True) - train_epochs = train_epochs_def - elif train_epochs in ['![IER:01]', '![IER:04]']: - train_epochs = train_epochs_def - train_epochs = int(train_epochs) - # check the dataset file - if os.path.exists(Database_dir): - # Load the dataset file - dataset = np.load(Database_dir, allow_pickle=True).item() - # ARG IL (ignore limits) - if len(dataset['images']) > 15 or check_arg(argv_Split, 'i', bool_OUTPUT_ONLY=True): - # Convert 'dataset['images']' and 'dataset['labels']' to NumPy arrays - images = np.array(dataset['images']) - labels = np.array(dataset['labels']) - images = np.reshape( - images, (-1, IMG_RES[0], IMG_RES[1], IMG_RES[2])) - try: - if model is None: - print_Color('loading the Ai model...', ['normal']) - model = load_model(Model_dir) - except (ImportError, IOError): - print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', - ['red', 'yellow'], advanced_mode=True) - else: - print('Training the model...\n') - # training - history = model.fit(images, labels, epochs=train_epochs, batch_size=1, - verbose='auto') # history not used - print('Training done.\n') - else: - print_Color('~*ERROR: ~*Data/dataset.npy Len is <= 15 add more data.', ['red', 'yellow'], - advanced_mode=True) - else: - print_Color('~*ERROR: ~*Data/dataset.npy doesnt exist.', - ['red', 'yellow'], advanced_mode=True) - -# CI_ulmd -def CI_ulmd(): - print_Color( - 'Warning: upload model data set (currently not available!!!)', - ['yellow']) - -# CI_pwai -def CI_pwai(Auto: bool = False): - # global var import - global model - # check for input img - if img_array is not None: - try: - if model is None: - print_Color('loading the Ai model...', ['normal']) - model = load_model(Model_dir) - except (ImportError, IOError): - print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', - ['red', 'yellow'], advanced_mode=True) - else: - print_Color('predicting with the Ai model...', ['normal']) - model_prediction_ORG = model.predict(img_array) - model_prediction = np.argmax(model_prediction_ORG, axis=1) - pred_class = 'PNEUMONIA' if model_prediction == 1 else 'NORMAL' - class_color = 'red' if model_prediction == 1 else 'green' - confidence = np.max(model_prediction_ORG) - print_Color(f'~*the Ai model prediction: ~*{pred_class}~* with confidence ~*{confidence:.2f}~*.', - ['normal', class_color, 'normal', 'green', 'normal'], advanced_mode=True) - if confidence < 0.82: - print_Color('~*WARNING: ~*the confidence is low.', - ['red', 'yellow'], advanced_mode=True) - if model_prediction == 1: - if not Auto: - print_Color('~*Do you want to see a Grad cam of the model? ~*[~*Y~*/~*n~*]: ', - ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], - advanced_mode=True, - print_END='') - Grad_cam_use = input('') - else: - Grad_cam_use = 'y' - if Grad_cam_use.lower() == 'y': - clahe = cv2.createCLAHE(clipLimit=1.8) - Grad_cam_heatmap = make_gradcam_heatmap(img_array, - model, 'top_activation', - second_last_conv_layer_name = 'top_conv', - sensitivity_map = 2, pred_index=tf.argmax(model_prediction_ORG[0])) - Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2])) - Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap) - Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS) - Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255) - # Resize the heatmap for a larger display - display_size = (600, 600) # Change this to your desired display size - Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap[0], display_size) - reference_image = np.uint8(cv2.resize(img_array[0] * 255, display_size)) - # Apply the CLAHE algorithm to the reference image - reference_image_CLAHE = np.clip(clahe.apply(cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)), 0, 255) - # Display the heatmap in a new window - cv2.imshow('Grad-CAM Heatmap', Grad_cam_heatmap) - cv2.imshow('Reference Original Image', reference_image) - cv2.imshow('Reference Original Image (CLAHE)', reference_image_CLAHE) - cv2.waitKey(0) # Wait for any key to be pressed - cv2.destroyAllWindows() # Close the window - else: - print_Color('~*ERROR: ~*image data doesnt exist.', - ['red', 'yellow'], advanced_mode=True) - -# CI_rlmw -def CI_rlmw(): - # global var import - global model - # main proc - model = None - print_Color('loading the Ai model...', ['normal']) - try: - model = load_model(Model_dir) - except (ImportError, IOError): - print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', - ['red', 'yellow'], advanced_mode=True) - print_Color('loading the Ai model done.', ['normal']) - -# CI_liid -def CI_liid(Auto: bool = False): - # global var import - global img_array - global label - replace_img = 'y' - # check for img - if img_array is not None and not Auto: - # Ask the user if they want to replace the image - print_Color('~*Warning: An image is already loaded. Do you want to replace it? ~*[~*Y~*/~*n~*]: ', - ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], - advanced_mode=True, - print_END='') - replace_img = input('') - # If the user answers 'n' or 'N', return the existing img_array - if replace_img.lower() == 'y': - if not Auto: - print_Color('img dir. Enter \'G\' for using GUI: ', - ['yellow'], print_END='') - img_dir = input().strip('"') - if img_dir.lower() == 'g': - img_dir = open_file_GUI() - else: - img_dir = open_file_GUI() - logger.debug(f'CI_liid:img_dir {img_dir}') - # Extract file extension from img_dir - try: - _, file_extension = os.path.splitext(img_dir) - except TypeError: - file_extension = 'TEMP FILE EXTENSION' - if file_extension.upper()[1:] not in IMG_AF: - print_Color('~*ERROR: ~*Invalid file format. Please provide an image file.', ['red', 'yellow'], - advanced_mode=True) - logger.warning('CI_liid>>ERROR: Invalid file format. Please provide an image file.') - else: - try: - # Load and resize the image - img = Image.open(img_dir).resize((IMG_RES[1], IMG_RES[0])) - except Exception: - print_Color('~*ERROR: ~*Invalid file dir. Please provide an image file.', ['red', 'yellow'], - advanced_mode=True) - logger.warning('CI_liid>>ERROR: Invalid file dir. Please provide an image file.') - else: - # Check for RGB mode - if img.mode != 'RGB': - img = img.convert('RGB') - # Convert to numpy array - img_array = np.asarray(img) - - # Normalize pixel values to [0, 1] - img_array = img_array / 255.0 - - # Add a dimension to transform from (height, width, channels) to (batch_size, height, width, channels) - img_array = np.expand_dims(img_array, axis=0) - - # Assign labels to the image - if not Auto: - print_Color('~*Enter label ~*(0 for Normal, 1 for Pneumonia, 2 Unknown): ', [ - 'yellow', 'normal'], print_END='', advanced_mode=True) - try: - label = int(input('')) - except ValueError: - print_Color('~*ERROR: ~*Invalid input.', - ['red', 'yellow'], advanced_mode=True) - logger.warning('CI_liid>>ERROR: Invalid input label.') - else: - logger.debug(f'CI_liid:(INPUT) label {label}') - if label in [0, 1]: - # Convert label to categorical format - label = to_categorical(int(label), num_classes=2) - print_Color('The label is saved.', ['green']) - else: - label = None - print_Color('The image is loaded.', ['green']) - -# CI_uaim -def CI_uaim(): - print_Color('~*Do you want to download the light model? ~*[~*Y~*/~*n~*]: ', - ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], - advanced_mode=True, - print_END='') - download_light_model = input('') - if download_light_model.lower() == 'y': - Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_light_name - else: - Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_name - try: - download_file_from_github(Github_repo_Releases_URL, - Github_repo_Releases_Model_name_temp, - Model_dir, - 1024) - except Exception: - print_Color('\n~*ERROR: ~*Failed to download the model.', ['red', 'yellow'], advanced_mode=True) - -# CMT>>> -command_tuple = ( - 'help', # help - 'atmd', # add to model dataset - 'axid', # simple image classification - 'tmwd', # train model with dataset - 'ulmd', # upload model data set (not available!!!) - 'pwai', # predict with Ai - 'rlmw', # reload model - 'liid', # load img input data - 'debug', # Debug - 'uaim', # Update AI model - 'exit', # Quit the CLI - 'clear' # Clear the CLI -) -# SCH table: -# 'β”‚' (U+2502): Box Drawings Light Vertical -# 'β”Œ' (U+250C): Box Drawings Light Down and Right -# '┐' (U+2510): Box Drawings Light Down and Left -# 'β””' (U+2514): Box Drawings Light Up and Right -# 'β”˜' (U+2518): Box Drawings Light Up and Left -# 'β”œ' (U+251C): Box Drawings Light Vertical and Right -# '─' (U+2524): Box Drawings Light Vertical and Left -# '┬' (U+252C): Box Drawings Light Down and Horizontal -# 'β”΄' (U+2534): Box Drawings Light Up and Horizontal -# 'β”Ό' (U+253C): Box Drawings Light Vertical and Horizontal -# '─' -cmd_descriptions = { - 'help': 'Show the help menu with the list of all available commands', - 'axid': 'simple auto classification' -} -cmd_descriptions_other = { - 'liid': 'Load image data for input', - 'pwai': 'Make predictions using the trained AI model', - 'atmd': 'Add data to the model dataset for training', - 'tmwd': f'Train the model with the existing dataset. \x1b[31m(deprecated!)\x1b[0m\n\ - β”‚ └────Optional Args:\n\ - β”‚ β”œβ”€β”€β”€β”€\'-i\' Ignore the limits.\n\ - β”‚ └────\'-e\' The number after \'e\' will be training epochs (default: {train_epochs_def}).\n\ - β”‚ └────Example: \'-e10\'', - 'ulmd': 'Upload model data set (currently not available)', - 'uaim': 'Update the AI model', - 'rlmw': 'Reload/Load Ai model', - 'exit': 'Quit the CLI', - 'clear': 'Clear the CLI' -} -# funcs(INTERNAL)>>> -# CLI_IM -def CLI_IM(CLII: bool = True): - if CLII: - print_Color('>>> ' if Debug_m else '>>> ', ['red' if Debug_m else 'green'], print_END='', - advanced_mode=False) - U_input = input('').lower() - try: - str_array = U_input.split() - if str_array[0] in command_tuple: - return str_array - else: - closest_match = difflib.get_close_matches( - str_array[0], command_tuple, n=1) - if closest_match: - print_Color( - f'~*ERROR: ~*Invalid input. you can use \'~*help~*\', did you mean \'~*{closest_match[0]}~*\'.', - ['red', 'yellow', 'green', 'yellow', 'green', 'yellow'], advanced_mode=True) - else: - print_Color(f'~*ERROR: ~*Invalid input. you can use \'~*help~*\'.', - ['red', 'yellow', 'green', 'yellow'], advanced_mode=True) - return ['IIE'] - except IndexError: - return ['IIE'] - -# IEH -def IEH(id: str = 'Unknown', stop: bool = True, DEV: bool = True): - Debug('IEH INPUT: ', f'id:{id}|stop:{stop}|DEV:{DEV}') - print_Color(f'~*ERROR: ~*Internal error info/id:\n~*{id}~*.', ['red', 'yellow', 'bg_red', 'yellow'], - advanced_mode=True) - logger.exception(f'Internal Error Handler [stop:{stop}|DEV:{DEV}|id:{id}]') - if DEV: - print_Color('~*Do you want to see the detailed error message? ~*[~*Y~*/~*n~*]: ', - ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], - advanced_mode=True, - print_END='') - show_detailed_error = input('') - if show_detailed_error.lower() == 'y': - print_Color('detailed error message:', ['yellow']) - traceback.print_exc() - if stop: - logger.warning('SYS EXIT|ERROR: Internal|by Internal Error Handler') - sys.exit('SYS EXIT|ERROR: Internal|by Internal Error Handler') - -# main -def main(): - # global - global Debug_m - # CLI loop - while True: # WT - # input manager - input_array = CLI_IM() - Debug('input_array', input_array) - logger.debug(f'input_array {input_array}') - match input_array[0]: # MI - case 'help': - CI_help() - case 'atmd': - CI_atmd() - case 'tmwd': - if len(input_array) > 1: - CI_tmwd(argv_Split=input_array[1:]) - else: - CI_tmwd() - case 'ulmd': - CI_ulmd() - case 'pwai': - CI_pwai() - case 'axid': - CI_liid(Auto=True) - CI_pwai(Auto=True) - case 'rlmw': - CI_rlmw() - case 'liid': - CI_liid() - case 'uaim': - CI_uaim() - case 'IIE': - pass - case 'debug': - print('Debug mode is ON...') - Debug_m = True - case 'clear': - os.system('cls' if os.name == 'nt' else 'clear') - print(CLI_Info) - case 'exit': - logger.info('Exit by prompt.') - raise KeyboardInterrupt - case _: - IEH(id='Func[main],P:[CLI loop]>>[match input],Error[nothing matched]', - stop=False, DEV=False) - -# start>>> -# clear the 'start L1' prompt -print(' ', end='\r') -# Start INFO -VER = f'V{CLI_Ver}' + datetime.now().strftime(" CDT(%Y/%m/%d | %H:%M:%S)") -gpus = tf.config.list_physical_devices('GPU') -if gpus: - TF_MODE = 'GPU' - TF_sys_details = tf.sysconfig.get_build_info() - TF_CUDA_VER = TF_sys_details['cuda_version'] - TF_CUDNN_VER = TF_sys_details['cudnn_version'] # NOT USED - try: - gpu_name = subprocess.check_output( - ["nvidia-smi", "-L"]).decode("utf-8").split(":")[1].split("(")[0].strip() - # GPU 0: NVIDIA `THE GPU NAME` (UUID: GPU-'xxxxxxxxxxxxxxxxxxxx') - # β”‚ β”‚ - # β”Œ---β”΄----------┐ β”Œ---β”΄----------┐ - # β”‚.split(":")[1]β”‚ β”‚.split("(")[0]β”‚ - # β””--------------β”˜ β””--------------β”˜ - except Exception: - gpu_name = '\x1b[0;31mNVIDIA-SMI-ERROR\x1b[0m' - TF_INFO = f'GPU NAME: {gpus[0].name}>>{gpu_name}, CUDA Version: {TF_CUDA_VER}' -else: - TF_MODE = 'CPU' - info = cpuinfo.get_cpu_info()['brand_raw'] - TF_INFO = f'{info}' -# CLI_Info -CLI_Info = f'PDAI Ver: {VER} \nPython Ver: {sys.version} \nTensorflow Ver: {tf.version.VERSION}, Mode: {TF_MODE}, {TF_INFO} \nType \'help\' for more information.' -logger.info(f'PDAI Ver: {VER}') -logger.info(f'Python Ver: {sys.version}') -logger.info(f'Tensorflow Ver: {tf.version.VERSION}') -logger.info(f'Mode: {TF_MODE}, {TF_INFO}') -print(CLI_Info) -# FP -if Model_FORMAT not in ['TF_dir', 'H5_SF']: - logger.info(f'Model file format [{Model_FORMAT}]') - IEH(id=f'F[SYS],P[FP],Error[Invalid Model_FORMAT]', DEV=False) -elif Model_FORMAT == 'H5_SF': - Model_dir += '.h5' -# start main -if __name__ == '__main__': - try: - try: - main() - except (EOFError, KeyboardInterrupt): - logger.info('KeyboardInterrupt.') - pass - except Exception as e: - IEH(id=f'F[SYS],RFunc[main],Error[{e}]', DEV=True) - else: - logger.info('CLI Exit.') - print_Color('\n~*[PDAI CLI] ~*closed.', - ['yellow', 'red'], advanced_mode=True) -else: - logger.info('CLI Imported.') -# end(EOF) +# Copyright (c) 2023 Aydin Hamedi +# +# This software is released under the MIT License. +# https://opensource.org/licenses/MIT + +# start L1 +print('Loading the CLI...', end='\r') +# pylib +import os +import re +import cv2 +import sys +import difflib +import inspect +import traceback +import subprocess +import requests +from tqdm import tqdm +import cpuinfo +from loguru import logger +import efficientnet.tfkeras +from tkinter import filedialog +from datetime import datetime +from PIL import Image +import tensorflow as tf +from keras.models import load_model +from keras.preprocessing.image import ImageDataGenerator +from keras.utils import to_categorical +import numpy as np +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.Grad_cam import make_gradcam_heatmap +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from Utils.Other import * +# global vars>>> +# CONST SYS +CLI_Ver = '0.8.9.3 (CLI)' +Model_dir = 'Data/PAI_model' # without file extention +Database_dir = 'Data/dataset.npy' +IMG_AF = ('JPEG', 'PNG', 'BMP', 'TIFF', 'JPG') +Github_repo_Releases_Model_name = 'PAI_model_T.h5' +Github_repo_Releases_Model_light_name = 'PAI_model_light_T.h5' +Github_repo_Releases_URL = 'https://api.github.com/repos/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest' +Model_FORMAT = 'H5_SF' # TF_dir/H5_SF +IMG_RES = (224, 224, 3) +train_epochs_def = 4 +SHOW_CSAA_OS = False +# normal global +img_array = None +Debug_m = False +label = None +model = None +# Other +logger.remove() +logger.add('Data\\logs\\SYS_LOG_{time}.log', + backtrace=True, diagnose=True, compression='zip') +logger.info('CLI Start...\n') +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) +# HF>>> +# check_args +def check_arg(arg_list: list, arg_str: str, return_arg: bool = False, bool_OUTPUT_ONLY: bool = False): + """ + This function checks if a specific argument exists in a list of arguments. + + Parameters: + arg_list (list): A list of arguments. + arg_str (str): The argument to check for. + return_arg (bool, optional): If True, returns the string after the argument if it exists. Defaults to False. + + Returns: + bool/str: Returns True if the argument exists and return_arg is False. + Returns the string after the argument if return_arg is True and the argument exists. + Returns specific error codes in case of errors. + + Error Codes: + + '![IER:01]': This error is returned when the provided argument list (arg_list) is empty or contains only 'none' or ''. + It indicates that there are no arguments to check against. + + '![IER:02]': This error is returned when the argument to check for (arg_str) is an empty string. + It indicates that there is no argument specified to look for in the argument list. + + '![IER:03]': This error is returned when the argument to check for (arg_str) is found in the argument list (arg_list), + but there is no string after the argument and return_arg is set to True. + It indicates that the function was expected to return the string following the argument, but there was none. + + '![IER:04]': This error is returned when the argument to check for (arg_str) is not found in the argument list (arg_list). + It indicates that the specified argument does not exist in the provided argument list. + + Note: If the bool_OUTPUT_ONLY parameter is set to True, the function will return False instead of these error codes. + """ + + # Error handling + if arg_list == [] or arg_list == ['none'] or arg_list == ['']: + return False if bool_OUTPUT_ONLY else '![IER:01]' + if arg_str == '': + return False if bool_OUTPUT_ONLY else '![IER:02]' + + for item in arg_list: + if item.startswith('-'): + if item[1] == arg_str: + if len(item) == 2 and return_arg: + return False if bool_OUTPUT_ONLY else '![IER:03]' + return True if not return_arg else item[2:] + + return False if bool_OUTPUT_ONLY else '![IER:04]' + + +check_arg_ERROR_LIST_USAGE = ['![IER:02]'] +check_arg_ERROR_LIST_RT = ['![IER:03]'] + +# open_file_GUI +def open_file_GUI(): + """Opens a file selection dialog GUI to allow the user to select an image file. + + Builds a filetypes filter from the IMG_AF global variable, joins the extensions + together into a filter string, converts to lowercase. Opens the file dialog, + and returns the selected file path if one was chosen. + + Returns: + str: The path to the selected image file, or None if no file was chosen. + """ + formats = ";*.".join(IMG_AF) + formats = "*." + formats.lower() + file_path = filedialog.askopenfilename( + filetypes=[("Image Files", formats)]) + if file_path: + return file_path + +# Debug + +# Debug +def Debug(ID, DEBUG_IF, SFL: bool = True, Force: bool = False, SFCS: bool = True): + """ + This function is used for debugging purposes. It prints out various information about the data passed to it. + + Args: + ID (Any): The identifier for the data. This could be any type, but is typically a string. + DEBUG_IF (Any): The data that needs to be debugged. This could be any type. + SFL (bool, optional): A flag to determine if the stack frame location should be included in the debug information. Defaults to True. + Force (bool, optional): A flag to force the debug information to be printed even if the global Debug_m is set to False. Defaults to False. + SFCS (bool, optional): A flag to determine if the function call stack should be included in the debug information. Defaults to True. + + Returns: + None + """ + try: + if Debug_m or Force: + frame_info = inspect.currentframe() + stack_trace = traceback.format_stack() + stack_trace_formated = '' + for line in stack_trace[:-1]: + stack_trace_formated += '--> [!>>>' + line + location = f'{inspect.stack()[1].filename}:{frame_info.f_back.f_lineno}' if SFL else f'L:{frame_info.f_back.f_lineno}' + Debug_data = \ + f'\n~*--> ~*DEBUG INFO id: ~*[{str(ID)}]~*, ' \ + f'Location: ~*[{location}]~*, ' \ + f'time: ~*[{datetime.now().strftime("%Y/%m/%d | %H:%M:%S")}]\n~*--> ~*' \ + f'Data: ~*{str(DEBUG_IF)}\n~*--> ~*' \ + f'Data Type: ~*{type(DEBUG_IF)}\n~*--> ~*' \ + f'Memory Address: ~*DEC>>>~*{id(DEBUG_IF)}~* | HEX>>>~*{hex(id(DEBUG_IF))}~* | BIN>>>~*{bin(id(DEBUG_IF))}\n' + if SFCS: + Debug_data += f'~*--> ~*Function Call Stack: ~*↓\n~*{stack_trace_formated}\n' + print_Color(Debug_data, + ['red', 'magenta', 'green', 'magenta', 'yellow', 'magenta', 'yellow', + 'red', 'magenta', 'yellow', 'red', 'magenta', 'yellow', 'red', 'magenta', + 'cyan', 'yellow', 'cyan', 'yellow', 'cyan', 'yellow', 'red', 'magenta', 'green', + 'yellow'] if SFCS else + ['red', 'magenta', 'green', 'magenta', 'yellow', 'magenta', 'yellow', + 'red', 'magenta', 'yellow', 'red', 'magenta', 'yellow', 'red', 'magenta', + 'cyan', 'yellow', 'cyan', 'yellow', 'cyan', 'yellow'], + advanced_mode=True) + except NameError: + print_Color( + '~*[`Debug` func] --> ERROR: ~*carate a global var named `Debug_m` for turning on and off the Debug func.', + ['red', 'yellow'], advanced_mode=True) + +# download_file_from_github +def download_file_from_github(url: str, file_name: str, save_as: str, chunk_size: int): + """Downloads a file from a GitHub release API URL to a local path. + + Args: + url (str): The GitHub API URL for the release to download from. + file_name (str): The name of the file to download from the release. + save_as (str): The local path to save the downloaded file to. + chunk_size (int): The chunk size to use when streaming the download. + """ + response = requests.get(url) + data = response.json() + logger.debug(f'download_file_from_github:data(json) {data}') + # Get the name of the latest release + release_name = data['name'] + print(f'Latest release: {release_name}') + + # Get the assets of the latest release + assets = data['assets'] + + # Find the required asset in the assets + for asset in assets: + if asset['name'] == file_name: + download_url = asset['browser_download_url'] + break + if 'download_url' in locals(): + # Download the file with a progress bar + response = requests.get(download_url, stream=True) + file_size = int(response.headers['Content-Length']) + progress_bar = tqdm(total=file_size, unit='b', unit_scale=True) + + with open(save_as, 'wb') as f: + for chunk in response.iter_content(chunk_size=chunk_size): + progress_bar.update(len(chunk)) + f.write(chunk) + + progress_bar.close() + + if file_size != 0 and progress_bar.n != file_size: + print_Color('~*ERROR: ~*Something went wrong while downloading the file.', ['red', 'yellow'], advanced_mode=True) + logger.warning('download_file_from_github>>ERROR: Something went wrong while downloading the file.') + else: + print(f"File '{save_as}' downloaded successfully.") + logger.debug(f"download_file_from_github>>Debug: File '{save_as}' downloaded successfully.") + else: + print_Color('~*ERROR: ~*Something went wrong while finding the file.', ['red', 'yellow'], advanced_mode=True) + logger.warning('download_file_from_github>>ERROR: Something went wrong while finding the file.') + +# CF>>> +# CI_help +# change show_lines and SSUH to change the style +def CI_help(SSUH: bool = True, show_lines: bool = True): + """Prints a help message listing available commands. + + This function prints a formatted help message showing the available + commands and their descriptions. It takes two boolean arguments: + + SSUH: Whether to print section headers and formatting. + show_lines: Whether to show line graphics. + + It first prints a header and list of main commands if SSUH is True. + Then it prints a header and list of other commands. + + The commands are printed from the cmd_descriptions and + cmd_descriptions_other dictionaries, with some simple formatting. + """ + # main + if SSUH: + print_Color(f'{("β”Œβ”€ " if show_lines else "")}~*Main (you can run them in order for simple usage):', + ['cyan'], advanced_mode=True) + for i, (cmd, desc) in enumerate(cmd_descriptions.items(), start=1): + if i == len(cmd_descriptions): + print_Color(f'{("β”‚ └─ " if show_lines else "")}~*{i}. {cmd}: ~*{desc}', + ['yellow', 'normal'], advanced_mode=True) + else: + print_Color(f'{("β”‚ β”œβ”€ " if show_lines else "")}~*{i}. {cmd}: ~*{desc}', + ['yellow', 'normal'], advanced_mode=True) + # other + print_Color(f'{("└─ " if show_lines else "")}~*Other:', + ['cyan'], advanced_mode=True) + for i, (cmd_other, desc_other) in enumerate(cmd_descriptions_other.items(), start=1): + if i == len(cmd_descriptions_other): + print_Color(f'{(" └─ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ + 'yellow', 'normal'], advanced_mode=True) + else: + print_Color(f'{(" β”œβ”€ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ + 'yellow', 'normal'], advanced_mode=True) + else: + print_Color(f'~*commands:', ['cyan'], advanced_mode=True) + # main + for i, (cmd, desc) in enumerate(cmd_descriptions.items(), start=1): + if i == len(cmd_descriptions): + print_Color(f'{("└─ " if show_lines else "")}~*{cmd}: ~*{desc}', + ['yellow', 'normal'], advanced_mode=True) + else: + print_Color(f'{("β”œβ”€ " if show_lines else "")}~*{cmd}: ~*{desc}', + ['yellow', 'normal'], advanced_mode=True) + # others + for i, (cmd_other, desc_other) in enumerate(cmd_descriptions_other.items(), start=1): + if i == len(cmd_descriptions_other): + print_Color(f'{("└─ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ + 'yellow', 'normal'], advanced_mode=True) + else: + print_Color(f'{("β”œβ”€ " if show_lines else "")}~*{cmd_other}: ~*{desc_other}', [ + 'yellow', 'normal'], advanced_mode=True) + +# CI_atmd +def CI_atmd(): + # global var import + global img_array + global label + # check for a image with a label + if label is not None: + # Check if the dataset file exists + if os.path.exists(Database_dir): + # Load the dataset file + print_Color('loading the existing dataset...', ['normal']) + logger.debug(f'CI_atmd>>Debug: loading the existing dataset...') + dataset = np.load(Database_dir, allow_pickle=True).item() + else: + # Create a new dataset file if it doesn't exist + dataset = {'images': [], 'labels': []} + + # Add the image array to the dataset + dataset['images'].append(img_array) + dataset['labels'].append(label) + label_UF = np.argmax(label) + label_class = 'PNEUMONIA' if label_UF == 1 else 'NORMAL' + label_class_color = 'red' if label_UF == 1 else 'green' + # Save the dataset file + np.save(Database_dir, dataset) + # Display the length of the dataset + print(f"Dataset length: {len(dataset['images'])}") + logger.debug(f'CI_atmd>>Debug: Dataset length: {len(dataset["images"])}') + print_Color(f"Saved label: ~*{label_class}", + [label_class_color], advanced_mode=True) + print_Color('The image and its label are saved.', ['green']) + label = None + else: + print_Color('~*ERROR: ~*a image with a label doesnt exist.', + ['red', 'yellow'], advanced_mode=True) + logger.warning('CI_atmd>>ERROR: A image with a label doesnt exist.') + +# CI_tmwd +def CI_tmwd(argv_Split: list = ['none']): + Debug('FUNC[CI_tmwd] ARGV INPUT', argv_Split) + # global var import + global model + # argv + train_epochs = check_arg(argv_Split, 'e', return_arg=True) + Debug('FUNC[CI_tmwd] check_arg `-e`', train_epochs) + if train_epochs in check_arg_ERROR_LIST_USAGE: + IEH('Func[main>>CI_tmwd],P:[check_arg]>>[get `-e`],Error[check_arg.error in check_arg_ERROR_LIST_USAGE]', + DEV=False) + if train_epochs in check_arg_ERROR_LIST_RT or train_epochs.isalpha(): + print_Color(f'~*WARNING: ~*Invalid arg for -e. Using default value {train_epochs_def}.', ['red', 'yellow'], + advanced_mode=True) + train_epochs = train_epochs_def + elif train_epochs in ['![IER:01]', '![IER:04]']: + train_epochs = train_epochs_def + train_epochs = int(train_epochs) + # check the dataset file + if os.path.exists(Database_dir): + # Load the dataset file + dataset = np.load(Database_dir, allow_pickle=True).item() + # ARG IL (ignore limits) + if len(dataset['images']) > 15 or check_arg(argv_Split, 'i', bool_OUTPUT_ONLY=True): + # Convert 'dataset['images']' and 'dataset['labels']' to NumPy arrays + images = np.array(dataset['images']) + labels = np.array(dataset['labels']) + images = np.reshape( + images, (-1, IMG_RES[0], IMG_RES[1], IMG_RES[2])) + try: + if model is None: + print_Color('loading the Ai model...', ['normal']) + model = load_model(Model_dir) + except (ImportError, IOError): + print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', + ['red', 'yellow'], advanced_mode=True) + else: + print('Training the model...\n') + # training + history = model.fit(images, labels, epochs=train_epochs, batch_size=1, + verbose='auto') # history not used + print('Training done.\n') + else: + print_Color('~*ERROR: ~*Data/dataset.npy Len is <= 15 add more data.', ['red', 'yellow'], + advanced_mode=True) + else: + print_Color('~*ERROR: ~*Data/dataset.npy doesnt exist.', + ['red', 'yellow'], advanced_mode=True) + +# CI_ulmd +def CI_ulmd(): + print_Color( + 'Warning: upload model data set (currently not available!!!)', + ['yellow']) + +# CI_pwai +def CI_pwai(Auto: bool = False): + # global var import + global model + # check for input img + if img_array is not None: + try: + if model is None: + print_Color('loading the Ai model...', ['normal']) + model = load_model(Model_dir) + except (ImportError, IOError): + print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', + ['red', 'yellow'], advanced_mode=True) + else: + print_Color('predicting with the Ai model...', ['normal']) + model_prediction_ORG = model.predict(img_array) + model_prediction = np.argmax(model_prediction_ORG, axis=1) + pred_class = 'PNEUMONIA' if model_prediction == 1 else 'NORMAL' + class_color = 'red' if model_prediction == 1 else 'green' + confidence = np.max(model_prediction_ORG) + print_Color(f'~*the Ai model prediction: ~*{pred_class}~* with confidence ~*{confidence:.2f}~*.', + ['normal', class_color, 'normal', 'green', 'normal'], advanced_mode=True) + if confidence < 0.82: + print_Color('~*WARNING: ~*the confidence is low.', + ['red', 'yellow'], advanced_mode=True) + if model_prediction == 1: + if not Auto: + print_Color('~*Do you want to see a Grad cam of the model? ~*[~*Y~*/~*n~*]: ', + ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], + advanced_mode=True, + print_END='') + Grad_cam_use = input('') + else: + Grad_cam_use = 'y' + if Grad_cam_use.lower() == 'y': + clahe = cv2.createCLAHE(clipLimit=1.8) + Grad_cam_heatmap = make_gradcam_heatmap(img_array, + model, 'top_activation', + second_last_conv_layer_name = 'top_conv', + sensitivity_map = 2, pred_index=tf.argmax(model_prediction_ORG[0])) + Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2])) + Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap) + Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS) + Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255) + # Resize the heatmap for a larger display + display_size = (600, 600) # Change this to your desired display size + Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap[0], display_size) + reference_image = np.uint8(cv2.resize(img_array[0] * 255, display_size)) + # Apply the CLAHE algorithm to the reference image + reference_image_CLAHE = np.clip(clahe.apply(cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)), 0, 255) + # Display the heatmap in a new window + cv2.imshow('Grad-CAM Heatmap', Grad_cam_heatmap) + cv2.imshow('Reference Original Image', reference_image) + cv2.imshow('Reference Original Image (CLAHE)', reference_image_CLAHE) + cv2.waitKey(0) # Wait for any key to be pressed + cv2.destroyAllWindows() # Close the window + else: + print_Color('~*ERROR: ~*image data doesnt exist.', + ['red', 'yellow'], advanced_mode=True) + +# CI_rlmw +def CI_rlmw(): + # global var import + global model + # main proc + model = None + print_Color('loading the Ai model...', ['normal']) + try: + model = load_model(Model_dir) + except (ImportError, IOError): + print_Color('~*ERROR: ~*Failed to load the model. Try running `uaim` first.', + ['red', 'yellow'], advanced_mode=True) + print_Color('loading the Ai model done.', ['normal']) + +# CI_liid +def CI_liid(Auto: bool = False): + # global var import + global img_array + global label + replace_img = 'y' + # check for img + if img_array is not None and not Auto: + # Ask the user if they want to replace the image + print_Color('~*Warning: An image is already loaded. Do you want to replace it? ~*[~*Y~*/~*n~*]: ', + ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], + advanced_mode=True, + print_END='') + replace_img = input('') + # If the user answers 'n' or 'N', return the existing img_array + if replace_img.lower() == 'y': + if not Auto: + print_Color('img dir. Enter \'G\' for using GUI: ', + ['yellow'], print_END='') + img_dir = input().strip('"') + if img_dir.lower() == 'g': + img_dir = open_file_GUI() + else: + img_dir = open_file_GUI() + logger.debug(f'CI_liid:img_dir {img_dir}') + # Extract file extension from img_dir + try: + _, file_extension = os.path.splitext(img_dir) + except TypeError: + file_extension = 'TEMP FILE EXTENSION' + if file_extension.upper()[1:] not in IMG_AF: + print_Color('~*ERROR: ~*Invalid file format. Please provide an image file.', ['red', 'yellow'], + advanced_mode=True) + logger.warning('CI_liid>>ERROR: Invalid file format. Please provide an image file.') + else: + try: + # Load and resize the image + img = Image.open(img_dir).resize((IMG_RES[1], IMG_RES[0])) + except Exception: + print_Color('~*ERROR: ~*Invalid file dir. Please provide an image file.', ['red', 'yellow'], + advanced_mode=True) + logger.warning('CI_liid>>ERROR: Invalid file dir. Please provide an image file.') + else: + # Check for RGB mode + if img.mode != 'RGB': + img = img.convert('RGB') + # Convert to numpy array + img_array = np.asarray(img) + + # Normalize pixel values to [0, 1] + img_array = img_array / 255.0 + + # Add a dimension to transform from (height, width, channels) to (batch_size, height, width, channels) + img_array = np.expand_dims(img_array, axis=0) + + # Assign labels to the image + if not Auto: + print_Color('~*Enter label ~*(0 for Normal, 1 for Pneumonia, 2 Unknown): ', [ + 'yellow', 'normal'], print_END='', advanced_mode=True) + try: + label = int(input('')) + except ValueError: + print_Color('~*ERROR: ~*Invalid input.', + ['red', 'yellow'], advanced_mode=True) + logger.warning('CI_liid>>ERROR: Invalid input label.') + else: + logger.debug(f'CI_liid:(INPUT) label {label}') + if label in [0, 1]: + # Convert label to categorical format + label = to_categorical(int(label), num_classes=2) + print_Color('The label is saved.', ['green']) + else: + label = None + print_Color('The image is loaded.', ['green']) + +# CI_uaim +def CI_uaim(): + print_Color('~*Do you want to download the light model? ~*[~*Y~*/~*n~*]: ', + ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], + advanced_mode=True, + print_END='') + download_light_model = input('') + if download_light_model.lower() == 'y': + Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_light_name + else: + Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_name + try: + download_file_from_github(Github_repo_Releases_URL, + Github_repo_Releases_Model_name_temp, + Model_dir, + 1024) + except Exception: + print_Color('\n~*ERROR: ~*Failed to download the model.', ['red', 'yellow'], advanced_mode=True) + +# CMT>>> +command_tuple = ( + 'help', # help + 'atmd', # add to model dataset + 'axid', # simple image classification + 'tmwd', # train model with dataset + 'ulmd', # upload model data set (not available!!!) + 'pwai', # predict with Ai + 'rlmw', # reload model + 'liid', # load img input data + 'debug', # Debug + 'uaim', # Update AI model + 'exit', # Quit the CLI + 'clear' # Clear the CLI +) +# SCH table: +# 'β”‚' (U+2502): Box Drawings Light Vertical +# 'β”Œ' (U+250C): Box Drawings Light Down and Right +# '┐' (U+2510): Box Drawings Light Down and Left +# 'β””' (U+2514): Box Drawings Light Up and Right +# 'β”˜' (U+2518): Box Drawings Light Up and Left +# 'β”œ' (U+251C): Box Drawings Light Vertical and Right +# '─' (U+2524): Box Drawings Light Vertical and Left +# '┬' (U+252C): Box Drawings Light Down and Horizontal +# 'β”΄' (U+2534): Box Drawings Light Up and Horizontal +# 'β”Ό' (U+253C): Box Drawings Light Vertical and Horizontal +# '─' +cmd_descriptions = { + 'help': 'Show the help menu with the list of all available commands', + 'axid': 'simple auto classification' +} +cmd_descriptions_other = { + 'liid': 'Load image data for input', + 'pwai': 'Make predictions using the trained AI model', + 'atmd': 'Add data to the model dataset for training', + 'tmwd': f'Train the model with the existing dataset. \x1b[31m(deprecated!)\x1b[0m\n\ + β”‚ └────Optional Args:\n\ + β”‚ β”œβ”€β”€β”€β”€\'-i\' Ignore the limits.\n\ + β”‚ └────\'-e\' The number after \'e\' will be training epochs (default: {train_epochs_def}).\n\ + β”‚ └────Example: \'-e10\'', + 'ulmd': 'Upload model data set (currently not available)', + 'uaim': 'Update the AI model', + 'rlmw': 'Reload/Load Ai model', + 'exit': 'Quit the CLI', + 'clear': 'Clear the CLI' +} +# funcs(INTERNAL)>>> +# CLI_IM +def CLI_IM(CLII: bool = True): + if CLII: + print_Color('>>> ' if Debug_m else '>>> ', ['red' if Debug_m else 'green'], print_END='', + advanced_mode=False) + U_input = input('').lower() + try: + str_array = U_input.split() + if str_array[0] in command_tuple: + return str_array + else: + closest_match = difflib.get_close_matches( + str_array[0], command_tuple, n=1) + if closest_match: + print_Color( + f'~*ERROR: ~*Invalid input. you can use \'~*help~*\', did you mean \'~*{closest_match[0]}~*\'.', + ['red', 'yellow', 'green', 'yellow', 'green', 'yellow'], advanced_mode=True) + else: + print_Color(f'~*ERROR: ~*Invalid input. you can use \'~*help~*\'.', + ['red', 'yellow', 'green', 'yellow'], advanced_mode=True) + return ['IIE'] + except IndexError: + return ['IIE'] + +# IEH +def IEH(id: str = 'Unknown', stop: bool = True, DEV: bool = True): + Debug('IEH INPUT: ', f'id:{id}|stop:{stop}|DEV:{DEV}') + print_Color(f'~*ERROR: ~*Internal error info/id:\n~*{id}~*.', ['red', 'yellow', 'bg_red', 'yellow'], + advanced_mode=True) + logger.exception(f'Internal Error Handler [stop:{stop}|DEV:{DEV}|id:{id}]') + if DEV: + print_Color('~*Do you want to see the detailed error message? ~*[~*Y~*/~*n~*]: ', + ['yellow', 'normal', 'green', 'normal', 'red', 'normal'], + advanced_mode=True, + print_END='') + show_detailed_error = input('') + if show_detailed_error.lower() == 'y': + print_Color('detailed error message:', ['yellow']) + traceback.print_exc() + if stop: + logger.warning('SYS EXIT|ERROR: Internal|by Internal Error Handler') + sys.exit('SYS EXIT|ERROR: Internal|by Internal Error Handler') + +# main +def main(): + # global + global Debug_m + # CLI loop + while True: # WT + # input manager + input_array = CLI_IM() + Debug('input_array', input_array) + logger.debug(f'input_array {input_array}') + match input_array[0]: # MI + case 'help': + CI_help() + case 'atmd': + CI_atmd() + case 'tmwd': + if len(input_array) > 1: + CI_tmwd(argv_Split=input_array[1:]) + else: + CI_tmwd() + case 'ulmd': + CI_ulmd() + case 'pwai': + CI_pwai() + case 'axid': + CI_liid(Auto=True) + CI_pwai(Auto=True) + case 'rlmw': + CI_rlmw() + case 'liid': + CI_liid() + case 'uaim': + CI_uaim() + case 'IIE': + pass + case 'debug': + print('Debug mode is ON...') + Debug_m = True + case 'clear': + os.system('cls' if os.name == 'nt' else 'clear') + print(CLI_Info) + case 'exit': + logger.info('Exit by prompt.') + raise KeyboardInterrupt + case _: + IEH(id='Func[main],P:[CLI loop]>>[match input],Error[nothing matched]', + stop=False, DEV=False) + +# start>>> +# clear the 'start L1' prompt +print(' ', end='\r') +# Start INFO +VER = f'V{CLI_Ver}' + datetime.now().strftime(" CDT(%Y/%m/%d | %H:%M:%S)") +gpus = tf.config.list_physical_devices('GPU') +if gpus: + TF_MODE = 'GPU' + TF_sys_details = tf.sysconfig.get_build_info() + TF_CUDA_VER = TF_sys_details['cuda_version'] + TF_CUDNN_VER = TF_sys_details['cudnn_version'] # NOT USED + try: + gpu_name = subprocess.check_output( + ["nvidia-smi", "-L"]).decode("utf-8").split(":")[1].split("(")[0].strip() + # GPU 0: NVIDIA `THE GPU NAME` (UUID: GPU-'xxxxxxxxxxxxxxxxxxxx') + # β”‚ β”‚ + # β”Œ---β”΄----------┐ β”Œ---β”΄----------┐ + # β”‚.split(":")[1]β”‚ β”‚.split("(")[0]β”‚ + # β””--------------β”˜ β””--------------β”˜ + except Exception: + gpu_name = '\x1b[0;31mNVIDIA-SMI-ERROR\x1b[0m' + TF_INFO = f'GPU NAME: {gpus[0].name}>>{gpu_name}, CUDA Version: {TF_CUDA_VER}' +else: + TF_MODE = 'CPU' + info = cpuinfo.get_cpu_info()['brand_raw'] + TF_INFO = f'{info}' +# CLI_Info +CLI_Info = f'PDAI Ver: {VER} \nPython Ver: {sys.version} \nTensorflow Ver: {tf.version.VERSION}, Mode: {TF_MODE}, {TF_INFO} \nType \'help\' for more information.' +logger.info(f'PDAI Ver: {VER}') +logger.info(f'Python Ver: {sys.version}') +logger.info(f'Tensorflow Ver: {tf.version.VERSION}') +logger.info(f'Mode: {TF_MODE}, {TF_INFO}') +print(CLI_Info) +# FP +if Model_FORMAT not in ['TF_dir', 'H5_SF']: + logger.info(f'Model file format [{Model_FORMAT}]') + IEH(id=f'F[SYS],P[FP],Error[Invalid Model_FORMAT]', DEV=False) +elif Model_FORMAT == 'H5_SF': + Model_dir += '.h5' +# start main +if __name__ == '__main__': + try: + try: + main() + except (EOFError, KeyboardInterrupt): + logger.info('KeyboardInterrupt.') + pass + except Exception as e: + IEH(id=f'F[SYS],RFunc[main],Error[{e}]', DEV=True) + else: + logger.info('CLI Exit.') + print_Color('\n~*[PDAI CLI] ~*closed.', + ['yellow', 'red'], advanced_mode=True) +else: + logger.info('CLI Imported.') +# end(EOF) diff --git a/Interface/CLI/Data/Utils/Grad_cam.py b/Interface/CLI/Data/Utils/Grad_cam.py index fc2a71f..c63729a 100644 --- a/Interface/CLI/Data/Utils/Grad_cam.py +++ b/Interface/CLI/Data/Utils/Grad_cam.py @@ -1,63 +1,63 @@ -import os -import glob -import numpy as np -import tensorflow as tf -# Other -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - -# Main -def _compute_heatmap(model, - img_array, - conv_layer_name, - pred_index): - """ - Helper function to compute the heatmap for a given convolutional layer. - """ - grad_model = tf.keras.models.Model( - [model.inputs], - [model.get_layer(conv_layer_name).output, model.output] - ) - - with tf.GradientTape() as tape: - conv_layer_output, preds = grad_model(img_array) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, conv_layer_output) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - - conv_layer_output = conv_layer_output[0] - heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] - heatmap = tf.squeeze(heatmap) - heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) - return heatmap - -def make_gradcam_heatmap(img_array, - model, - last_conv_layer_name, - second_last_conv_layer_name=None, - pred_index=None, - sensitivity_map=1.0): - """ - Function to compute the Grad-CAM heatmap for a specific class, given an input image. - """ - if pred_index is None: - preds = model.predict(img_array) - pred_index = tf.argmax(preds[0]) - - # Compute heatmap for the last convolutional layer - heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) - heatmap = heatmap ** sensitivity_map - - if second_last_conv_layer_name is not None: - # Compute heatmap for the second last convolutional layer - heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) - heatmap_second = heatmap_second ** sensitivity_map - - # Average the two heatmaps - heatmap = (heatmap + heatmap_second) / 2.0 - +import os +import glob +import numpy as np +import tensorflow as tf +# Other +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + +# Main +def _compute_heatmap(model, + img_array, + conv_layer_name, + pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, + model, + last_conv_layer_name, + second_last_conv_layer_name=None, + pred_index=None, + sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + return heatmap \ No newline at end of file diff --git a/Interface/CLI/Data/Utils/Other.py b/Interface/CLI/Data/Utils/Other.py index e9dc555..fa8aed8 100644 --- a/Interface/CLI/Data/Utils/Other.py +++ b/Interface/CLI/Data/Utils/Other.py @@ -1,32 +1,32 @@ -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -import pickle -import gzip - -def save_list(history, filename, compress=True): - # Saves the given history list to the specified filename. - # If compress is True, the file will be gzip compressed. - # Otherwise it will be saved as a normal pickle file. - if compress: - with gzip.open(filename, 'wb') as f: - pickle.dump(history, f) - else: - with open(filename, 'wb') as f: - pickle.dump(history, f) - - -def load_list(filename, compressed=True): - # Loads a pickled object from a file. - # If compressed=True, it will load from a gzip compressed file. - # Otherwise loads from a regular file. - if compressed: - with gzip.open(filename, 'rb') as f: - return pickle.load(f) - else: - with open(filename, 'rb') as f: - return pickle.load(f) -def P_warning(msg): - # Prints a warning message with color formatting. - # msg: The message to print as a warning. - print_Color_V2(f'Warning: {msg}') - +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +import pickle +import gzip + +def save_list(history, filename, compress=True): + # Saves the given history list to the specified filename. + # If compress is True, the file will be gzip compressed. + # Otherwise it will be saved as a normal pickle file. + if compress: + with gzip.open(filename, 'wb') as f: + pickle.dump(history, f) + else: + with open(filename, 'wb') as f: + pickle.dump(history, f) + + +def load_list(filename, compressed=True): + # Loads a pickled object from a file. + # If compressed=True, it will load from a gzip compressed file. + # Otherwise loads from a regular file. + if compressed: + with gzip.open(filename, 'rb') as f: + return pickle.load(f) + else: + with open(filename, 'rb') as f: + return pickle.load(f) +def P_warning(msg): + # Prints a warning message with color formatting. + # msg: The message to print as a warning. + print_Color_V2(f'Warning: {msg}') + diff --git a/Interface/CLI/Data/Utils/README.md b/Interface/CLI/Data/Utils/README.md index 2d85e26..16ffebb 100644 --- a/Interface/CLI/Data/Utils/README.md +++ b/Interface/CLI/Data/Utils/README.md @@ -1,15 +1,15 @@ -# Utils: - -## one_cycle_lr and lr_find (by 'benihime91') -- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) - - ### doc link: [1_README.md](docs\1_README.md) - -## Python-color-print-V2 and Python-color-print (by Me) -- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) - - ### doc link: [2_README.md](docs\2_README.md) -- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) - - ### doc link: [3_README.md](docs\3_README.md) - -## Grad_cam (by GPT-4 😁) - -## Other.py (by Me) +# Utils: + +## one_cycle_lr and lr_find (by 'benihime91') +- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) + - ### doc link: [1_README.md](docs\1_README.md) + +## Python-color-print-V2 and Python-color-print (by Me) +- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) + - ### doc link: [2_README.md](docs\2_README.md) +- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) + - ### doc link: [3_README.md](docs\3_README.md) + +## Grad_cam (by GPT-4 😁) + +## Other.py (by Me) diff --git a/Interface/CLI/Data/Utils/docs/1_README.md b/Interface/CLI/Data/Utils/docs/1_README.md index 461a2de..ada2767 100644 --- a/Interface/CLI/Data/Utils/docs/1_README.md +++ b/Interface/CLI/Data/Utils/docs/1_README.md @@ -1,208 +1,208 @@ -# one_cycle_lr-tensorflow: - -## Installation: - - Ensure that `python >= 3.6` is installed. - ```bash - $ git clone https://github.com/benihime91/one_cycle_lr-tensorflow.git - $ cd one_cycle_lr-tensorflow - $ pip install -r requirements.txt - ``` -## Demo: -[JupyterNotebook](https://github.com/benihime91/tensorflow-on-steroids/blob/master/nbs/one_cycle_%26_lr_finder_tf.ipynb). - -## Important : -LrFinder does not support TPU training . - -## Contents: - -1. **OneCycleLR learning rate scheduler** - - [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/one_cycle.py) - - **Example :** - ```python - - # Import `OneCycleLr` - from one_cycle import OneCycleLr - - # Configs - max_lr = 5e-02 - epochs = 5 - - # Istantiate `OneCycleLr` - one_c = OneCycleLr(max_lr=max_lr, steps_per_epoch=len(trn_ds), epochs=epochs) - - # Instantiate CallbackList - cbs = [one_c, ...] - - # Instantiate Optimizer & loss_fn - optim = keras.optimizers.SGD(momentum=0.9, clipvalue=0.1) - loss_fn = ... - - # Compile Model - model.compile(optimizer=optim, loss=loss_fn, metrics=["acc"]) - - # Fit Model - h = model.fit(trn_ds, validation_data=val_ds, epochs=epochs, callbacks=cbs) - ``` - - **To view the learning_rate and momentum plots:** - - ```python - # to plot the learning_rate & momentum(or beta_1) graphs - one_c.plot_lrs_moms() - ``` - - ![one_cycle_lr_plot](vis/one_cycle_plots.png) - - -2. **Learning Rate Finder** - - [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/lr_find.py) - - **Example:** - ```python - # Import LrFinder - from lr_find import LrFinder - - # Instantiate Optimizer & loss_fn - # [must be instance of tf.keras.Optimizers & tf.keras.Losses] - optimizer = ... - loss_fn = ... - - # Instantiate LrFinder - lr_find = LrFinder(model, optimizer, loss_fn) - - # Start range_test - lr_find.range_test(trn_ds) - ``` - **To view `lr_finder` plots:** - ```python - # Plot LrFinder graphs - lr_find.plot_lrs() - ``` - ![Lr_finder Plot](vis/lr_finder_plot_1.png) - - **To view `lr_finder` plots with suggestion:** - ```python - # Plot LrFinder graphs - lr_find.plot_lrs(skip_end=0, suggestion=True) - ``` - ![Lr_finder Plot](vis/lr_finder_plot_2.png) - - -## Information: - -1. **OneCycleLR learning rate scheduler:** - - Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://arxiv.org/abs/1708.07120) and popularized by [fast.ai](https://www.fast.ai/). - - - The 1cycle learning rate policy changes the learning rate after every batch. - - - Note also that the `total number of steps` in the cycle can be determined in one of two ways (listed in order of precedence): - - - A value for `total_steps` is explicitly provided. - - - A number of `epochs (epochs)` and a number of `steps per epoch (steps_per_epoch)` are provided. In this case, the number of `total steps` is inferred by `total_steps = epochs * steps_per_epoch`. - - You must either provide a value for total_steps or provide a value for both epochs and steps_per_epoch. - - - **OneCycleLR callback arguments:** - - - **max_lr** (`float`): Upper learning rate boundaries in the cycle. - - **total_steps** (`int`): The total number of steps in the cycle. Note that - if a value is not provided here, then it must be inferred by providing - a value for epochs and steps_per_epoch. - Default: None - - **epochs** (`int`): The number of epochs to train for. This is used along - with steps_per_epoch in order to infer the total number of steps in the cycle - if a value for total_steps is not provided. - Default: None - - **steps_per_epoch** (`int`): The number of steps per epoch to train for. This is - used along with epochs in order to infer the total number of steps in the - cycle if a value for total_steps is not provided. - Default: None - - **pct_start** (`float`): The percentage of the cycle (in number of steps) spent - increasing the learning rate. - Default: 0.3 - - **anneal_strategy** (`str`): {'cos', 'linear'} - Specifies the annealing strategy: "cos" for cosine annealing, "linear" for - linear annealing. - Default: 'cos' - - **cycle_momentum** (`bool`): If ``True``, momentum is cycled inversely - to learning rate between 'base_momentum' and 'max_momentum'. - Default: True - - **base_momentum** (`float`): Lower momentum boundaries in the cycle - for each parameter group. Note that momentum is cycled inversely - to learning rate; at the peak of a cycle, momentum is - 'base_momentum' and learning rate is 'max_lr'. - Default: 0.85 - - **max_momentum** (`float or list`): Upper momentum boundaries in the cycle - for each parameter group. Functionally, - it defines the cycle amplitude (max_momentum - base_momentum). - Note that momentum is cycled inversely - to learning rate; at the start of a cycle, momentum is 'max_momentum' - and learning rate is 'base_lr' - Default: 0.95 - - **div_factor** (`float`): Determines the initial learning rate via - initial_lr = max_lr/div_factor - Default: 25 - - **final_div_factor** (`float`): Determines the minimum learning rate via - min_lr = initial_lr/final_div_factor - Default: 1e4 - -2. **Learning_rate Finder:** - - - For training deep neural networks, selecting a good learning rate is essential for both better performance and faster convergence. Even optimizers such as Adam that are self-adjusting the learning rate can benefit from more optimal choices. - - - To reduce the amount of guesswork concerning choosing a good initial learning rate, a learning rate finder can be used. As described in this [paper](https://arxiv.org/abs/1506.01186) a learning rate finder does a small run where the learning rate is increased after each processed batch and the corresponding loss is logged. The result of this is a lr vs. loss plot that can be used as guidance for choosing a optimal initial lr. - - - **Arguments to Initialize LrFinder class:** - - **model** (`tf.keras.Model`): wrapped model - - **optimizer** (`tf.keras.optimizers`): wrapped optimizer - - **loss_fn** (t`f.keras.losses`): loss function - - - **Arguments to start range test:** - - **trn_ds** (`tf.data.Dataset`): the train dataset. - - **start_lr** (`float, optional`): the starting learning rate for the range test. - Default:1e-07. - - **end_lr** (`float, optional`): the maximum learning rate to test. Default: 10. - - **num_iter** (`int, optional`): the number of steps over which the test - occurs. Default: 100. - - **beta** (`float, optional`): the loss smoothing factor within the [0, 1] - interval. The loss is smoothed using exponential smoothing. - Default: 0.98. - - -## References & Citations: - ``` - @misc{smith2015cyclical, - title={Cyclical Learning Rates for Training Neural Networks}, - author={Leslie N. Smith}, - year={2015}, - eprint={1506.01186}, - archivePrefix={arXiv}, - primaryClass={cs.CV} - } - ``` - ``` - @misc{howard2018fastai, - title={fastai}, - author={Howard, Jeremy and others}, - year={2018}, - publisher={GitHub}, - howpublished={\url{https://github.com/fastai/fastai}}, - } - ``` - ``` - @incollection{NEURIPS2019_9015, - title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, - author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith}, - booktitle = {Advances in Neural Information Processing Systems 32}, - editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, - pages = {8024--8035}, - year = {2019}, - publisher = {Curran Associates, Inc.}, - url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf} - } - ``` +# one_cycle_lr-tensorflow: + +## Installation: + + Ensure that `python >= 3.6` is installed. + ```bash + $ git clone https://github.com/benihime91/one_cycle_lr-tensorflow.git + $ cd one_cycle_lr-tensorflow + $ pip install -r requirements.txt + ``` +## Demo: +[JupyterNotebook](https://github.com/benihime91/tensorflow-on-steroids/blob/master/nbs/one_cycle_%26_lr_finder_tf.ipynb). + +## Important : +LrFinder does not support TPU training . + +## Contents: + +1. **OneCycleLR learning rate scheduler** + + [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/one_cycle.py) + + **Example :** + ```python + + # Import `OneCycleLr` + from one_cycle import OneCycleLr + + # Configs + max_lr = 5e-02 + epochs = 5 + + # Istantiate `OneCycleLr` + one_c = OneCycleLr(max_lr=max_lr, steps_per_epoch=len(trn_ds), epochs=epochs) + + # Instantiate CallbackList + cbs = [one_c, ...] + + # Instantiate Optimizer & loss_fn + optim = keras.optimizers.SGD(momentum=0.9, clipvalue=0.1) + loss_fn = ... + + # Compile Model + model.compile(optimizer=optim, loss=loss_fn, metrics=["acc"]) + + # Fit Model + h = model.fit(trn_ds, validation_data=val_ds, epochs=epochs, callbacks=cbs) + ``` + + **To view the learning_rate and momentum plots:** + + ```python + # to plot the learning_rate & momentum(or beta_1) graphs + one_c.plot_lrs_moms() + ``` + + ![one_cycle_lr_plot](vis/one_cycle_plots.png) + + +2. **Learning Rate Finder** + + [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/lr_find.py) + + **Example:** + ```python + # Import LrFinder + from lr_find import LrFinder + + # Instantiate Optimizer & loss_fn + # [must be instance of tf.keras.Optimizers & tf.keras.Losses] + optimizer = ... + loss_fn = ... + + # Instantiate LrFinder + lr_find = LrFinder(model, optimizer, loss_fn) + + # Start range_test + lr_find.range_test(trn_ds) + ``` + **To view `lr_finder` plots:** + ```python + # Plot LrFinder graphs + lr_find.plot_lrs() + ``` + ![Lr_finder Plot](vis/lr_finder_plot_1.png) + + **To view `lr_finder` plots with suggestion:** + ```python + # Plot LrFinder graphs + lr_find.plot_lrs(skip_end=0, suggestion=True) + ``` + ![Lr_finder Plot](vis/lr_finder_plot_2.png) + + +## Information: + +1. **OneCycleLR learning rate scheduler:** + - Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://arxiv.org/abs/1708.07120) and popularized by [fast.ai](https://www.fast.ai/). + + - The 1cycle learning rate policy changes the learning rate after every batch. + + - Note also that the `total number of steps` in the cycle can be determined in one of two ways (listed in order of precedence): + + - A value for `total_steps` is explicitly provided. + + - A number of `epochs (epochs)` and a number of `steps per epoch (steps_per_epoch)` are provided. In this case, the number of `total steps` is inferred by `total_steps = epochs * steps_per_epoch`. + + You must either provide a value for total_steps or provide a value for both epochs and steps_per_epoch. + + - **OneCycleLR callback arguments:** + + - **max_lr** (`float`): Upper learning rate boundaries in the cycle. + - **total_steps** (`int`): The total number of steps in the cycle. Note that + if a value is not provided here, then it must be inferred by providing + a value for epochs and steps_per_epoch. + Default: None + - **epochs** (`int`): The number of epochs to train for. This is used along + with steps_per_epoch in order to infer the total number of steps in the cycle + if a value for total_steps is not provided. + Default: None + - **steps_per_epoch** (`int`): The number of steps per epoch to train for. This is + used along with epochs in order to infer the total number of steps in the + cycle if a value for total_steps is not provided. + Default: None + - **pct_start** (`float`): The percentage of the cycle (in number of steps) spent + increasing the learning rate. + Default: 0.3 + - **anneal_strategy** (`str`): {'cos', 'linear'} + Specifies the annealing strategy: "cos" for cosine annealing, "linear" for + linear annealing. + Default: 'cos' + - **cycle_momentum** (`bool`): If ``True``, momentum is cycled inversely + to learning rate between 'base_momentum' and 'max_momentum'. + Default: True + - **base_momentum** (`float`): Lower momentum boundaries in the cycle + for each parameter group. Note that momentum is cycled inversely + to learning rate; at the peak of a cycle, momentum is + 'base_momentum' and learning rate is 'max_lr'. + Default: 0.85 + - **max_momentum** (`float or list`): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (max_momentum - base_momentum). + Note that momentum is cycled inversely + to learning rate; at the start of a cycle, momentum is 'max_momentum' + and learning rate is 'base_lr' + Default: 0.95 + - **div_factor** (`float`): Determines the initial learning rate via + initial_lr = max_lr/div_factor + Default: 25 + - **final_div_factor** (`float`): Determines the minimum learning rate via + min_lr = initial_lr/final_div_factor + Default: 1e4 + +2. **Learning_rate Finder:** + + - For training deep neural networks, selecting a good learning rate is essential for both better performance and faster convergence. Even optimizers such as Adam that are self-adjusting the learning rate can benefit from more optimal choices. + + - To reduce the amount of guesswork concerning choosing a good initial learning rate, a learning rate finder can be used. As described in this [paper](https://arxiv.org/abs/1506.01186) a learning rate finder does a small run where the learning rate is increased after each processed batch and the corresponding loss is logged. The result of this is a lr vs. loss plot that can be used as guidance for choosing a optimal initial lr. + + - **Arguments to Initialize LrFinder class:** + - **model** (`tf.keras.Model`): wrapped model + - **optimizer** (`tf.keras.optimizers`): wrapped optimizer + - **loss_fn** (t`f.keras.losses`): loss function + + - **Arguments to start range test:** + - **trn_ds** (`tf.data.Dataset`): the train dataset. + - **start_lr** (`float, optional`): the starting learning rate for the range test. + Default:1e-07. + - **end_lr** (`float, optional`): the maximum learning rate to test. Default: 10. + - **num_iter** (`int, optional`): the number of steps over which the test + occurs. Default: 100. + - **beta** (`float, optional`): the loss smoothing factor within the [0, 1] + interval. The loss is smoothed using exponential smoothing. + Default: 0.98. + + +## References & Citations: + ``` + @misc{smith2015cyclical, + title={Cyclical Learning Rates for Training Neural Networks}, + author={Leslie N. Smith}, + year={2015}, + eprint={1506.01186}, + archivePrefix={arXiv}, + primaryClass={cs.CV} + } + ``` + ``` + @misc{howard2018fastai, + title={fastai}, + author={Howard, Jeremy and others}, + year={2018}, + publisher={GitHub}, + howpublished={\url{https://github.com/fastai/fastai}}, + } + ``` + ``` + @incollection{NEURIPS2019_9015, + title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, + author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith}, + booktitle = {Advances in Neural Information Processing Systems 32}, + editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, + pages = {8024--8035}, + year = {2019}, + publisher = {Curran Associates, Inc.}, + url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf} + } + ``` diff --git a/Interface/CLI/Data/Utils/docs/2_README.md b/Interface/CLI/Data/Utils/docs/2_README.md index c610a15..5c42240 100644 --- a/Interface/CLI/Data/Utils/docs/2_README.md +++ b/Interface/CLI/Data/Utils/docs/2_README.md @@ -1,152 +1,152 @@ -# Python-color-print-V2 -![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue) - -A Python function to print colored text to the console using advanced terminal colors. - -## Function Signature -```python -def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): -``` - -## Parameters -- `Input` (str): The input string to be printed. '' is used to specify the color of the following text. -- `print_END` (str): The string appended after the final output. Default is '\\n'. -- `start_char` (str): The character used as the start of the color specifier. Default is '<'. -- `end_char` (str): The character used as the end of the color specifier. Default is '>'. - -## Usage -you can print a string in color. For example: -```python -print_Color('Hello, World!') -``` -This will print 'Hello, World!' in green. - -Or like: -```python -print_Color('hello hello in red hello in green') -``` - -## Special Characters -The '<>' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. - -## Code snippet -```python -import re - -def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. '' is used to specify the color of the following text. - print_END (str): The string appended after the final output. Default is '\\n'. - start_char (str): The character used as the start of the color specifier. Default is '<'. - end_char (str): The character used as the end of the color specifier. Default is '>'. - - Examples: - ~~~python - print_Color('Hello, World!') - # Prints 'Hello, World!' in normal color. - - print_Color('Hello in red Hello in green') - # Prints 'Hello in red' in red and 'Hello in green' in green. - - print_Color('~red!Hello in red', start_char='~', end_char='!') - # Prints 'Hello, World!' in normal color. - - Note: - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m' - } - pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) - substrings = re.split(pattern, Input) - current_color = 'normal' - for i, sub_str in enumerate(substrings): - if i % 2 == 0: - print(color_code[current_color] + sub_str + color_code['normal'], end='') - current_color = 'normal' - else: - color = sub_str.strip() - if color in color_code: - current_color = color - else: - print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") - print('', end=print_END) -``` - -## Supported Colors -#### you can use the key word like 'black' and... to set the text color. -~~~ -'black': '\x1b[0;30m', -'red': '\x1b[0;31m', -'green': '\x1b[0;32m', -'yellow': '\x1b[0;33m', -'blue': '\x1b[0;34m', -'magenta': '\x1b[0;35m', -'cyan': '\x1b[0;36m', -'white': '\x1b[0;37m', -'normal': '\x1b[0m', -'bg_black': '\x1b[40m', -'bg_red': '\x1b[41m', -'bg_green': '\x1b[42m', -'bg_yellow': '\x1b[43m', -'bg_blue': '\x1b[44m', -'bg_magenta': '\x1b[45m', -'bg_cyan': '\x1b[46m', -'bg_white': '\x1b[47m', -'bg_normal': '\x1b[49m', -'light_gray': '\x1b[0;90m', -'light_red': '\x1b[0;91m', -'light_green': '\x1b[0;92m', -'light_yellow': '\x1b[0;93m', -'light_blue': '\x1b[0;94m', -'light_magenta': '\x1b[0;95m', -'light_cyan': '\x1b[0;96m', -'light_white': '\x1b[0;97m', -'bg_light_gray': '\x1b[0;100m', -'bg_light_red': '\x1b[0;101m', -'bg_light_green': '\x1b[0;102m', -'bg_light_yellow': '\x1b[0;103m', -'bg_light_blue': '\x1b[0;104m', -'bg_light_magenta': '\x1b[0;105m', -'bg_light_cyan': '\x1b[0;106m', -'bg_light_white': '\x1b[0;107m', -'underline': '\x1b[4m', -'bold': '\x1b[1m', -'blink': '\x1b[5m' -~~~ +# Python-color-print-V2 +![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue) + +A Python function to print colored text to the console using advanced terminal colors. + +## Function Signature +```python +def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): +``` + +## Parameters +- `Input` (str): The input string to be printed. '' is used to specify the color of the following text. +- `print_END` (str): The string appended after the final output. Default is '\\n'. +- `start_char` (str): The character used as the start of the color specifier. Default is '<'. +- `end_char` (str): The character used as the end of the color specifier. Default is '>'. + +## Usage +you can print a string in color. For example: +```python +print_Color('Hello, World!') +``` +This will print 'Hello, World!' in green. + +Or like: +```python +print_Color('hello hello in red hello in green') +``` + +## Special Characters +The '<>' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. + +## Code snippet +```python +import re + +def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. '' is used to specify the color of the following text. + print_END (str): The string appended after the final output. Default is '\\n'. + start_char (str): The character used as the start of the color specifier. Default is '<'. + end_char (str): The character used as the end of the color specifier. Default is '>'. + + Examples: + ~~~python + print_Color('Hello, World!') + # Prints 'Hello, World!' in normal color. + + print_Color('Hello in red Hello in green') + # Prints 'Hello in red' in red and 'Hello in green' in green. + + print_Color('~red!Hello in red', start_char='~', end_char='!') + # Prints 'Hello, World!' in normal color. + + Note: + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m' + } + pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) + substrings = re.split(pattern, Input) + current_color = 'normal' + for i, sub_str in enumerate(substrings): + if i % 2 == 0: + print(color_code[current_color] + sub_str + color_code['normal'], end='') + current_color = 'normal' + else: + color = sub_str.strip() + if color in color_code: + current_color = color + else: + print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") + print('', end=print_END) +``` + +## Supported Colors +#### you can use the key word like 'black' and... to set the text color. +~~~ +'black': '\x1b[0;30m', +'red': '\x1b[0;31m', +'green': '\x1b[0;32m', +'yellow': '\x1b[0;33m', +'blue': '\x1b[0;34m', +'magenta': '\x1b[0;35m', +'cyan': '\x1b[0;36m', +'white': '\x1b[0;37m', +'normal': '\x1b[0m', +'bg_black': '\x1b[40m', +'bg_red': '\x1b[41m', +'bg_green': '\x1b[42m', +'bg_yellow': '\x1b[43m', +'bg_blue': '\x1b[44m', +'bg_magenta': '\x1b[45m', +'bg_cyan': '\x1b[46m', +'bg_white': '\x1b[47m', +'bg_normal': '\x1b[49m', +'light_gray': '\x1b[0;90m', +'light_red': '\x1b[0;91m', +'light_green': '\x1b[0;92m', +'light_yellow': '\x1b[0;93m', +'light_blue': '\x1b[0;94m', +'light_magenta': '\x1b[0;95m', +'light_cyan': '\x1b[0;96m', +'light_white': '\x1b[0;97m', +'bg_light_gray': '\x1b[0;100m', +'bg_light_red': '\x1b[0;101m', +'bg_light_green': '\x1b[0;102m', +'bg_light_yellow': '\x1b[0;103m', +'bg_light_blue': '\x1b[0;104m', +'bg_light_magenta': '\x1b[0;105m', +'bg_light_cyan': '\x1b[0;106m', +'bg_light_white': '\x1b[0;107m', +'underline': '\x1b[4m', +'bold': '\x1b[1m', +'blink': '\x1b[5m' +~~~ diff --git a/Interface/CLI/Data/Utils/docs/3_README.md b/Interface/CLI/Data/Utils/docs/3_README.md index 59ca165..d27dae1 100644 --- a/Interface/CLI/Data/Utils/docs/3_README.md +++ b/Interface/CLI/Data/Utils/docs/3_README.md @@ -1,71 +1,71 @@ -# Python-color-print - - -## Function Signature -```python -def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False): -``` - -## Parameters -- `Input` (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. -- `colors` (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. -- `print_END` (str): The string appended after the final output, default is '\\n'. -- `advanced_mode` (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. - -## Usage -In **normal mode**, you can print a string in a single color. For example: -```python -print_Color('Hello, World!', ['green']) -``` -This will print 'Hello, World!' in green. - -In **advanced mode**, you can print different parts of a string in different colors. For example: -```python -print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) -``` -This will print 'Hello in green' in green and 'Hello in red' in red. - -## Special Characters -The '~*' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. - -## Supported Colors -#### you can use the key word like 'black' and... to set the text color. -~~~ -'black': '\x1b[0;30m', -'red': '\x1b[0;31m', -'green': '\x1b[0;32m', -'yellow': '\x1b[0;33m', -'blue': '\x1b[0;34m', -'magenta': '\x1b[0;35m', -'cyan': '\x1b[0;36m', -'white': '\x1b[0;37m', -'normal': '\x1b[0m', -'bg_black': '\x1b[40m', -'bg_red': '\x1b[41m', -'bg_green': '\x1b[42m', -'bg_yellow': '\x1b[43m', -'bg_blue': '\x1b[44m', -'bg_magenta': '\x1b[45m', -'bg_cyan': '\x1b[46m', -'bg_white': '\x1b[47m', -'bg_normal': '\x1b[49m', -'light_gray': '\x1b[0;90m', -'light_red': '\x1b[0;91m', -'light_green': '\x1b[0;92m', -'light_yellow': '\x1b[0;93m', -'light_blue': '\x1b[0;94m', -'light_magenta': '\x1b[0;95m', -'light_cyan': '\x1b[0;96m', -'light_white': '\x1b[0;97m', -'bg_light_gray': '\x1b[0;100m', -'bg_light_red': '\x1b[0;101m', -'bg_light_green': '\x1b[0;102m', -'bg_light_yellow': '\x1b[0;103m', -'bg_light_blue': '\x1b[0;104m', -'bg_light_magenta': '\x1b[0;105m', -'bg_light_cyan': '\x1b[0;106m', -'bg_light_white': '\x1b[0;107m', -'underline': '\x1b[4m', -'bold': '\x1b[1m', -'blink': '\x1b[5m' -~~~ +# Python-color-print + + +## Function Signature +```python +def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False): +``` + +## Parameters +- `Input` (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. +- `colors` (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. +- `print_END` (str): The string appended after the final output, default is '\\n'. +- `advanced_mode` (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. + +## Usage +In **normal mode**, you can print a string in a single color. For example: +```python +print_Color('Hello, World!', ['green']) +``` +This will print 'Hello, World!' in green. + +In **advanced mode**, you can print different parts of a string in different colors. For example: +```python +print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) +``` +This will print 'Hello in green' in green and 'Hello in red' in red. + +## Special Characters +The '~*' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. + +## Supported Colors +#### you can use the key word like 'black' and... to set the text color. +~~~ +'black': '\x1b[0;30m', +'red': '\x1b[0;31m', +'green': '\x1b[0;32m', +'yellow': '\x1b[0;33m', +'blue': '\x1b[0;34m', +'magenta': '\x1b[0;35m', +'cyan': '\x1b[0;36m', +'white': '\x1b[0;37m', +'normal': '\x1b[0m', +'bg_black': '\x1b[40m', +'bg_red': '\x1b[41m', +'bg_green': '\x1b[42m', +'bg_yellow': '\x1b[43m', +'bg_blue': '\x1b[44m', +'bg_magenta': '\x1b[45m', +'bg_cyan': '\x1b[46m', +'bg_white': '\x1b[47m', +'bg_normal': '\x1b[49m', +'light_gray': '\x1b[0;90m', +'light_red': '\x1b[0;91m', +'light_green': '\x1b[0;92m', +'light_yellow': '\x1b[0;93m', +'light_blue': '\x1b[0;94m', +'light_magenta': '\x1b[0;95m', +'light_cyan': '\x1b[0;96m', +'light_white': '\x1b[0;97m', +'bg_light_gray': '\x1b[0;100m', +'bg_light_red': '\x1b[0;101m', +'bg_light_green': '\x1b[0;102m', +'bg_light_yellow': '\x1b[0;103m', +'bg_light_blue': '\x1b[0;104m', +'bg_light_magenta': '\x1b[0;105m', +'bg_light_cyan': '\x1b[0;106m', +'bg_light_white': '\x1b[0;107m', +'underline': '\x1b[4m', +'bold': '\x1b[1m', +'blink': '\x1b[5m' +~~~ diff --git a/Interface/CLI/Data/Utils/lr_find.py b/Interface/CLI/Data/Utils/lr_find.py index 4356d32..cc136ab 100644 --- a/Interface/CLI/Data/Utils/lr_find.py +++ b/Interface/CLI/Data/Utils/lr_find.py @@ -1,209 +1,209 @@ -import tempfile - -import matplotlib.pyplot as plt -import numpy as np - -import tensorflow as tf -from tensorflow import keras -from tqdm.auto import tqdm - -K = keras.backend - - -class Scheduler: - def __init__(self, vals, n_iter: int) -> None: - 'Used to "step" from start,end (`vals`) over `n_iter` s on a schedule defined by `func`' - self.start, self.end = ( - (vals[0], vals[1]) if isinstance(vals, tuple) else (vals, 0) - ) - self.n_iter = max(1, n_iter) - self.func = self._aannealing_exp - self.n = 0 - - @staticmethod - def _aannealing_exp(start: float, end: float, pct: float) -> float: - "Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return start * (end / start) ** pct - - def restart(self) -> None: - self.n = 0 - - def step(self) -> float: - self.n += 1 - return self.func(self.start, self.end, self.n / self.n_iter) - - @property - def is_done(self) -> bool: - "Return `True` if schedule completed." - return self.n >= self.n_iter - - -class LrFinder: - """ - [LrFinder Implemetation taken from Fast.ai] - (https://github.com/fastai/fastai/tree/master/fastai) - - The learning rate range test increases the learning rate in a pre-training run - between two boundaries in a linear or exponential manner. It provides valuable - information on how well the network can be trained over a range of learning rates - and what is the optimal learning rate. - - Args: - model (tf.keras.Model): wrapped model - optimizer (tf.keras.optimizers): wrapped optimizer - loss_fn (tf.keras.losses): loss function - - Example: - >>> lr_finder = LrFinder(model, optimizer, loss_fn) - >>> lr_finder.range_test(trn_ds, end_lr=100, num_iter=100) - >>> lr_finder.plot_lrs() # to inspect the loss-learning rate graph - """ - - def __init__(self, - model: tf.keras.Model, - optimizer: tf.keras.optimizers.Optimizer, - loss_fn: tf.keras.losses.Loss, - ) -> None: - - self.lrs = [] - self.losses = [] - self.model = model - self.optimizer = optimizer - self.loss_fn = loss_fn - self.mw = self.model.get_weights() - self.init_lr = K.get_value(self.optimizer.lr) - self.iteration = 0 - self.weightsFile = tempfile.mkstemp()[1] - - @tf.function - def trn_step(self, xb, yb): - """performs 1 trainig step""" - with tf.GradientTape() as tape: - logits = self.model(xb, training=True) - main_loss = tf.reduce_mean(self.loss_fn(yb, logits)) - loss = tf.add_n([main_loss] + self.model.losses) - grads = tape.gradient(loss, self.model.trainable_variables) - return loss, grads - - def range_test(self, - trn_ds: tf.data.Dataset, - start_lr: float = 1e-7, - end_lr: float = 10, - num_iter: int = 100, - beta=0.98, - ) -> None: - """ - Explore lr from `start_lr` to `end_lr` over `num_it` s in `model`. - - Args: - trn_ds (tf.data.Dataset) - start_lr (float, optional): the starting learning rate for the range test. - Default:1e-07. - end_lr (float, optional): the maximum learning rate to test. Default: 10. - num_iter (int, optional): the number of s over which the test - occurs. Default: 100. - beta (float, optional): the loss smoothing factor within the [0, 1] - interval. The loss is smoothed using exponential smoothing. - Default: 0.98. - """ - # save original model weights - try: - self.model.save_weights(self.weightsFile) - except: - print("Unable to save initial weights, weights of model will change. Re-instantiate model to load previous weights ...") - # start scheduler - sched = Scheduler((start_lr, end_lr), num_iter) - avg_loss, best_loss, = 0.0, 0.0 - # set the startig lr - K.set_value(self.optimizer.lr, sched.start) - - print(f"Finding best initial lr over {num_iter} steps") - # initialize tqdm bar - bar = tqdm(iterable=range(num_iter)) - - # iterate over the batches - for (xb, yb) in trn_ds: - self.iteration += 1 - loss, grads = self.trn_step(xb, yb) - # compute smoothed loss - avg_loss = beta * avg_loss + (1 - beta) * loss - smoothed_loss = avg_loss / (1 - beta ** self.iteration) - - # record best loss - if self.iteration == 1 or smoothed_loss < best_loss: - best_loss = smoothed_loss - - # stop if loss is exploding - if sched.is_done or ( - smoothed_loss > 4 * best_loss or np.isnan(smoothed_loss) - ): - break - - # append losses and lrs - self.losses.append(smoothed_loss) - self.lrs.append(K.get_value(self.optimizer.lr)) - - # update weights - self.optimizer.apply_gradients( - zip(grads, self.model.trainable_variables)) - - # update lr - K.set_value(self.optimizer.lr, sched.step()) - - # update tqdm - bar.update(1) - - # clean-up - bar.close() - sched.restart() - self._print_prompt() - - def _print_prompt(self) -> None: - "Cleanup model weights disturbed during LRFinder exploration." - try: - self.model.load_weights(self.weightsFile) - except: - print( - "Unable to load inital weights. Re-instantiate model to load previous weights ...") - K.set_value(self.optimizer.lr, self.init_lr) - print( - "LR Finder is complete, type {LrFinder}.plot_lrs() to see the graph.") - - @staticmethod - def _split_list(vals, skip_start: int, skip_end: int) -> list: - return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:] - - def plot_lrs(self, - skip_start: int = 10, - skip_end: int = 5, - suggestion: bool = False, - show_grid: bool = False, - ) -> None: - """ - Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. - Optionally plot and return min gradient - """ - lrs = self._split_list(self.lrs, skip_start, skip_end) - losses = self._split_list(self.losses, skip_start, skip_end) - _, ax = plt.subplots(1, 1) - ax.plot(lrs, losses) - ax.set_ylabel("Loss") - ax.set_xlabel("Learning Rate") - ax.set_xscale("log") - if show_grid: - plt.grid(True, which="both", ls="-") - ax.xaxis.set_major_formatter(plt.FormatStrFormatter("%.0e")) - if suggestion: - try: - mg = (np.gradient(np.array(losses))).argmin() - except: - print( - "Failed to compute the gradients, there might not be enough points." - ) - return - print(f"Min numerical gradient: {lrs[mg]:.2E}") - ax.plot(lrs[mg], losses[mg], markersize=10, - marker="o", color="red") - self.min_grad_lr = lrs[mg] - ml = np.argmin(losses) - print(f"Min loss divided by 10: {lrs[ml]/10:.2E}") +import tempfile + +import matplotlib.pyplot as plt +import numpy as np + +import tensorflow as tf +from tensorflow import keras +from tqdm.auto import tqdm + +K = keras.backend + + +class Scheduler: + def __init__(self, vals, n_iter: int) -> None: + 'Used to "step" from start,end (`vals`) over `n_iter` s on a schedule defined by `func`' + self.start, self.end = ( + (vals[0], vals[1]) if isinstance(vals, tuple) else (vals, 0) + ) + self.n_iter = max(1, n_iter) + self.func = self._aannealing_exp + self.n = 0 + + @staticmethod + def _aannealing_exp(start: float, end: float, pct: float) -> float: + "Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0." + return start * (end / start) ** pct + + def restart(self) -> None: + self.n = 0 + + def step(self) -> float: + self.n += 1 + return self.func(self.start, self.end, self.n / self.n_iter) + + @property + def is_done(self) -> bool: + "Return `True` if schedule completed." + return self.n >= self.n_iter + + +class LrFinder: + """ + [LrFinder Implemetation taken from Fast.ai] + (https://github.com/fastai/fastai/tree/master/fastai) + + The learning rate range test increases the learning rate in a pre-training run + between two boundaries in a linear or exponential manner. It provides valuable + information on how well the network can be trained over a range of learning rates + and what is the optimal learning rate. + + Args: + model (tf.keras.Model): wrapped model + optimizer (tf.keras.optimizers): wrapped optimizer + loss_fn (tf.keras.losses): loss function + + Example: + >>> lr_finder = LrFinder(model, optimizer, loss_fn) + >>> lr_finder.range_test(trn_ds, end_lr=100, num_iter=100) + >>> lr_finder.plot_lrs() # to inspect the loss-learning rate graph + """ + + def __init__(self, + model: tf.keras.Model, + optimizer: tf.keras.optimizers.Optimizer, + loss_fn: tf.keras.losses.Loss, + ) -> None: + + self.lrs = [] + self.losses = [] + self.model = model + self.optimizer = optimizer + self.loss_fn = loss_fn + self.mw = self.model.get_weights() + self.init_lr = K.get_value(self.optimizer.lr) + self.iteration = 0 + self.weightsFile = tempfile.mkstemp()[1] + + @tf.function + def trn_step(self, xb, yb): + """performs 1 trainig step""" + with tf.GradientTape() as tape: + logits = self.model(xb, training=True) + main_loss = tf.reduce_mean(self.loss_fn(yb, logits)) + loss = tf.add_n([main_loss] + self.model.losses) + grads = tape.gradient(loss, self.model.trainable_variables) + return loss, grads + + def range_test(self, + trn_ds: tf.data.Dataset, + start_lr: float = 1e-7, + end_lr: float = 10, + num_iter: int = 100, + beta=0.98, + ) -> None: + """ + Explore lr from `start_lr` to `end_lr` over `num_it` s in `model`. + + Args: + trn_ds (tf.data.Dataset) + start_lr (float, optional): the starting learning rate for the range test. + Default:1e-07. + end_lr (float, optional): the maximum learning rate to test. Default: 10. + num_iter (int, optional): the number of s over which the test + occurs. Default: 100. + beta (float, optional): the loss smoothing factor within the [0, 1] + interval. The loss is smoothed using exponential smoothing. + Default: 0.98. + """ + # save original model weights + try: + self.model.save_weights(self.weightsFile) + except: + print("Unable to save initial weights, weights of model will change. Re-instantiate model to load previous weights ...") + # start scheduler + sched = Scheduler((start_lr, end_lr), num_iter) + avg_loss, best_loss, = 0.0, 0.0 + # set the startig lr + K.set_value(self.optimizer.lr, sched.start) + + print(f"Finding best initial lr over {num_iter} steps") + # initialize tqdm bar + bar = tqdm(iterable=range(num_iter)) + + # iterate over the batches + for (xb, yb) in trn_ds: + self.iteration += 1 + loss, grads = self.trn_step(xb, yb) + # compute smoothed loss + avg_loss = beta * avg_loss + (1 - beta) * loss + smoothed_loss = avg_loss / (1 - beta ** self.iteration) + + # record best loss + if self.iteration == 1 or smoothed_loss < best_loss: + best_loss = smoothed_loss + + # stop if loss is exploding + if sched.is_done or ( + smoothed_loss > 4 * best_loss or np.isnan(smoothed_loss) + ): + break + + # append losses and lrs + self.losses.append(smoothed_loss) + self.lrs.append(K.get_value(self.optimizer.lr)) + + # update weights + self.optimizer.apply_gradients( + zip(grads, self.model.trainable_variables)) + + # update lr + K.set_value(self.optimizer.lr, sched.step()) + + # update tqdm + bar.update(1) + + # clean-up + bar.close() + sched.restart() + self._print_prompt() + + def _print_prompt(self) -> None: + "Cleanup model weights disturbed during LRFinder exploration." + try: + self.model.load_weights(self.weightsFile) + except: + print( + "Unable to load inital weights. Re-instantiate model to load previous weights ...") + K.set_value(self.optimizer.lr, self.init_lr) + print( + "LR Finder is complete, type {LrFinder}.plot_lrs() to see the graph.") + + @staticmethod + def _split_list(vals, skip_start: int, skip_end: int) -> list: + return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:] + + def plot_lrs(self, + skip_start: int = 10, + skip_end: int = 5, + suggestion: bool = False, + show_grid: bool = False, + ) -> None: + """ + Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. + Optionally plot and return min gradient + """ + lrs = self._split_list(self.lrs, skip_start, skip_end) + losses = self._split_list(self.losses, skip_start, skip_end) + _, ax = plt.subplots(1, 1) + ax.plot(lrs, losses) + ax.set_ylabel("Loss") + ax.set_xlabel("Learning Rate") + ax.set_xscale("log") + if show_grid: + plt.grid(True, which="both", ls="-") + ax.xaxis.set_major_formatter(plt.FormatStrFormatter("%.0e")) + if suggestion: + try: + mg = (np.gradient(np.array(losses))).argmin() + except: + print( + "Failed to compute the gradients, there might not be enough points." + ) + return + print(f"Min numerical gradient: {lrs[mg]:.2E}") + ax.plot(lrs[mg], losses[mg], markersize=10, + marker="o", color="red") + self.min_grad_lr = lrs[mg] + ml = np.argmin(losses) + print(f"Min loss divided by 10: {lrs[ml]/10:.2E}") diff --git a/Interface/CLI/Data/Utils/one_cycle.py b/Interface/CLI/Data/Utils/one_cycle.py index 63739cd..dd3adf8 100644 --- a/Interface/CLI/Data/Utils/one_cycle.py +++ b/Interface/CLI/Data/Utils/one_cycle.py @@ -1,243 +1,243 @@ -from tensorflow import keras -import tensorflow as tf -import math -import matplotlib.pyplot as plt -import numpy as np - -K = keras.backend - - -class OneCycleLr(keras.callbacks.Callback): - """ - Sets the learning rate of each parameter group according to the - 1cycle learning rate policy. The 1cycle policy anneals the learning - rate from an initial learning rate to some maximum learning rate and then - from that maximum learning rate to some minimum learning rate much lower - than the initial learning rate. - This policy was initially described in the paper `Super-Convergence: - Very Fast Training of Neural Networks Using Large Learning Rates`_. - - [Implementation taken from PyTorch: - (https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR)] - - Note also that the total number of steps in the cycle can be determined in one - of two ways (listed in order of precedence): - - #. A value for total_steps is explicitly provided. - #. A number of epochs (epochs) and a number of steps per epoch - (steps_per_epoch) are provided. - In this case, the number of total steps is inferred by - total_steps = epochs * steps_per_epoch - You must either provide a value for total_steps or provide a value for both - epochs and steps_per_epoch. - - Args: - max_lr (float): Upper learning rate boundaries in the cycle. - total_steps (int): The total number of steps in the cycle. Note that - if a value is not provided here, then it must be inferred by providing - a value for epochs and steps_per_epoch. - Default: None - epochs (int): The number of epochs to train for. This is used along - with steps_per_epoch in order to infer the total number of steps in the cycle - if a value for total_steps is not provided. - Default: None - steps_per_epoch (int): The number of steps per epoch to train for. This is - used along with epochs in order to infer the total number of steps in the - cycle if a value for total_steps is not provided. - Default: None - pct_start (float): The percentage of the cycle (in number of steps) spent - increasing the learning rate. - Default: 0.3 - anneal_strategy (str): {'cos', 'linear'} - Specifies the annealing strategy: "cos" for cosine annealing, "linear" for - linear annealing. - Default: 'cos' - cycle_momentum (bool): If ``True``, momentum is cycled inversely - to learning rate between 'base_momentum' and 'max_momentum'. - Default: True - base_momentum (float): Lower momentum boundaries in the cycle - for each parameter group. Note that momentum is cycled inversely - to learning rate; at the peak of a cycle, momentum is - 'base_momentum' and learning rate is 'max_lr'. - Default: 0.85 - max_momentum (float or list): Upper momentum boundaries in the cycle - for each parameter group. Functionally, - it defines the cycle amplitude (max_momentum - base_momentum). - Note that momentum is cycled inversely - to learning rate; at the start of a cycle, momentum is 'max_momentum' - and learning rate is 'base_lr' - Default: 0.95 - div_factor (float): Determines the initial learning rate via - initial_lr = max_lr/div_factor - Default: 25 - final_div_factor (float): Determines the minimum learning rate via - min_lr = initial_lr/final_div_factor - Default: 1e4 - """ - - def __init__(self, - max_lr: float, - total_steps: int = None, - epochs: int = None, - steps_per_epoch: int = None, - pct_start: float = 0.3, - anneal_strategy: str = "cos", - cycle_momentum: bool = True, - base_momentum: float = 0.85, - max_momentum: float = 0.95, - div_factor: float = 25.0, - final_div_factor: float = 1e4, - ) -> None: - - super(OneCycleLr, self).__init__() - - # validate total steps: - if total_steps is None and epochs is None and steps_per_epoch is None: - raise ValueError( - "You must define either total_steps OR (epochs AND steps_per_epoch)" - ) - elif total_steps is not None: - if total_steps <= 0 or not isinstance(total_steps, int): - raise ValueError( - "Expected non-negative integer total_steps, but got {}".format( - total_steps - ) - ) - self.total_steps = total_steps - else: - if epochs <= 0 or not isinstance(epochs, int): - raise ValueError( - "Expected non-negative integer epochs, but got {}".format( - epochs) - ) - if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int): - raise ValueError( - "Expected non-negative integer steps_per_epoch, but got {}".format( - steps_per_epoch - ) - ) - # Compute total steps - self.total_steps = epochs * steps_per_epoch - - self.step_num = 0 - self.step_size_up = float(pct_start * self.total_steps) - 1 - self.step_size_down = float(self.total_steps - self.step_size_up) - 1 - - # Validate pct_start - if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): - raise ValueError( - "Expected float between 0 and 1 pct_start, but got {}".format( - pct_start) - ) - - # Validate anneal_strategy - if anneal_strategy not in ["cos", "linear"]: - raise ValueError( - "anneal_strategy must by one of 'cos' or 'linear', instead got {}".format( - anneal_strategy - ) - ) - elif anneal_strategy == "cos": - self.anneal_func = self._annealing_cos - elif anneal_strategy == "linear": - self.anneal_func = self._annealing_linear - - # Initialize learning rate variables - self.initial_lr = max_lr / div_factor - self.max_lr = max_lr - self.min_lr = self.initial_lr / final_div_factor - - # Initial momentum variables - self.cycle_momentum = cycle_momentum - if self.cycle_momentum: - self.m_momentum = max_momentum - self.momentum = max_momentum - self.b_momentum = base_momentum - - # Initialize variable to learning_rate & momentum - self.track_lr = [] - self.track_mom = [] - - def _annealing_cos(self, start, end, pct) -> float: - "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." - cos_out = math.cos(math.pi * pct) + 1 - return end + (start - end) / 2.0 * cos_out - - def _annealing_linear(self, start, end, pct) -> float: - "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return (end - start) * pct + start - - def set_lr_mom(self) -> None: - """Update the learning rate and momentum""" - if self.step_num <= self.step_size_up: - # update learining rate - computed_lr = self.anneal_func( - self.initial_lr, self.max_lr, self.step_num / self.step_size_up - ) - K.set_value(self.model.optimizer.lr, computed_lr) - # update momentum if cycle_momentum - if self.cycle_momentum: - computed_momentum = self.anneal_func( - self.m_momentum, self.b_momentum, self.step_num / self.step_size_up - ) - try: - K.set_value(self.model.optimizer.momentum, - computed_momentum) - except: - K.set_value(self.model.optimizer.beta_1, computed_momentum) - else: - down_step_num = self.step_num - self.step_size_up - # update learning rate - computed_lr = self.anneal_func( - self.max_lr, self.min_lr, down_step_num / self.step_size_down - ) - K.set_value(self.model.optimizer.lr, computed_lr) - # update momentum if cycle_momentum - if self.cycle_momentum: - computed_momentum = self.anneal_func( - self.b_momentum, - self.m_momentum, - down_step_num / self.step_size_down, - ) - try: - K.set_value(self.model.optimizer.momentum, - computed_momentum) - except: - K.set_value(self.model.optimizer.beta_1, computed_momentum) - - def on_train_begin(self, logs=None) -> None: - # Set initial learning rate & momentum values - K.set_value(self.model.optimizer.lr, self.initial_lr) - if self.cycle_momentum: - try: - K.set_value(self.model.optimizer.momentum, self.momentum) - except: - K.set_value(self.model.optimizer.beta_1, self.momentum) - - def on_train_batch_end(self, batch, logs=None) -> None: - # Grab the current learning rate & momentum - lr = float(K.get_value(self.model.optimizer.lr)) - try: - mom = float(K.get_value(self.model.optimizer.momentum)) - except: - mom = float(K.get_value(self.model.optimizer.beta_1)) - # Append to the list - self.track_lr.append(lr) - self.track_mom.append(mom) - # Update learning rate & momentum - self.set_lr_mom() - # increment step_num - self.step_num += 1 - - def plot_lrs_moms(self, axes=None) -> None: - if axes == None: - _, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) - else: - try: - ax1, ax2 = axes - except: - ax1, ax2 = axes[0], axes[1] - ax1.plot(self.track_lr) - ax1.set_title("Learning Rate vs Steps") - ax2.plot(self.track_mom) - ax2.set_title("Momentum (or beta_1) vs Steps") +from tensorflow import keras +import tensorflow as tf +import math +import matplotlib.pyplot as plt +import numpy as np + +K = keras.backend + + +class OneCycleLr(keras.callbacks.Callback): + """ + Sets the learning rate of each parameter group according to the + 1cycle learning rate policy. The 1cycle policy anneals the learning + rate from an initial learning rate to some maximum learning rate and then + from that maximum learning rate to some minimum learning rate much lower + than the initial learning rate. + This policy was initially described in the paper `Super-Convergence: + Very Fast Training of Neural Networks Using Large Learning Rates`_. + + [Implementation taken from PyTorch: + (https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR)] + + Note also that the total number of steps in the cycle can be determined in one + of two ways (listed in order of precedence): + + #. A value for total_steps is explicitly provided. + #. A number of epochs (epochs) and a number of steps per epoch + (steps_per_epoch) are provided. + In this case, the number of total steps is inferred by + total_steps = epochs * steps_per_epoch + You must either provide a value for total_steps or provide a value for both + epochs and steps_per_epoch. + + Args: + max_lr (float): Upper learning rate boundaries in the cycle. + total_steps (int): The total number of steps in the cycle. Note that + if a value is not provided here, then it must be inferred by providing + a value for epochs and steps_per_epoch. + Default: None + epochs (int): The number of epochs to train for. This is used along + with steps_per_epoch in order to infer the total number of steps in the cycle + if a value for total_steps is not provided. + Default: None + steps_per_epoch (int): The number of steps per epoch to train for. This is + used along with epochs in order to infer the total number of steps in the + cycle if a value for total_steps is not provided. + Default: None + pct_start (float): The percentage of the cycle (in number of steps) spent + increasing the learning rate. + Default: 0.3 + anneal_strategy (str): {'cos', 'linear'} + Specifies the annealing strategy: "cos" for cosine annealing, "linear" for + linear annealing. + Default: 'cos' + cycle_momentum (bool): If ``True``, momentum is cycled inversely + to learning rate between 'base_momentum' and 'max_momentum'. + Default: True + base_momentum (float): Lower momentum boundaries in the cycle + for each parameter group. Note that momentum is cycled inversely + to learning rate; at the peak of a cycle, momentum is + 'base_momentum' and learning rate is 'max_lr'. + Default: 0.85 + max_momentum (float or list): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (max_momentum - base_momentum). + Note that momentum is cycled inversely + to learning rate; at the start of a cycle, momentum is 'max_momentum' + and learning rate is 'base_lr' + Default: 0.95 + div_factor (float): Determines the initial learning rate via + initial_lr = max_lr/div_factor + Default: 25 + final_div_factor (float): Determines the minimum learning rate via + min_lr = initial_lr/final_div_factor + Default: 1e4 + """ + + def __init__(self, + max_lr: float, + total_steps: int = None, + epochs: int = None, + steps_per_epoch: int = None, + pct_start: float = 0.3, + anneal_strategy: str = "cos", + cycle_momentum: bool = True, + base_momentum: float = 0.85, + max_momentum: float = 0.95, + div_factor: float = 25.0, + final_div_factor: float = 1e4, + ) -> None: + + super(OneCycleLr, self).__init__() + + # validate total steps: + if total_steps is None and epochs is None and steps_per_epoch is None: + raise ValueError( + "You must define either total_steps OR (epochs AND steps_per_epoch)" + ) + elif total_steps is not None: + if total_steps <= 0 or not isinstance(total_steps, int): + raise ValueError( + "Expected non-negative integer total_steps, but got {}".format( + total_steps + ) + ) + self.total_steps = total_steps + else: + if epochs <= 0 or not isinstance(epochs, int): + raise ValueError( + "Expected non-negative integer epochs, but got {}".format( + epochs) + ) + if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int): + raise ValueError( + "Expected non-negative integer steps_per_epoch, but got {}".format( + steps_per_epoch + ) + ) + # Compute total steps + self.total_steps = epochs * steps_per_epoch + + self.step_num = 0 + self.step_size_up = float(pct_start * self.total_steps) - 1 + self.step_size_down = float(self.total_steps - self.step_size_up) - 1 + + # Validate pct_start + if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): + raise ValueError( + "Expected float between 0 and 1 pct_start, but got {}".format( + pct_start) + ) + + # Validate anneal_strategy + if anneal_strategy not in ["cos", "linear"]: + raise ValueError( + "anneal_strategy must by one of 'cos' or 'linear', instead got {}".format( + anneal_strategy + ) + ) + elif anneal_strategy == "cos": + self.anneal_func = self._annealing_cos + elif anneal_strategy == "linear": + self.anneal_func = self._annealing_linear + + # Initialize learning rate variables + self.initial_lr = max_lr / div_factor + self.max_lr = max_lr + self.min_lr = self.initial_lr / final_div_factor + + # Initial momentum variables + self.cycle_momentum = cycle_momentum + if self.cycle_momentum: + self.m_momentum = max_momentum + self.momentum = max_momentum + self.b_momentum = base_momentum + + # Initialize variable to learning_rate & momentum + self.track_lr = [] + self.track_mom = [] + + def _annealing_cos(self, start, end, pct) -> float: + "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." + cos_out = math.cos(math.pi * pct) + 1 + return end + (start - end) / 2.0 * cos_out + + def _annealing_linear(self, start, end, pct) -> float: + "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." + return (end - start) * pct + start + + def set_lr_mom(self) -> None: + """Update the learning rate and momentum""" + if self.step_num <= self.step_size_up: + # update learining rate + computed_lr = self.anneal_func( + self.initial_lr, self.max_lr, self.step_num / self.step_size_up + ) + K.set_value(self.model.optimizer.lr, computed_lr) + # update momentum if cycle_momentum + if self.cycle_momentum: + computed_momentum = self.anneal_func( + self.m_momentum, self.b_momentum, self.step_num / self.step_size_up + ) + try: + K.set_value(self.model.optimizer.momentum, + computed_momentum) + except: + K.set_value(self.model.optimizer.beta_1, computed_momentum) + else: + down_step_num = self.step_num - self.step_size_up + # update learning rate + computed_lr = self.anneal_func( + self.max_lr, self.min_lr, down_step_num / self.step_size_down + ) + K.set_value(self.model.optimizer.lr, computed_lr) + # update momentum if cycle_momentum + if self.cycle_momentum: + computed_momentum = self.anneal_func( + self.b_momentum, + self.m_momentum, + down_step_num / self.step_size_down, + ) + try: + K.set_value(self.model.optimizer.momentum, + computed_momentum) + except: + K.set_value(self.model.optimizer.beta_1, computed_momentum) + + def on_train_begin(self, logs=None) -> None: + # Set initial learning rate & momentum values + K.set_value(self.model.optimizer.lr, self.initial_lr) + if self.cycle_momentum: + try: + K.set_value(self.model.optimizer.momentum, self.momentum) + except: + K.set_value(self.model.optimizer.beta_1, self.momentum) + + def on_train_batch_end(self, batch, logs=None) -> None: + # Grab the current learning rate & momentum + lr = float(K.get_value(self.model.optimizer.lr)) + try: + mom = float(K.get_value(self.model.optimizer.momentum)) + except: + mom = float(K.get_value(self.model.optimizer.beta_1)) + # Append to the list + self.track_lr.append(lr) + self.track_mom.append(mom) + # Update learning rate & momentum + self.set_lr_mom() + # increment step_num + self.step_num += 1 + + def plot_lrs_moms(self, axes=None) -> None: + if axes == None: + _, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) + else: + try: + ax1, ax2 = axes + except: + ax1, ax2 = axes[0], axes[1] + ax1.plot(self.track_lr) + ax1.set_title("Learning Rate vs Steps") + ax2.plot(self.track_mom) + ax2.set_title("Momentum (or beta_1) vs Steps") diff --git a/Interface/CLI/Data/Utils/print_color_V1_OLD.py b/Interface/CLI/Data/Utils/print_color_V1_OLD.py index 15cd24a..7f37955 100644 --- a/Interface/CLI/Data/Utils/print_color_V1_OLD.py +++ b/Interface/CLI/Data/Utils/print_color_V1_OLD.py @@ -1,89 +1,89 @@ -#the print_Color func -def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False, return_str: bool = False): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. - colors (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. - print_END (str): The string appended after the final output. Default is '\\n'. - advanced_mode (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. - return_str (bool): If True, returns the colored string instead of printing it. Default is False. - Examples: - ~~~python - print_Color('Hello, World!', ['green']) - # Prints 'Hello, World!' in green. - - print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) - # Prints 'Hello in green' in green and 'Hello in red' in red. - - Note: - The advanced terminal colors can be used by providing the escape sequences directly in the colors list. - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m', - 'bold': '\x1b[1m', - 'underline': '\x1b[4m', - 'blink': '\x1b[5m' - } - return_temp = '' - if not advanced_mode: - if colors[0] in color_code: - if return_str: - return color_code[colors[0]] + Input + '\x1b[0m' - print(color_code[colors[0]] + Input + '\x1b[0m', end=print_END) - else: - print("[print_Color] ERROR: Invalid color input!!!") - else: - substrings = Input.split('~*') - if len(substrings) != len(colors) + 1: - print( - "[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") - else: - for sub_str, color in zip(substrings, ['normal'] + colors): - if color in color_code: - if return_str: - return_temp += color_code[color] + sub_str + '\x1b[0m' - else: - print(color_code[color] + sub_str + '\x1b[0m', end='') - else: - print( - f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") - print('', end=print_END) - if return_str: - return return_temp +#the print_Color func +def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False, return_str: bool = False): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. + colors (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. + print_END (str): The string appended after the final output. Default is '\\n'. + advanced_mode (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. + return_str (bool): If True, returns the colored string instead of printing it. Default is False. + Examples: + ~~~python + print_Color('Hello, World!', ['green']) + # Prints 'Hello, World!' in green. + + print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) + # Prints 'Hello in green' in green and 'Hello in red' in red. + + Note: + The advanced terminal colors can be used by providing the escape sequences directly in the colors list. + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m', + 'bold': '\x1b[1m', + 'underline': '\x1b[4m', + 'blink': '\x1b[5m' + } + return_temp = '' + if not advanced_mode: + if colors[0] in color_code: + if return_str: + return color_code[colors[0]] + Input + '\x1b[0m' + print(color_code[colors[0]] + Input + '\x1b[0m', end=print_END) + else: + print("[print_Color] ERROR: Invalid color input!!!") + else: + substrings = Input.split('~*') + if len(substrings) != len(colors) + 1: + print( + "[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") + else: + for sub_str, color in zip(substrings, ['normal'] + colors): + if color in color_code: + if return_str: + return_temp += color_code[color] + sub_str + '\x1b[0m' + else: + print(color_code[color] + sub_str + '\x1b[0m', end='') + else: + print( + f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") + print('', end=print_END) + if return_str: + return return_temp #the func end \ No newline at end of file diff --git a/Interface/CLI/Data/Utils/print_color_V2_NEW.py b/Interface/CLI/Data/Utils/print_color_V2_NEW.py index 603d673..15b73a3 100644 --- a/Interface/CLI/Data/Utils/print_color_V2_NEW.py +++ b/Interface/CLI/Data/Utils/print_color_V2_NEW.py @@ -1,76 +1,76 @@ -import re - -def print_Color_V2(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. '' is used to specify the color of the following text. - print_END (str): The string appended after the final output. Default is '\\n'. - start_char (str): The character used as the start of the color specifier. Default is '<'. - end_char (str): The character used as the end of the color specifier. Default is '>'. - - Examples: - ~~~python - print_Color('Hello, World!') - # Prints 'Hello, World!' in normal color. - - print_Color('Hello in red Hello in green') - # Prints 'Hello in red' in red and 'Hello in green' in green. - - print_Color('~red!Hello in red', start_char='~', end_char='!') - # Prints 'Hello, World!' in normal color. - - Note: - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m' - } - pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) - substrings = re.split(pattern, Input) - current_color = 'normal' - for i, sub_str in enumerate(substrings): - if i % 2 == 0: - print(color_code[current_color] + sub_str + color_code['normal'], end='') - current_color = 'normal' - else: - color = sub_str.strip() - if color in color_code: - current_color = color - else: - print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") - print('', end=print_END) +import re + +def print_Color_V2(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. '' is used to specify the color of the following text. + print_END (str): The string appended after the final output. Default is '\\n'. + start_char (str): The character used as the start of the color specifier. Default is '<'. + end_char (str): The character used as the end of the color specifier. Default is '>'. + + Examples: + ~~~python + print_Color('Hello, World!') + # Prints 'Hello, World!' in normal color. + + print_Color('Hello in red Hello in green') + # Prints 'Hello in red' in red and 'Hello in green' in green. + + print_Color('~red!Hello in red', start_char='~', end_char='!') + # Prints 'Hello, World!' in normal color. + + Note: + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m' + } + pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) + substrings = re.split(pattern, Input) + current_color = 'normal' + for i, sub_str in enumerate(substrings): + if i % 2 == 0: + print(color_code[current_color] + sub_str + color_code['normal'], end='') + current_color = 'normal' + else: + color = sub_str.strip() + if color in color_code: + current_color = color + else: + print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") + print('', end=print_END) diff --git a/Interface/CLI/Data/requirements.txt b/Interface/CLI/Data/requirements.txt index fc2108f..47a89fa 100644 --- a/Interface/CLI/Data/requirements.txt +++ b/Interface/CLI/Data/requirements.txt @@ -1,10 +1,10 @@ -numpy -keras -Pillow -py-cpuinfo -tensorflow -efficientnet -tqdm -matplotlib -opencv-python +numpy +keras +Pillow +py-cpuinfo +tensorflow +efficientnet +tqdm +matplotlib +opencv-python loguru \ No newline at end of file diff --git a/Interface/CLI/LICENSE b/Interface/CLI/LICENSE index 861128e..6bb4d95 100644 --- a/Interface/CLI/LICENSE +++ b/Interface/CLI/LICENSE @@ -1,21 +1,21 @@ -MIT License - -Copyright (c) 2023 Aydin hamedi - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +MIT License + +Copyright (c) 2023 Aydin hamedi + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Interface/GUI/Data/GUI_main.py b/Interface/GUI/Data/GUI_main.py index 5d044c9..901214e 100644 --- a/Interface/GUI/Data/GUI_main.py +++ b/Interface/GUI/Data/GUI_main.py @@ -1,734 +1,734 @@ -# Copyright (c) 2023 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT - -# start L1 -print('Loading the GUI...', end='\r') -# pylib -import os -import re -import time -import cv2 -import sys -import json -import queue -import hashlib -import pydicom -import cpuinfo -import difflib -import inspect -import traceback -import subprocess -import threading -import requests -from tqdm import tqdm -from time import sleep -import PySimpleGUI as sg -from loguru import logger -import efficientnet.tfkeras -from tkinter import filedialog -from datetime import datetime -from PIL import Image -import tensorflow as tf -from keras.models import load_model -from keras.preprocessing.image import ImageDataGenerator -from keras.utils import to_categorical -import numpy as np - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.Grad_cam import make_gradcam_heatmap -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from Utils.Other import * - -# global vars>>> -# CONST SYS -GUI_Ver = '0.9.0.1' -Model_dir = 'Data/PAI_model' # without file extention -Database_dir = 'Data/dataset.npy' -IMG_AF = ('JPEG', 'PNG', 'BMP', 'TIFF', 'JPG', 'DCM', 'DICOM') -Github_repo_Releases_Model_name = 'PAI_model_T.h5' -Github_repo_Releases_Model_light_name = 'PAI_model_light_T.h5' -Github_repo_Releases_Model_info_name = 'model_info.json' -Github_repo_Releases_URL = 'https://api.github.com/repos/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest' -Model_FORMAT = 'H5_SF' # TF_dir/H5_SF -IMG_RES = (224, 224, 3) -train_epochs_def = 4 -SHOW_CSAA_OS = False -Show_GUI_debug = False -# normal global -img_array = None -label = None -model = None -# Other -class CustomQueue: - # Custom queue class with size limit - # - # Initializes a Queue instance with a max size. Provides put(), get(), - # and is_updated() methods to add items, retrieve items, and check if - # updated since last get() call. - def __init__(self, max_items=4): - self.q = queue.Queue() - self.max_items = max_items - self.is_updated = False - - def put(self, item): - if self.q.qsize() == self.max_items: - self.q.get() - self.q.put(item) - self.is_updated = True - - def get(self, reset_updated=True): - items = list(self.q.queue) - if reset_updated: - self.is_updated = False - return items - - def is_updated(self): - return self.is_updated - -# GUI_Queue -GUI_Queue = { - '-Main_log-': CustomQueue(max_items=128) -} -logger.remove() -logger.add('Data\\logs\\SYS_LOG_{time}.log', - backtrace=True, diagnose=True, compression='zip') -logger.info('GUI Start...\n') -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) -# Making the GUI layout >>> -# prep GUI -sg.theme('GrayGrayGray') -# Main -GUI_layout_Tab_main = [ - [sg.Text('Enter the image dir:', font=(None, 10, 'bold'))], - [ - sg.Input(key='-INPUT_IMG_dir-'), - sg.Button('Browse', key='-BUTTON_BROWSE_IMG_dir-'), - sg.Button('Ok', key='-BUTTON_OK_IMG_dir-') - ], - [sg.Text('Log:', font=(None, 10, 'bold'))], - [sg.Multiline(key='-OUTPUT_ST-', size=(54, 6), autoscroll=True)], - [sg.Text('Result:', font=(None, 10, 'bold'))], - [sg.Text(key='-OUTPUT_ST_R-', size=(50, 2), background_color='white')], - [ - sg.Checkbox('Show Grad-CAM', key='-CHECKBOX_SHOW_Grad-CAM-', default=True), - sg.Checkbox('Show DICOM Info', key='-CHECKBOX_SHOW_DICOM_INFO-', default=True) - ], - [ - sg.Button('Analyse'), - sg.Button('Close') - ] -] -# Ai Model -GUI_layout_Tab_Ai_Model = [ - [sg.Text('Ai Model Settings:', font=(None, 10, 'bold'))], - [ - sg.Button('Update/Download Model', key='-BUTTON_UPDATE_MODEL-'), - sg.Button('Reload Model', key='-BUTTON_RELOAD_MODEL-') - ], - [ - sg.Checkbox('Download Light Model', key='-CHECKBOX_DOWNLOAD_LIGHT_MODEL-', default=False) - ], - [sg.Text('Ai Model Info:', font=(None, 10, 'bold'))], - [ - sg.Text(key='-OUTPUT_Model_info-', size=(40, 7), pad=(4, 0)) - ] -] - -# DICOM Info -def C_GUI_layout_DICOM_Info_Window() -> list: - """Returns the layout for the DICOM Info tab. - - This consists of a single Multiline element to display the DICOM metadata. - - Returns: - list: The layout as a list of rows. - """ - return [[sg.Multiline(key='-OUTPUT_DICOM_Info-', size=(120, 40), font=(None, 11, 'normal'), autoscroll=True)]] - -# GUI logo -GUI_text_logo = ''' -~* - _______ __ __ __ .___ ___. ______ _______ _______ - / _____|| | | | | | | \/ | / __ \ | \ | ____| -| | __ | | | | | | | \ / | | | | | | .--. || |__ -| | |_ | | | | | | | | |\/| | | | | | | | | || __| -| |__| | | `--' | | | | | | | | `--' | | '--' || |____ - \______| \______/ |__| |__| |__| \______/ |_______/ |_______| -~* - ______ .__ __. - / __ \ | \ | | -| | | | | \| | -| | | | | . ` | -| `--' | | |\ | - \______/ |__| \__| - -''' - -# HF>>> -# calculate_file_hash -def calculate_file_hash(file_path) -> str: - '''Calculates a SHA256 hash for the contents of the given file. - - Args: - file_path (str): The path to the file to hash. - - Returns: - str: The hex string of the SHA256 hash. - ''' - with open(file_path, 'rb') as f: - bytes = f.read() - readable_hash = hashlib.sha256(bytes).hexdigest() - return readable_hash - -# get_model_info -def get_model_info(model_path) -> dict: - '''Gets information about a model file. - - Checks if the model file exists at the given path, calculates its hash, - and looks up version information in a JSON file if it exists. - - Args: - model_path: Path to the model file. - - Returns: - Dict with file hash, whether it exists, version, and model type. - ''' - - # Check if the model exists - model_exists = os.path.exists(model_path) - - if model_exists: - # Calculate the hash of the file - file_hash = calculate_file_hash(model_path) - - # Load the JSON data - with open('Data/model_info.json', 'r') as json_file: - model_info = json.load(json_file) - - # Check if the file's hash is in the JSON data - if file_hash in model_info: - # Return the 'Ver' and 'stored_type' attributes for the file - return { - 'file_hash': file_hash, - 'file_exists': True, - 'Ver': model_info[file_hash]['Ver'], - 'stored_type': model_info[file_hash]['stored_type'] - } - else: - return { - 'file_hash': file_hash, - 'file_exists': True, - 'Ver': 'Unknown', - 'stored_type': 'Unknown' - } - else: - return { - 'file_hash': 'Unknown', - 'file_exists': False, - 'Ver': 'Unknown', - 'stored_type': 'Unknown' - } - -# open_file_GUI -def open_file_GUI() -> str: - '''Opens a file selection dialog GUI to allow the user to select an image file. - - Builds a filetypes filter from the IMG_AF global variable, joins the extensions - together into a filter string, converts to lowercase. Opens the file dialog, - and returns the selected file path if one was chosen. - - Returns: - str: The path to the selected image file, or None if no file was chosen. - ''' - formats = ';*.'.join(IMG_AF) - formats = '*.' + formats.lower() - file_path = filedialog.askopenfilename( - filetypes=[('Image Files', formats)]) - if file_path: - return file_path - -# download_file_from_github -def download_file_from_github(url: str, file_name: str, save_as: str, chunk_size: int) -> None: - '''Downloads a file from a GitHub release API URL to a local path. - - Args: - url (str): The GitHub API URL for the release to download from. - file_name (str): The name of the file to download from the release. - save_as (str): The local path to save the downloaded file to. - chunk_size (int): The chunk size to use when streaming the download. - ''' - response = requests.get(url) - data = response.json() - logger.debug(f'download_file_from_github:data(json) {data}') - # Get the name of the latest release - release_name = data['name'] - print(f'Latest release: {release_name}') - GUI_Queue['-Main_log-'].put(f'Latest Github repo release: {release_name}') - - # Get the assets of the latest release - assets = data['assets'] - - # Find the required asset in the assets - for asset in assets: - if asset['name'] == file_name: - download_url = asset['browser_download_url'] - break - if 'download_url' in locals(): - # Download the file with a progress bar - response = requests.get(download_url, stream=True) - file_size = int(response.headers['Content-Length']) - progress_bar = tqdm(total=file_size, unit='b', unit_scale=True) - - with open(save_as, 'wb') as f: - for chunk in response.iter_content(chunk_size=chunk_size): - progress_bar.update(len(chunk)) - f.write(chunk) - - progress_bar.close() - - if file_size != 0 and progress_bar.n != file_size: - print_Color('~*ERROR: ~*Something went wrong while downloading the file.', ['red', 'yellow'], - advanced_mode=True) - GUI_Queue['-Main_log-'].put('ERROR: Something went wrong while downloading the file.') - logger.warning('download_file_from_github>>ERROR: Something went wrong while downloading the file.') - else: - print(f'File "{save_as}" downloaded successfully.') - logger.debug(f'download_file_from_github>>Debug: File "{save_as}" downloaded successfully.') - else: - print_Color('~*ERROR: ~*Something went wrong while finding the file.', ['red', 'yellow'], advanced_mode=True) - GUI_Queue['-Main_log-'].put('ERROR: Something went wrong while finding the file.') - logger.warning('download_file_from_github>>ERROR: Something went wrong while finding the file.') - -# CF>>> -# CI_ulmd -def CI_ulmd() -> None: - """Prints a warning that model data upload is currently unavailable.""" - print_Color( - 'Warning: upload model data set (currently not available!!!)', - ['yellow']) - -# CI_pwai -def CI_pwai(show_gradcam: bool = True) -> str: - """ - CI_pwai predicts pneumonia from an input image using a pre-trained deep learning model. - - It loads the model if not already loaded, runs prediction, computes confidence score - and class name. Optionally displays GradCAM visualization heatmap. - - Returns: - str: Prediction result string with class name, confidence score and warnings. - """ - # global var import - global model - # check for input img - if img_array is not None: - try: - if model is None: - print_Color('loading the Ai model...', ['normal']) - model = load_model(Model_dir) - except (ImportError, IOError): - return 'ERROR: Failed to load the model.' - else: - print_Color('predicting with the Ai model...', ['normal']) - model_prediction_ORG = model.predict(img_array) - model_prediction = np.argmax(model_prediction_ORG, axis=1) - pred_class = 'PNEUMONIA' if model_prediction == 1 else 'NORMAL' - confidence = np.max(model_prediction_ORG) - return_temp = f'the Ai model prediction: {pred_class} with confidence {confidence:.2f}.' - if confidence < 0.82: - return_temp += 'WARNING: the confidence is low.' - if model_prediction == 1 and show_gradcam: - clahe = cv2.createCLAHE(clipLimit=1.8) - Grad_cam_heatmap = make_gradcam_heatmap(img_array, - model, 'top_activation', - second_last_conv_layer_name='top_conv', - sensitivity_map=2, - pred_index=tf.argmax(model_prediction_ORG[0])) - Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2])) - Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap) - Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS) - Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255) - # Resize the heatmap for a larger display - display_size = (600, 600) # Change this to your desired display size - Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap[0], display_size) - reference_image = np.uint8(cv2.resize(img_array[0] * 255, display_size)) - # Apply the CLAHE algorithm to the reference image - reference_image_CLAHE = np.clip(clahe.apply(cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)), 0, 255) - # Display the heatmap in a new window - cv2.imshow('Grad-CAM Heatmap', Grad_cam_heatmap) - cv2.imshow('Reference Original Image', reference_image) - cv2.imshow('Reference Original Image (CLAHE)', reference_image_CLAHE) - return return_temp - else: - print_Color('~*ERROR: ~*image data doesnt exist.', - ['red', 'yellow'], advanced_mode=True, return_str=True) - -# CI_rlmw -def CI_rlmw() -> None: - """Loads the AI model on startup. - - Tries to load the model from the Model_dir path. If successful, logs a message to the GUI queue. If loading fails, logs an error. - """ - # global var import - global model - # main proc - model = None - GUI_Queue['-Main_log-'].put('loading the Ai model...') - try: - model = load_model(Model_dir) - except (ImportError, IOError): - GUI_Queue['-Main_log-'].put('ERROR: Failed to load the model.') - return None - GUI_Queue['-Main_log-'].put('loading the Ai model done.') - -# CI_liid -def CI_liid(img_dir, Show_DICOM_INFO: bool = True) -> str: - """Loads an image from the given image file path into a numpy array for model prediction. - - Supports JPEG, PNG and DICOM image formats. Resizes images to the model input shape, normalizes pixel values, - adds batch dimension, and provides optional DICOM metadata output. - - Args: - img_dir: File path of image to load. - Show_DICOM_INFO: Whether to output DICOM metadata to GUI window. - - Returns: - Status message string indicating if image was loaded successfully. - - """ - # global var import - global img_array - # check for img - logger.debug(f'CI_liid:img_dir {img_dir}') - # Extract file extension from img_dir - try: - _, file_extension = os.path.splitext(img_dir) - except TypeError: - file_extension = 'TEMP FILE EXTENSION' - if file_extension.upper()[1:] not in IMG_AF: - logger.warning('CI_liid>>ERROR: Invalid file format. Please provide an image file.') - return 'ERROR: Invalid file format. Please provide an image file.' - else: - try: - # Load and resize the image - if file_extension.upper()[1:] in ['DICOM', 'DCM']: - ds = pydicom.dcmread(img_dir) - img = Image.fromarray(ds.pixel_array).resize(IMG_RES[:2]) - if Show_DICOM_INFO: - GUI_layout_DICOM_Info_Window_layout = C_GUI_layout_DICOM_Info_Window() - GUI_layout_DICOM_Info_Window = sg.Window('DICOM Info - File Metadata', - GUI_layout_DICOM_Info_Window_layout, finalize=True) - # Write DICOM info to the window - for element in ds: - if element.name != 'Pixel Data': - tag_info = f'[Tag: {element.tag} | VR: {element.VR}]' - name_info = f'(Name: {element.name})' - value_info = f'>Value: {element.value}' - GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(tag_info, text_color='blue', - end='') - GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(name_info, text_color='green', - end='') - GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(value_info, text_color='black', - end='\n') - GUI_layout_DICOM_Info_Window.finalize() - else: - img = Image.open(img_dir).resize((IMG_RES[1], IMG_RES[0])) - except NameError: - logger.warning('CI_liid>>ERROR: Invalid file dir. Please provide an image file.') - return 'ERROR: Invalid file dir. Please provide an image file.' - else: - # Check for RGB mode - if img.mode != 'RGB': - img = img.convert('RGB') - # Convert to numpy array - img_array = np.asarray(img) - - # Normalize pixel values to [0, 1] - img_array = img_array / 255.0 - - # Add a dimension to transform from (height, width, channels) to (batch_size, height, width, channels) - img_array = np.expand_dims(img_array, axis=0) - - return 'Image loaded.' - -# CI_uaim -def CI_uaim(download_light_model: bool = False) -> None: - """Downloads the model from GitHub releases. - - Handles logging status messages to the GUI queue and any errors. - Supports downloading the full model or a smaller "light" model. - """ - if download_light_model: - Log_temp_txt = 'Downloading the light model...' - Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_light_name - else: - Log_temp_txt = 'Downloading the model...' - Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_name - GUI_Queue['-Main_log-'].put(Log_temp_txt) - try: - download_file_from_github(Github_repo_Releases_URL, - Github_repo_Releases_Model_name_temp, - Model_dir, - 1024) - CI_rlmw() - print('Model downloaded.') - except Exception: - GUI_Queue['-Main_log-'].put('ERROR: Failed to download the model.') - GUI_Queue['-Main_log-'].put('Model downloaded.') - -# CI_umij -def CI_umij() -> None: - """Downloads the model info JSON file from GitHub releases. - - Handles logging status messages to the GUI queue and any errors. - The model info file contains metadata about the model version. - """ - try: - download_file_from_github(Github_repo_Releases_URL, - Github_repo_Releases_Model_info_name, - 'Data\\model_info.json', - 256) - except Exception: - GUI_Queue['-Main_log-'].put('ERROR: Failed to download the model info.') - GUI_Queue['-Main_log-'].put('Model info downloaded.') - -# CI_gmi -def CI_gmi() -> str: - if not os.path.isfile('Data\\model_info.json') or time.time() - os.path.getmtime( - 'Data/model_info.json') > 4 * 60 * 60: - CI_umij() - model_info_dict = get_model_info(Model_dir) - if model_info_dict['Ver'] != 'Unknown': - Model_State = 'OK' - elif model_info_dict['Ver'] == 'Unknown' and model_info_dict['file_exists']: - Model_State = 'Model is not a valid model. (hash not found!)' - else: - Model_State = 'Model file is missing.' - model_info_str = f'File_exists: {str(model_info_dict["file_exists"])}\n' - model_info_str += f'Model_hash (SHA256): {model_info_dict["file_hash"].strip()}\n' - model_info_str += f'stored_type: {model_info_dict["stored_type"]}\n' - model_info_str += f'State: {Model_State}\n' - model_info_str += f'Ver: {model_info_dict["Ver"]}' - return model_info_str - -# funcs(INTERNAL)>>> -# IEH -def IEH(id: str = 'Unknown', stop: bool = True, DEV: bool = True) -> None: - """Prints an error message, logs the exception, optionally shows the traceback, and optionally exits. - - This is an internal error handler to nicely handle unexpected errors and optionally exit gracefully. - """ - print_Color(f'~*ERROR: ~*Internal error info/id:\n~*{id}~*.', ['red', 'yellow', 'bg_red', 'yellow'], - advanced_mode=True) - logger.exception(f'Internal Error Handler [stop:{stop}|DEV:{DEV}|id:{id}]') - if DEV: - sg.popup( - f'An internal error occurred.\nERROR-INFO:\n\nErr-ID:\n{id}\n\nErr-Traceback:\n{traceback.format_exc()}', - title=f'Internal Error Exit[{stop}]', - custom_text=('Exit')) - print_Color('detailed error message:', ['yellow']) - traceback.print_exc() - if stop: - logger.warning('SYS EXIT|ERROR: Internal|by Internal Error Handler') - sys.exit('SYS EXIT|ERROR: Internal|by Internal Error Handler') - -# UWL -def UWL(Only_finalize: bool = False) -> None: - """Updates the GUI window. - - This is an internal function to update the GUI window. - """ - # Update the GUI window - GUI_window.read(timeout=0) - if GUI_Queue['-Main_log-'].is_updated and not Only_finalize: - # Retrieve the result from the queue - result_expanded = '' - result = GUI_Queue['-Main_log-'].get() - print(f'Queue Data: {result}') - logger.debug(f'Queue:get: {result}') - # Update the GUI with the result message - for block in result: - result_expanded += f'> {block}\n' - GUI_window['-OUTPUT_ST-'].update(result_expanded, text_color='black') - GUI_window.finalize() - -# main -def main() -> None: - """Main function for the GUI. - """ - # start - sg.SystemTray.notify(f'Pneumonia-Detection-Ai-GUI', f'Gui started.\nV{GUI_Ver}') - if Show_GUI_debug: - sg.SystemTray.notify(f'Pneumonia-Detection-Ai-GUI', f'Looks like you are a programmer\nWow.\nV{GUI_Ver}') - sg.show_debugger_window() - # global - global GUI_window - # Text print - print_Color( - GUI_text_logo, - ['yellow', 'green'], - advanced_mode=True - ) - # prep var - IMG_dir = None - Update_model_info_LXT = None - # Create the tabs - GUI_tab_main = sg.Tab('Main', GUI_layout_Tab_main) - GUI_tab_other = sg.Tab('Ai Model', GUI_layout_Tab_Ai_Model) - GUI_layout_group = [[sg.TabGroup([[GUI_tab_main, GUI_tab_other]])]] - # Create the window - GUI_window = sg.Window(f'Pneumonia-Detection-Ai-GUI V{GUI_Ver}', GUI_layout_group) - # Pre up - CI_umij() - # Main loop for the Graphical User Interface (GUI) - while True: - # Read events and values from the GUI window - event, values = GUI_window.read(timeout=100, timeout_key='-TIMEOUT-') - if not event == '-TIMEOUT-': - logger.debug(f'GUI_window:event: {event}') - logger.debug(f'GUI_window:values: {values}') - - # Check if the window has been closed or the 'Close' button has been clicked - if event == sg.WINDOW_CLOSED or event == 'Close': - # close GUI_window - GUI_window.close() - # try to stop the CI_uaim_Thread - # try: - # CI_uaim_Thread.() - # except Exception: - # pass - break # Exit the loop and close the window - - # Handle event for updating the model - if event == '-BUTTON_RELOAD_MODEL-': - # Call the function to reload the model - CI_rlmw() - - # Handle event for browsing and selecting an image directory - if event == '-BUTTON_BROWSE_IMG_dir-': - # Open file dialog to select an image, and update the input field with the selected directory - IMG_dir = open_file_GUI() - GUI_window['-INPUT_IMG_dir-'].update(IMG_dir) - - # Handle event for confirming the selected image directory - if event == '-BUTTON_OK_IMG_dir-': - # Retrieve the image directory from the input field and update the display - IMG_dir = GUI_window['-INPUT_IMG_dir-'].get() - GUI_window['-INPUT_IMG_dir-'].update(IMG_dir) - - # Handle event for analyzing the selected image - if event == 'Analyse': - # Call the function to load the image and update the output status - Log_temp_txt = CI_liid(IMG_dir, Show_DICOM_INFO=values['-CHECKBOX_SHOW_DICOM_INFO-']) - GUI_Queue['-Main_log-'].put(Log_temp_txt) - UWL() - - # If the image is successfully loaded, proceed with analysis - if Log_temp_txt == 'Image loaded.': - GUI_Queue['-Main_log-'].put('Analyzing...') - UWL() - # Call the function to perform pneumonia analysis and display the results - Log_temp_txt2 = CI_pwai(show_gradcam=values['-CHECKBOX_SHOW_Grad-CAM-']) - logger.info(f'CI_pwai: {Log_temp_txt2}') - GUI_Queue['-Main_log-'].put('Done Analyzing.') - UWL() - GUI_window['-OUTPUT_ST_R-'].update( - Log_temp_txt2, - text_color='green' if 'NORMAL' in Log_temp_txt2 else 'red', - background_color='white' - ) - UWL() - - # Handle event for updating the AI model - if event == '-BUTTON_UPDATE_MODEL-': - # Start a new thread to download the model without freezing the GUI - CI_uaim_Thread = threading.Thread( - target=CI_uaim, - args=(values['-CHECKBOX_DOWNLOAD_LIGHT_MODEL-'],), - daemon=True - ) - CI_uaim_Thread.start() - # Updating the model info - if Update_model_info_LXT is None or time.time() - Update_model_info_LXT > 6: - Update_model_info_LXT = time.time() - GUI_window['-OUTPUT_Model_info-'].update(CI_gmi(), text_color='black') - UWL(Only_finalize=True) - # Continuously check if there are results in the queue to be processed '-Main_log-' - if GUI_Queue['-Main_log-'].is_updated: - # Retrieve the result from the queue - result_expanded = '' - result = GUI_Queue['-Main_log-'].get() - print(f'Queue Data: {result}') - logger.debug(f'Queue[-Main_log-]:get: {result}') - # Update the GUI with the result message - for block in result: - result_expanded += f'> {block}\n' - GUI_window['-OUTPUT_ST-'].update(result_expanded, text_color='black') - UWL() - - -# start>>> -# clear the 'start L1' prompt -print(' ', end='\r') -# Start INFO -VER = f'V{GUI_Ver}' + datetime.now().strftime(' | CDT(%Y/%m/%d | %H:%M:%S)') -gpus = tf.config.list_physical_devices('GPU') -if gpus: - TF_MODE = 'GPU' - TF_sys_details = tf.sysconfig.get_build_info() - TF_CUDA_VER = TF_sys_details['cuda_version'] - TF_CUDNN_VER = TF_sys_details['cudnn_version'] # NOT USED - try: - gpu_name = subprocess.check_output( - ['nvidia-smi', '-L']).decode('utf-8').split(':')[1].split('(')[0].strip() - # GPU 0: NVIDIA `THE GPU NAME` (UUID: GPU-'xxxxxxxxxxxxxxxxxxxx') - # β”‚ β”‚ - # β”Œ---β”΄----------┐ β”Œ---β”΄----------┐ - # β”‚.split(':')[1]β”‚ β”‚.split('(')[0]β”‚ - # β””--------------β”˜ β””--------------β”˜ - except Exception: - gpu_name = '\x1b[0;31mNVIDIA-SMI-ERROR\x1b[0m' - TF_INFO = f'GPU NAME: {gpus[0].name}>>{gpu_name}, CUDA Version: {TF_CUDA_VER}' -else: - TF_MODE = 'CPU' - info = cpuinfo.get_cpu_info()['brand_raw'] - TF_INFO = f'{info}' -# GUI_Info -GUI_Info = f'PDAI Ver: {VER} \nPython Ver: {sys.version} \nTensorflow Ver: {tf.version.VERSION}, Mode: {TF_MODE}, {TF_INFO}' -logger.info(f'PDAI Ver: {VER}') -logger.info(f'Python Ver: {sys.version}') -logger.info(f'Tensorflow Ver: {tf.version.VERSION}') -logger.info(f'Mode: {TF_MODE}, {TF_INFO}') -print(GUI_Info) -# FP -if Model_FORMAT not in ['TF_dir', 'H5_SF']: - logger.info(f'Model file format [{Model_FORMAT}]') - IEH(id=f'F[SYS],P[FP],Error[Invalid Model_FORMAT]', DEV=False) -elif Model_FORMAT == 'H5_SF': - Model_dir += '.h5' -# start main -if __name__ == '__main__': - try: - try: - main() - except (EOFError, KeyboardInterrupt): - logger.info('KeyboardInterrupt.') - pass - except Exception as e: - IEH(id=f'F[SYS],RFunc[main],Error[{e}]', DEV=True) - else: - logger.info('GUI Exit.') - print_Color('\n~*[PDAI GUI] ~*closed.', - ['yellow', 'red'], advanced_mode=True) -else: - logger.info('GUI Imported.') -# end(EOF) +# Copyright (c) 2023 Aydin Hamedi +# +# This software is released under the MIT License. +# https://opensource.org/licenses/MIT + +# start L1 +print('Loading the GUI...', end='\r') +# pylib +import os +import re +import time +import cv2 +import sys +import json +import queue +import hashlib +import pydicom +import cpuinfo +import difflib +import inspect +import traceback +import subprocess +import threading +import requests +from tqdm import tqdm +from time import sleep +import PySimpleGUI as sg +from loguru import logger +import efficientnet.tfkeras +from tkinter import filedialog +from datetime import datetime +from PIL import Image +import tensorflow as tf +from keras.models import load_model +from keras.preprocessing.image import ImageDataGenerator +from keras.utils import to_categorical +import numpy as np + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.Grad_cam import make_gradcam_heatmap +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from Utils.Other import * + +# global vars>>> +# CONST SYS +GUI_Ver = '0.9.0.1' +Model_dir = 'Data/PAI_model' # without file extention +Database_dir = 'Data/dataset.npy' +IMG_AF = ('JPEG', 'PNG', 'BMP', 'TIFF', 'JPG', 'DCM', 'DICOM') +Github_repo_Releases_Model_name = 'PAI_model_T.h5' +Github_repo_Releases_Model_light_name = 'PAI_model_light_T.h5' +Github_repo_Releases_Model_info_name = 'model_info.json' +Github_repo_Releases_URL = 'https://api.github.com/repos/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest' +Model_FORMAT = 'H5_SF' # TF_dir/H5_SF +IMG_RES = (224, 224, 3) +train_epochs_def = 4 +SHOW_CSAA_OS = False +Show_GUI_debug = False +# normal global +img_array = None +label = None +model = None +# Other +class CustomQueue: + # Custom queue class with size limit + # + # Initializes a Queue instance with a max size. Provides put(), get(), + # and is_updated() methods to add items, retrieve items, and check if + # updated since last get() call. + def __init__(self, max_items=4): + self.q = queue.Queue() + self.max_items = max_items + self.is_updated = False + + def put(self, item): + if self.q.qsize() == self.max_items: + self.q.get() + self.q.put(item) + self.is_updated = True + + def get(self, reset_updated=True): + items = list(self.q.queue) + if reset_updated: + self.is_updated = False + return items + + def is_updated(self): + return self.is_updated + +# GUI_Queue +GUI_Queue = { + '-Main_log-': CustomQueue(max_items=128) +} +logger.remove() +logger.add('Data\\logs\\SYS_LOG_{time}.log', + backtrace=True, diagnose=True, compression='zip') +logger.info('GUI Start...\n') +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) +# Making the GUI layout >>> +# prep GUI +sg.theme('GrayGrayGray') +# Main +GUI_layout_Tab_main = [ + [sg.Text('Enter the image dir:', font=(None, 10, 'bold'))], + [ + sg.Input(key='-INPUT_IMG_dir-'), + sg.Button('Browse', key='-BUTTON_BROWSE_IMG_dir-'), + sg.Button('Ok', key='-BUTTON_OK_IMG_dir-') + ], + [sg.Text('Log:', font=(None, 10, 'bold'))], + [sg.Multiline(key='-OUTPUT_ST-', size=(54, 6), autoscroll=True)], + [sg.Text('Result:', font=(None, 10, 'bold'))], + [sg.Text(key='-OUTPUT_ST_R-', size=(50, 2), background_color='white')], + [ + sg.Checkbox('Show Grad-CAM', key='-CHECKBOX_SHOW_Grad-CAM-', default=True), + sg.Checkbox('Show DICOM Info', key='-CHECKBOX_SHOW_DICOM_INFO-', default=True) + ], + [ + sg.Button('Analyse'), + sg.Button('Close') + ] +] +# Ai Model +GUI_layout_Tab_Ai_Model = [ + [sg.Text('Ai Model Settings:', font=(None, 10, 'bold'))], + [ + sg.Button('Update/Download Model', key='-BUTTON_UPDATE_MODEL-'), + sg.Button('Reload Model', key='-BUTTON_RELOAD_MODEL-') + ], + [ + sg.Checkbox('Download Light Model', key='-CHECKBOX_DOWNLOAD_LIGHT_MODEL-', default=False) + ], + [sg.Text('Ai Model Info:', font=(None, 10, 'bold'))], + [ + sg.Text(key='-OUTPUT_Model_info-', size=(40, 7), pad=(4, 0)) + ] +] + +# DICOM Info +def C_GUI_layout_DICOM_Info_Window() -> list: + """Returns the layout for the DICOM Info tab. + + This consists of a single Multiline element to display the DICOM metadata. + + Returns: + list: The layout as a list of rows. + """ + return [[sg.Multiline(key='-OUTPUT_DICOM_Info-', size=(120, 40), font=(None, 11, 'normal'), autoscroll=True)]] + +# GUI logo +GUI_text_logo = ''' +~* + _______ __ __ __ .___ ___. ______ _______ _______ + / _____|| | | | | | | \/ | / __ \ | \ | ____| +| | __ | | | | | | | \ / | | | | | | .--. || |__ +| | |_ | | | | | | | | |\/| | | | | | | | | || __| +| |__| | | `--' | | | | | | | | `--' | | '--' || |____ + \______| \______/ |__| |__| |__| \______/ |_______/ |_______| +~* + ______ .__ __. + / __ \ | \ | | +| | | | | \| | +| | | | | . ` | +| `--' | | |\ | + \______/ |__| \__| + +''' + +# HF>>> +# calculate_file_hash +def calculate_file_hash(file_path) -> str: + '''Calculates a SHA256 hash for the contents of the given file. + + Args: + file_path (str): The path to the file to hash. + + Returns: + str: The hex string of the SHA256 hash. + ''' + with open(file_path, 'rb') as f: + bytes = f.read() + readable_hash = hashlib.sha256(bytes).hexdigest() + return readable_hash + +# get_model_info +def get_model_info(model_path) -> dict: + '''Gets information about a model file. + + Checks if the model file exists at the given path, calculates its hash, + and looks up version information in a JSON file if it exists. + + Args: + model_path: Path to the model file. + + Returns: + Dict with file hash, whether it exists, version, and model type. + ''' + + # Check if the model exists + model_exists = os.path.exists(model_path) + + if model_exists: + # Calculate the hash of the file + file_hash = calculate_file_hash(model_path) + + # Load the JSON data + with open('Data/model_info.json', 'r') as json_file: + model_info = json.load(json_file) + + # Check if the file's hash is in the JSON data + if file_hash in model_info: + # Return the 'Ver' and 'stored_type' attributes for the file + return { + 'file_hash': file_hash, + 'file_exists': True, + 'Ver': model_info[file_hash]['Ver'], + 'stored_type': model_info[file_hash]['stored_type'] + } + else: + return { + 'file_hash': file_hash, + 'file_exists': True, + 'Ver': 'Unknown', + 'stored_type': 'Unknown' + } + else: + return { + 'file_hash': 'Unknown', + 'file_exists': False, + 'Ver': 'Unknown', + 'stored_type': 'Unknown' + } + +# open_file_GUI +def open_file_GUI() -> str: + '''Opens a file selection dialog GUI to allow the user to select an image file. + + Builds a filetypes filter from the IMG_AF global variable, joins the extensions + together into a filter string, converts to lowercase. Opens the file dialog, + and returns the selected file path if one was chosen. + + Returns: + str: The path to the selected image file, or None if no file was chosen. + ''' + formats = ';*.'.join(IMG_AF) + formats = '*.' + formats.lower() + file_path = filedialog.askopenfilename( + filetypes=[('Image Files', formats)]) + if file_path: + return file_path + +# download_file_from_github +def download_file_from_github(url: str, file_name: str, save_as: str, chunk_size: int) -> None: + '''Downloads a file from a GitHub release API URL to a local path. + + Args: + url (str): The GitHub API URL for the release to download from. + file_name (str): The name of the file to download from the release. + save_as (str): The local path to save the downloaded file to. + chunk_size (int): The chunk size to use when streaming the download. + ''' + response = requests.get(url) + data = response.json() + logger.debug(f'download_file_from_github:data(json) {data}') + # Get the name of the latest release + release_name = data['name'] + print(f'Latest release: {release_name}') + GUI_Queue['-Main_log-'].put(f'Latest Github repo release: {release_name}') + + # Get the assets of the latest release + assets = data['assets'] + + # Find the required asset in the assets + for asset in assets: + if asset['name'] == file_name: + download_url = asset['browser_download_url'] + break + if 'download_url' in locals(): + # Download the file with a progress bar + response = requests.get(download_url, stream=True) + file_size = int(response.headers['Content-Length']) + progress_bar = tqdm(total=file_size, unit='b', unit_scale=True) + + with open(save_as, 'wb') as f: + for chunk in response.iter_content(chunk_size=chunk_size): + progress_bar.update(len(chunk)) + f.write(chunk) + + progress_bar.close() + + if file_size != 0 and progress_bar.n != file_size: + print_Color('~*ERROR: ~*Something went wrong while downloading the file.', ['red', 'yellow'], + advanced_mode=True) + GUI_Queue['-Main_log-'].put('ERROR: Something went wrong while downloading the file.') + logger.warning('download_file_from_github>>ERROR: Something went wrong while downloading the file.') + else: + print(f'File "{save_as}" downloaded successfully.') + logger.debug(f'download_file_from_github>>Debug: File "{save_as}" downloaded successfully.') + else: + print_Color('~*ERROR: ~*Something went wrong while finding the file.', ['red', 'yellow'], advanced_mode=True) + GUI_Queue['-Main_log-'].put('ERROR: Something went wrong while finding the file.') + logger.warning('download_file_from_github>>ERROR: Something went wrong while finding the file.') + +# CF>>> +# CI_ulmd +def CI_ulmd() -> None: + """Prints a warning that model data upload is currently unavailable.""" + print_Color( + 'Warning: upload model data set (currently not available!!!)', + ['yellow']) + +# CI_pwai +def CI_pwai(show_gradcam: bool = True) -> str: + """ + CI_pwai predicts pneumonia from an input image using a pre-trained deep learning model. + + It loads the model if not already loaded, runs prediction, computes confidence score + and class name. Optionally displays GradCAM visualization heatmap. + + Returns: + str: Prediction result string with class name, confidence score and warnings. + """ + # global var import + global model + # check for input img + if img_array is not None: + try: + if model is None: + print_Color('loading the Ai model...', ['normal']) + model = load_model(Model_dir) + except (ImportError, IOError): + return 'ERROR: Failed to load the model.' + else: + print_Color('predicting with the Ai model...', ['normal']) + model_prediction_ORG = model.predict(img_array) + model_prediction = np.argmax(model_prediction_ORG, axis=1) + pred_class = 'PNEUMONIA' if model_prediction == 1 else 'NORMAL' + confidence = np.max(model_prediction_ORG) + return_temp = f'the Ai model prediction: {pred_class} with confidence {confidence:.2f}.' + if confidence < 0.82: + return_temp += 'WARNING: the confidence is low.' + if model_prediction == 1 and show_gradcam: + clahe = cv2.createCLAHE(clipLimit=1.8) + Grad_cam_heatmap = make_gradcam_heatmap(img_array, + model, 'top_activation', + second_last_conv_layer_name='top_conv', + sensitivity_map=2, + pred_index=tf.argmax(model_prediction_ORG[0])) + Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2])) + Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap) + Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS) + Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255) + # Resize the heatmap for a larger display + display_size = (600, 600) # Change this to your desired display size + Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap[0], display_size) + reference_image = np.uint8(cv2.resize(img_array[0] * 255, display_size)) + # Apply the CLAHE algorithm to the reference image + reference_image_CLAHE = np.clip(clahe.apply(cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)), 0, 255) + # Display the heatmap in a new window + cv2.imshow('Grad-CAM Heatmap', Grad_cam_heatmap) + cv2.imshow('Reference Original Image', reference_image) + cv2.imshow('Reference Original Image (CLAHE)', reference_image_CLAHE) + return return_temp + else: + print_Color('~*ERROR: ~*image data doesnt exist.', + ['red', 'yellow'], advanced_mode=True, return_str=True) + +# CI_rlmw +def CI_rlmw() -> None: + """Loads the AI model on startup. + + Tries to load the model from the Model_dir path. If successful, logs a message to the GUI queue. If loading fails, logs an error. + """ + # global var import + global model + # main proc + model = None + GUI_Queue['-Main_log-'].put('loading the Ai model...') + try: + model = load_model(Model_dir) + except (ImportError, IOError): + GUI_Queue['-Main_log-'].put('ERROR: Failed to load the model.') + return None + GUI_Queue['-Main_log-'].put('loading the Ai model done.') + +# CI_liid +def CI_liid(img_dir, Show_DICOM_INFO: bool = True) -> str: + """Loads an image from the given image file path into a numpy array for model prediction. + + Supports JPEG, PNG and DICOM image formats. Resizes images to the model input shape, normalizes pixel values, + adds batch dimension, and provides optional DICOM metadata output. + + Args: + img_dir: File path of image to load. + Show_DICOM_INFO: Whether to output DICOM metadata to GUI window. + + Returns: + Status message string indicating if image was loaded successfully. + + """ + # global var import + global img_array + # check for img + logger.debug(f'CI_liid:img_dir {img_dir}') + # Extract file extension from img_dir + try: + _, file_extension = os.path.splitext(img_dir) + except TypeError: + file_extension = 'TEMP FILE EXTENSION' + if file_extension.upper()[1:] not in IMG_AF: + logger.warning('CI_liid>>ERROR: Invalid file format. Please provide an image file.') + return 'ERROR: Invalid file format. Please provide an image file.' + else: + try: + # Load and resize the image + if file_extension.upper()[1:] in ['DICOM', 'DCM']: + ds = pydicom.dcmread(img_dir) + img = Image.fromarray(ds.pixel_array).resize(IMG_RES[:2]) + if Show_DICOM_INFO: + GUI_layout_DICOM_Info_Window_layout = C_GUI_layout_DICOM_Info_Window() + GUI_layout_DICOM_Info_Window = sg.Window('DICOM Info - File Metadata', + GUI_layout_DICOM_Info_Window_layout, finalize=True) + # Write DICOM info to the window + for element in ds: + if element.name != 'Pixel Data': + tag_info = f'[Tag: {element.tag} | VR: {element.VR}]' + name_info = f'(Name: {element.name})' + value_info = f'>Value: {element.value}' + GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(tag_info, text_color='blue', + end='') + GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(name_info, text_color='green', + end='') + GUI_layout_DICOM_Info_Window['-OUTPUT_DICOM_Info-'].print(value_info, text_color='black', + end='\n') + GUI_layout_DICOM_Info_Window.finalize() + else: + img = Image.open(img_dir).resize((IMG_RES[1], IMG_RES[0])) + except NameError: + logger.warning('CI_liid>>ERROR: Invalid file dir. Please provide an image file.') + return 'ERROR: Invalid file dir. Please provide an image file.' + else: + # Check for RGB mode + if img.mode != 'RGB': + img = img.convert('RGB') + # Convert to numpy array + img_array = np.asarray(img) + + # Normalize pixel values to [0, 1] + img_array = img_array / 255.0 + + # Add a dimension to transform from (height, width, channels) to (batch_size, height, width, channels) + img_array = np.expand_dims(img_array, axis=0) + + return 'Image loaded.' + +# CI_uaim +def CI_uaim(download_light_model: bool = False) -> None: + """Downloads the model from GitHub releases. + + Handles logging status messages to the GUI queue and any errors. + Supports downloading the full model or a smaller "light" model. + """ + if download_light_model: + Log_temp_txt = 'Downloading the light model...' + Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_light_name + else: + Log_temp_txt = 'Downloading the model...' + Github_repo_Releases_Model_name_temp = Github_repo_Releases_Model_name + GUI_Queue['-Main_log-'].put(Log_temp_txt) + try: + download_file_from_github(Github_repo_Releases_URL, + Github_repo_Releases_Model_name_temp, + Model_dir, + 1024) + CI_rlmw() + print('Model downloaded.') + except Exception: + GUI_Queue['-Main_log-'].put('ERROR: Failed to download the model.') + GUI_Queue['-Main_log-'].put('Model downloaded.') + +# CI_umij +def CI_umij() -> None: + """Downloads the model info JSON file from GitHub releases. + + Handles logging status messages to the GUI queue and any errors. + The model info file contains metadata about the model version. + """ + try: + download_file_from_github(Github_repo_Releases_URL, + Github_repo_Releases_Model_info_name, + 'Data\\model_info.json', + 256) + except Exception: + GUI_Queue['-Main_log-'].put('ERROR: Failed to download the model info.') + GUI_Queue['-Main_log-'].put('Model info downloaded.') + +# CI_gmi +def CI_gmi() -> str: + if not os.path.isfile('Data\\model_info.json') or time.time() - os.path.getmtime( + 'Data/model_info.json') > 4 * 60 * 60: + CI_umij() + model_info_dict = get_model_info(Model_dir) + if model_info_dict['Ver'] != 'Unknown': + Model_State = 'OK' + elif model_info_dict['Ver'] == 'Unknown' and model_info_dict['file_exists']: + Model_State = 'Model is not a valid model. (hash not found!)' + else: + Model_State = 'Model file is missing.' + model_info_str = f'File_exists: {str(model_info_dict["file_exists"])}\n' + model_info_str += f'Model_hash (SHA256): {model_info_dict["file_hash"].strip()}\n' + model_info_str += f'stored_type: {model_info_dict["stored_type"]}\n' + model_info_str += f'State: {Model_State}\n' + model_info_str += f'Ver: {model_info_dict["Ver"]}' + return model_info_str + +# funcs(INTERNAL)>>> +# IEH +def IEH(id: str = 'Unknown', stop: bool = True, DEV: bool = True) -> None: + """Prints an error message, logs the exception, optionally shows the traceback, and optionally exits. + + This is an internal error handler to nicely handle unexpected errors and optionally exit gracefully. + """ + print_Color(f'~*ERROR: ~*Internal error info/id:\n~*{id}~*.', ['red', 'yellow', 'bg_red', 'yellow'], + advanced_mode=True) + logger.exception(f'Internal Error Handler [stop:{stop}|DEV:{DEV}|id:{id}]') + if DEV: + sg.popup( + f'An internal error occurred.\nERROR-INFO:\n\nErr-ID:\n{id}\n\nErr-Traceback:\n{traceback.format_exc()}', + title=f'Internal Error Exit[{stop}]', + custom_text=('Exit')) + print_Color('detailed error message:', ['yellow']) + traceback.print_exc() + if stop: + logger.warning('SYS EXIT|ERROR: Internal|by Internal Error Handler') + sys.exit('SYS EXIT|ERROR: Internal|by Internal Error Handler') + +# UWL +def UWL(Only_finalize: bool = False) -> None: + """Updates the GUI window. + + This is an internal function to update the GUI window. + """ + # Update the GUI window + GUI_window.read(timeout=0) + if GUI_Queue['-Main_log-'].is_updated and not Only_finalize: + # Retrieve the result from the queue + result_expanded = '' + result = GUI_Queue['-Main_log-'].get() + print(f'Queue Data: {result}') + logger.debug(f'Queue:get: {result}') + # Update the GUI with the result message + for block in result: + result_expanded += f'> {block}\n' + GUI_window['-OUTPUT_ST-'].update(result_expanded, text_color='black') + GUI_window.finalize() + +# main +def main() -> None: + """Main function for the GUI. + """ + # start + sg.SystemTray.notify(f'Pneumonia-Detection-Ai-GUI', f'Gui started.\nV{GUI_Ver}') + if Show_GUI_debug: + sg.SystemTray.notify(f'Pneumonia-Detection-Ai-GUI', f'Looks like you are a programmer\nWow.\nV{GUI_Ver}') + sg.show_debugger_window() + # global + global GUI_window + # Text print + print_Color( + GUI_text_logo, + ['yellow', 'green'], + advanced_mode=True + ) + # prep var + IMG_dir = None + Update_model_info_LXT = None + # Create the tabs + GUI_tab_main = sg.Tab('Main', GUI_layout_Tab_main) + GUI_tab_other = sg.Tab('Ai Model', GUI_layout_Tab_Ai_Model) + GUI_layout_group = [[sg.TabGroup([[GUI_tab_main, GUI_tab_other]])]] + # Create the window + GUI_window = sg.Window(f'Pneumonia-Detection-Ai-GUI V{GUI_Ver}', GUI_layout_group) + # Pre up + CI_umij() + # Main loop for the Graphical User Interface (GUI) + while True: + # Read events and values from the GUI window + event, values = GUI_window.read(timeout=100, timeout_key='-TIMEOUT-') + if not event == '-TIMEOUT-': + logger.debug(f'GUI_window:event: {event}') + logger.debug(f'GUI_window:values: {values}') + + # Check if the window has been closed or the 'Close' button has been clicked + if event == sg.WINDOW_CLOSED or event == 'Close': + # close GUI_window + GUI_window.close() + # try to stop the CI_uaim_Thread + # try: + # CI_uaim_Thread.() + # except Exception: + # pass + break # Exit the loop and close the window + + # Handle event for updating the model + if event == '-BUTTON_RELOAD_MODEL-': + # Call the function to reload the model + CI_rlmw() + + # Handle event for browsing and selecting an image directory + if event == '-BUTTON_BROWSE_IMG_dir-': + # Open file dialog to select an image, and update the input field with the selected directory + IMG_dir = open_file_GUI() + GUI_window['-INPUT_IMG_dir-'].update(IMG_dir) + + # Handle event for confirming the selected image directory + if event == '-BUTTON_OK_IMG_dir-': + # Retrieve the image directory from the input field and update the display + IMG_dir = GUI_window['-INPUT_IMG_dir-'].get() + GUI_window['-INPUT_IMG_dir-'].update(IMG_dir) + + # Handle event for analyzing the selected image + if event == 'Analyse': + # Call the function to load the image and update the output status + Log_temp_txt = CI_liid(IMG_dir, Show_DICOM_INFO=values['-CHECKBOX_SHOW_DICOM_INFO-']) + GUI_Queue['-Main_log-'].put(Log_temp_txt) + UWL() + + # If the image is successfully loaded, proceed with analysis + if Log_temp_txt == 'Image loaded.': + GUI_Queue['-Main_log-'].put('Analyzing...') + UWL() + # Call the function to perform pneumonia analysis and display the results + Log_temp_txt2 = CI_pwai(show_gradcam=values['-CHECKBOX_SHOW_Grad-CAM-']) + logger.info(f'CI_pwai: {Log_temp_txt2}') + GUI_Queue['-Main_log-'].put('Done Analyzing.') + UWL() + GUI_window['-OUTPUT_ST_R-'].update( + Log_temp_txt2, + text_color='green' if 'NORMAL' in Log_temp_txt2 else 'red', + background_color='white' + ) + UWL() + + # Handle event for updating the AI model + if event == '-BUTTON_UPDATE_MODEL-': + # Start a new thread to download the model without freezing the GUI + CI_uaim_Thread = threading.Thread( + target=CI_uaim, + args=(values['-CHECKBOX_DOWNLOAD_LIGHT_MODEL-'],), + daemon=True + ) + CI_uaim_Thread.start() + # Updating the model info + if Update_model_info_LXT is None or time.time() - Update_model_info_LXT > 6: + Update_model_info_LXT = time.time() + GUI_window['-OUTPUT_Model_info-'].update(CI_gmi(), text_color='black') + UWL(Only_finalize=True) + # Continuously check if there are results in the queue to be processed '-Main_log-' + if GUI_Queue['-Main_log-'].is_updated: + # Retrieve the result from the queue + result_expanded = '' + result = GUI_Queue['-Main_log-'].get() + print(f'Queue Data: {result}') + logger.debug(f'Queue[-Main_log-]:get: {result}') + # Update the GUI with the result message + for block in result: + result_expanded += f'> {block}\n' + GUI_window['-OUTPUT_ST-'].update(result_expanded, text_color='black') + UWL() + + +# start>>> +# clear the 'start L1' prompt +print(' ', end='\r') +# Start INFO +VER = f'V{GUI_Ver}' + datetime.now().strftime(' | CDT(%Y/%m/%d | %H:%M:%S)') +gpus = tf.config.list_physical_devices('GPU') +if gpus: + TF_MODE = 'GPU' + TF_sys_details = tf.sysconfig.get_build_info() + TF_CUDA_VER = TF_sys_details['cuda_version'] + TF_CUDNN_VER = TF_sys_details['cudnn_version'] # NOT USED + try: + gpu_name = subprocess.check_output( + ['nvidia-smi', '-L']).decode('utf-8').split(':')[1].split('(')[0].strip() + # GPU 0: NVIDIA `THE GPU NAME` (UUID: GPU-'xxxxxxxxxxxxxxxxxxxx') + # β”‚ β”‚ + # β”Œ---β”΄----------┐ β”Œ---β”΄----------┐ + # β”‚.split(':')[1]β”‚ β”‚.split('(')[0]β”‚ + # β””--------------β”˜ β””--------------β”˜ + except Exception: + gpu_name = '\x1b[0;31mNVIDIA-SMI-ERROR\x1b[0m' + TF_INFO = f'GPU NAME: {gpus[0].name}>>{gpu_name}, CUDA Version: {TF_CUDA_VER}' +else: + TF_MODE = 'CPU' + info = cpuinfo.get_cpu_info()['brand_raw'] + TF_INFO = f'{info}' +# GUI_Info +GUI_Info = f'PDAI Ver: {VER} \nPython Ver: {sys.version} \nTensorflow Ver: {tf.version.VERSION}, Mode: {TF_MODE}, {TF_INFO}' +logger.info(f'PDAI Ver: {VER}') +logger.info(f'Python Ver: {sys.version}') +logger.info(f'Tensorflow Ver: {tf.version.VERSION}') +logger.info(f'Mode: {TF_MODE}, {TF_INFO}') +print(GUI_Info) +# FP +if Model_FORMAT not in ['TF_dir', 'H5_SF']: + logger.info(f'Model file format [{Model_FORMAT}]') + IEH(id=f'F[SYS],P[FP],Error[Invalid Model_FORMAT]', DEV=False) +elif Model_FORMAT == 'H5_SF': + Model_dir += '.h5' +# start main +if __name__ == '__main__': + try: + try: + main() + except (EOFError, KeyboardInterrupt): + logger.info('KeyboardInterrupt.') + pass + except Exception as e: + IEH(id=f'F[SYS],RFunc[main],Error[{e}]', DEV=True) + else: + logger.info('GUI Exit.') + print_Color('\n~*[PDAI GUI] ~*closed.', + ['yellow', 'red'], advanced_mode=True) +else: + logger.info('GUI Imported.') +# end(EOF) diff --git a/Interface/GUI/Data/Utils/Grad_cam.py b/Interface/GUI/Data/Utils/Grad_cam.py index fc2a71f..c63729a 100644 --- a/Interface/GUI/Data/Utils/Grad_cam.py +++ b/Interface/GUI/Data/Utils/Grad_cam.py @@ -1,63 +1,63 @@ -import os -import glob -import numpy as np -import tensorflow as tf -# Other -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - -# Main -def _compute_heatmap(model, - img_array, - conv_layer_name, - pred_index): - """ - Helper function to compute the heatmap for a given convolutional layer. - """ - grad_model = tf.keras.models.Model( - [model.inputs], - [model.get_layer(conv_layer_name).output, model.output] - ) - - with tf.GradientTape() as tape: - conv_layer_output, preds = grad_model(img_array) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, conv_layer_output) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - - conv_layer_output = conv_layer_output[0] - heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] - heatmap = tf.squeeze(heatmap) - heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) - return heatmap - -def make_gradcam_heatmap(img_array, - model, - last_conv_layer_name, - second_last_conv_layer_name=None, - pred_index=None, - sensitivity_map=1.0): - """ - Function to compute the Grad-CAM heatmap for a specific class, given an input image. - """ - if pred_index is None: - preds = model.predict(img_array) - pred_index = tf.argmax(preds[0]) - - # Compute heatmap for the last convolutional layer - heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) - heatmap = heatmap ** sensitivity_map - - if second_last_conv_layer_name is not None: - # Compute heatmap for the second last convolutional layer - heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) - heatmap_second = heatmap_second ** sensitivity_map - - # Average the two heatmaps - heatmap = (heatmap + heatmap_second) / 2.0 - +import os +import glob +import numpy as np +import tensorflow as tf +# Other +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + +# Main +def _compute_heatmap(model, + img_array, + conv_layer_name, + pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, + model, + last_conv_layer_name, + second_last_conv_layer_name=None, + pred_index=None, + sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + return heatmap \ No newline at end of file diff --git a/Interface/GUI/Data/Utils/Other.py b/Interface/GUI/Data/Utils/Other.py index e9dc555..fa8aed8 100644 --- a/Interface/GUI/Data/Utils/Other.py +++ b/Interface/GUI/Data/Utils/Other.py @@ -1,32 +1,32 @@ -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -import pickle -import gzip - -def save_list(history, filename, compress=True): - # Saves the given history list to the specified filename. - # If compress is True, the file will be gzip compressed. - # Otherwise it will be saved as a normal pickle file. - if compress: - with gzip.open(filename, 'wb') as f: - pickle.dump(history, f) - else: - with open(filename, 'wb') as f: - pickle.dump(history, f) - - -def load_list(filename, compressed=True): - # Loads a pickled object from a file. - # If compressed=True, it will load from a gzip compressed file. - # Otherwise loads from a regular file. - if compressed: - with gzip.open(filename, 'rb') as f: - return pickle.load(f) - else: - with open(filename, 'rb') as f: - return pickle.load(f) -def P_warning(msg): - # Prints a warning message with color formatting. - # msg: The message to print as a warning. - print_Color_V2(f'Warning: {msg}') - +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +import pickle +import gzip + +def save_list(history, filename, compress=True): + # Saves the given history list to the specified filename. + # If compress is True, the file will be gzip compressed. + # Otherwise it will be saved as a normal pickle file. + if compress: + with gzip.open(filename, 'wb') as f: + pickle.dump(history, f) + else: + with open(filename, 'wb') as f: + pickle.dump(history, f) + + +def load_list(filename, compressed=True): + # Loads a pickled object from a file. + # If compressed=True, it will load from a gzip compressed file. + # Otherwise loads from a regular file. + if compressed: + with gzip.open(filename, 'rb') as f: + return pickle.load(f) + else: + with open(filename, 'rb') as f: + return pickle.load(f) +def P_warning(msg): + # Prints a warning message with color formatting. + # msg: The message to print as a warning. + print_Color_V2(f'Warning: {msg}') + diff --git a/Interface/GUI/Data/Utils/README.md b/Interface/GUI/Data/Utils/README.md index 2d85e26..16ffebb 100644 --- a/Interface/GUI/Data/Utils/README.md +++ b/Interface/GUI/Data/Utils/README.md @@ -1,15 +1,15 @@ -# Utils: - -## one_cycle_lr and lr_find (by 'benihime91') -- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) - - ### doc link: [1_README.md](docs\1_README.md) - -## Python-color-print-V2 and Python-color-print (by Me) -- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) - - ### doc link: [2_README.md](docs\2_README.md) -- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) - - ### doc link: [3_README.md](docs\3_README.md) - -## Grad_cam (by GPT-4 😁) - -## Other.py (by Me) +# Utils: + +## one_cycle_lr and lr_find (by 'benihime91') +- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) + - ### doc link: [1_README.md](docs\1_README.md) + +## Python-color-print-V2 and Python-color-print (by Me) +- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) + - ### doc link: [2_README.md](docs\2_README.md) +- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) + - ### doc link: [3_README.md](docs\3_README.md) + +## Grad_cam (by GPT-4 😁) + +## Other.py (by Me) diff --git a/Interface/GUI/Data/Utils/docs/1_README.md b/Interface/GUI/Data/Utils/docs/1_README.md index 461a2de..ada2767 100644 --- a/Interface/GUI/Data/Utils/docs/1_README.md +++ b/Interface/GUI/Data/Utils/docs/1_README.md @@ -1,208 +1,208 @@ -# one_cycle_lr-tensorflow: - -## Installation: - - Ensure that `python >= 3.6` is installed. - ```bash - $ git clone https://github.com/benihime91/one_cycle_lr-tensorflow.git - $ cd one_cycle_lr-tensorflow - $ pip install -r requirements.txt - ``` -## Demo: -[JupyterNotebook](https://github.com/benihime91/tensorflow-on-steroids/blob/master/nbs/one_cycle_%26_lr_finder_tf.ipynb). - -## Important : -LrFinder does not support TPU training . - -## Contents: - -1. **OneCycleLR learning rate scheduler** - - [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/one_cycle.py) - - **Example :** - ```python - - # Import `OneCycleLr` - from one_cycle import OneCycleLr - - # Configs - max_lr = 5e-02 - epochs = 5 - - # Istantiate `OneCycleLr` - one_c = OneCycleLr(max_lr=max_lr, steps_per_epoch=len(trn_ds), epochs=epochs) - - # Instantiate CallbackList - cbs = [one_c, ...] - - # Instantiate Optimizer & loss_fn - optim = keras.optimizers.SGD(momentum=0.9, clipvalue=0.1) - loss_fn = ... - - # Compile Model - model.compile(optimizer=optim, loss=loss_fn, metrics=["acc"]) - - # Fit Model - h = model.fit(trn_ds, validation_data=val_ds, epochs=epochs, callbacks=cbs) - ``` - - **To view the learning_rate and momentum plots:** - - ```python - # to plot the learning_rate & momentum(or beta_1) graphs - one_c.plot_lrs_moms() - ``` - - ![one_cycle_lr_plot](vis/one_cycle_plots.png) - - -2. **Learning Rate Finder** - - [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/lr_find.py) - - **Example:** - ```python - # Import LrFinder - from lr_find import LrFinder - - # Instantiate Optimizer & loss_fn - # [must be instance of tf.keras.Optimizers & tf.keras.Losses] - optimizer = ... - loss_fn = ... - - # Instantiate LrFinder - lr_find = LrFinder(model, optimizer, loss_fn) - - # Start range_test - lr_find.range_test(trn_ds) - ``` - **To view `lr_finder` plots:** - ```python - # Plot LrFinder graphs - lr_find.plot_lrs() - ``` - ![Lr_finder Plot](vis/lr_finder_plot_1.png) - - **To view `lr_finder` plots with suggestion:** - ```python - # Plot LrFinder graphs - lr_find.plot_lrs(skip_end=0, suggestion=True) - ``` - ![Lr_finder Plot](vis/lr_finder_plot_2.png) - - -## Information: - -1. **OneCycleLR learning rate scheduler:** - - Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://arxiv.org/abs/1708.07120) and popularized by [fast.ai](https://www.fast.ai/). - - - The 1cycle learning rate policy changes the learning rate after every batch. - - - Note also that the `total number of steps` in the cycle can be determined in one of two ways (listed in order of precedence): - - - A value for `total_steps` is explicitly provided. - - - A number of `epochs (epochs)` and a number of `steps per epoch (steps_per_epoch)` are provided. In this case, the number of `total steps` is inferred by `total_steps = epochs * steps_per_epoch`. - - You must either provide a value for total_steps or provide a value for both epochs and steps_per_epoch. - - - **OneCycleLR callback arguments:** - - - **max_lr** (`float`): Upper learning rate boundaries in the cycle. - - **total_steps** (`int`): The total number of steps in the cycle. Note that - if a value is not provided here, then it must be inferred by providing - a value for epochs and steps_per_epoch. - Default: None - - **epochs** (`int`): The number of epochs to train for. This is used along - with steps_per_epoch in order to infer the total number of steps in the cycle - if a value for total_steps is not provided. - Default: None - - **steps_per_epoch** (`int`): The number of steps per epoch to train for. This is - used along with epochs in order to infer the total number of steps in the - cycle if a value for total_steps is not provided. - Default: None - - **pct_start** (`float`): The percentage of the cycle (in number of steps) spent - increasing the learning rate. - Default: 0.3 - - **anneal_strategy** (`str`): {'cos', 'linear'} - Specifies the annealing strategy: "cos" for cosine annealing, "linear" for - linear annealing. - Default: 'cos' - - **cycle_momentum** (`bool`): If ``True``, momentum is cycled inversely - to learning rate between 'base_momentum' and 'max_momentum'. - Default: True - - **base_momentum** (`float`): Lower momentum boundaries in the cycle - for each parameter group. Note that momentum is cycled inversely - to learning rate; at the peak of a cycle, momentum is - 'base_momentum' and learning rate is 'max_lr'. - Default: 0.85 - - **max_momentum** (`float or list`): Upper momentum boundaries in the cycle - for each parameter group. Functionally, - it defines the cycle amplitude (max_momentum - base_momentum). - Note that momentum is cycled inversely - to learning rate; at the start of a cycle, momentum is 'max_momentum' - and learning rate is 'base_lr' - Default: 0.95 - - **div_factor** (`float`): Determines the initial learning rate via - initial_lr = max_lr/div_factor - Default: 25 - - **final_div_factor** (`float`): Determines the minimum learning rate via - min_lr = initial_lr/final_div_factor - Default: 1e4 - -2. **Learning_rate Finder:** - - - For training deep neural networks, selecting a good learning rate is essential for both better performance and faster convergence. Even optimizers such as Adam that are self-adjusting the learning rate can benefit from more optimal choices. - - - To reduce the amount of guesswork concerning choosing a good initial learning rate, a learning rate finder can be used. As described in this [paper](https://arxiv.org/abs/1506.01186) a learning rate finder does a small run where the learning rate is increased after each processed batch and the corresponding loss is logged. The result of this is a lr vs. loss plot that can be used as guidance for choosing a optimal initial lr. - - - **Arguments to Initialize LrFinder class:** - - **model** (`tf.keras.Model`): wrapped model - - **optimizer** (`tf.keras.optimizers`): wrapped optimizer - - **loss_fn** (t`f.keras.losses`): loss function - - - **Arguments to start range test:** - - **trn_ds** (`tf.data.Dataset`): the train dataset. - - **start_lr** (`float, optional`): the starting learning rate for the range test. - Default:1e-07. - - **end_lr** (`float, optional`): the maximum learning rate to test. Default: 10. - - **num_iter** (`int, optional`): the number of steps over which the test - occurs. Default: 100. - - **beta** (`float, optional`): the loss smoothing factor within the [0, 1] - interval. The loss is smoothed using exponential smoothing. - Default: 0.98. - - -## References & Citations: - ``` - @misc{smith2015cyclical, - title={Cyclical Learning Rates for Training Neural Networks}, - author={Leslie N. Smith}, - year={2015}, - eprint={1506.01186}, - archivePrefix={arXiv}, - primaryClass={cs.CV} - } - ``` - ``` - @misc{howard2018fastai, - title={fastai}, - author={Howard, Jeremy and others}, - year={2018}, - publisher={GitHub}, - howpublished={\url{https://github.com/fastai/fastai}}, - } - ``` - ``` - @incollection{NEURIPS2019_9015, - title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, - author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith}, - booktitle = {Advances in Neural Information Processing Systems 32}, - editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, - pages = {8024--8035}, - year = {2019}, - publisher = {Curran Associates, Inc.}, - url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf} - } - ``` +# one_cycle_lr-tensorflow: + +## Installation: + + Ensure that `python >= 3.6` is installed. + ```bash + $ git clone https://github.com/benihime91/one_cycle_lr-tensorflow.git + $ cd one_cycle_lr-tensorflow + $ pip install -r requirements.txt + ``` +## Demo: +[JupyterNotebook](https://github.com/benihime91/tensorflow-on-steroids/blob/master/nbs/one_cycle_%26_lr_finder_tf.ipynb). + +## Important : +LrFinder does not support TPU training . + +## Contents: + +1. **OneCycleLR learning rate scheduler** + + [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/one_cycle.py) + + **Example :** + ```python + + # Import `OneCycleLr` + from one_cycle import OneCycleLr + + # Configs + max_lr = 5e-02 + epochs = 5 + + # Istantiate `OneCycleLr` + one_c = OneCycleLr(max_lr=max_lr, steps_per_epoch=len(trn_ds), epochs=epochs) + + # Instantiate CallbackList + cbs = [one_c, ...] + + # Instantiate Optimizer & loss_fn + optim = keras.optimizers.SGD(momentum=0.9, clipvalue=0.1) + loss_fn = ... + + # Compile Model + model.compile(optimizer=optim, loss=loss_fn, metrics=["acc"]) + + # Fit Model + h = model.fit(trn_ds, validation_data=val_ds, epochs=epochs, callbacks=cbs) + ``` + + **To view the learning_rate and momentum plots:** + + ```python + # to plot the learning_rate & momentum(or beta_1) graphs + one_c.plot_lrs_moms() + ``` + + ![one_cycle_lr_plot](vis/one_cycle_plots.png) + + +2. **Learning Rate Finder** + + [Source](https://github.com/benihime91/tensorflow-on-steroids/blob/master/lr_find.py) + + **Example:** + ```python + # Import LrFinder + from lr_find import LrFinder + + # Instantiate Optimizer & loss_fn + # [must be instance of tf.keras.Optimizers & tf.keras.Losses] + optimizer = ... + loss_fn = ... + + # Instantiate LrFinder + lr_find = LrFinder(model, optimizer, loss_fn) + + # Start range_test + lr_find.range_test(trn_ds) + ``` + **To view `lr_finder` plots:** + ```python + # Plot LrFinder graphs + lr_find.plot_lrs() + ``` + ![Lr_finder Plot](vis/lr_finder_plot_1.png) + + **To view `lr_finder` plots with suggestion:** + ```python + # Plot LrFinder graphs + lr_find.plot_lrs(skip_end=0, suggestion=True) + ``` + ![Lr_finder Plot](vis/lr_finder_plot_2.png) + + +## Information: + +1. **OneCycleLR learning rate scheduler:** + - Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then from that maximum learning rate to some minimum learning rate much lower than the initial learning rate. This policy was initially described in the paper [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://arxiv.org/abs/1708.07120) and popularized by [fast.ai](https://www.fast.ai/). + + - The 1cycle learning rate policy changes the learning rate after every batch. + + - Note also that the `total number of steps` in the cycle can be determined in one of two ways (listed in order of precedence): + + - A value for `total_steps` is explicitly provided. + + - A number of `epochs (epochs)` and a number of `steps per epoch (steps_per_epoch)` are provided. In this case, the number of `total steps` is inferred by `total_steps = epochs * steps_per_epoch`. + + You must either provide a value for total_steps or provide a value for both epochs and steps_per_epoch. + + - **OneCycleLR callback arguments:** + + - **max_lr** (`float`): Upper learning rate boundaries in the cycle. + - **total_steps** (`int`): The total number of steps in the cycle. Note that + if a value is not provided here, then it must be inferred by providing + a value for epochs and steps_per_epoch. + Default: None + - **epochs** (`int`): The number of epochs to train for. This is used along + with steps_per_epoch in order to infer the total number of steps in the cycle + if a value for total_steps is not provided. + Default: None + - **steps_per_epoch** (`int`): The number of steps per epoch to train for. This is + used along with epochs in order to infer the total number of steps in the + cycle if a value for total_steps is not provided. + Default: None + - **pct_start** (`float`): The percentage of the cycle (in number of steps) spent + increasing the learning rate. + Default: 0.3 + - **anneal_strategy** (`str`): {'cos', 'linear'} + Specifies the annealing strategy: "cos" for cosine annealing, "linear" for + linear annealing. + Default: 'cos' + - **cycle_momentum** (`bool`): If ``True``, momentum is cycled inversely + to learning rate between 'base_momentum' and 'max_momentum'. + Default: True + - **base_momentum** (`float`): Lower momentum boundaries in the cycle + for each parameter group. Note that momentum is cycled inversely + to learning rate; at the peak of a cycle, momentum is + 'base_momentum' and learning rate is 'max_lr'. + Default: 0.85 + - **max_momentum** (`float or list`): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (max_momentum - base_momentum). + Note that momentum is cycled inversely + to learning rate; at the start of a cycle, momentum is 'max_momentum' + and learning rate is 'base_lr' + Default: 0.95 + - **div_factor** (`float`): Determines the initial learning rate via + initial_lr = max_lr/div_factor + Default: 25 + - **final_div_factor** (`float`): Determines the minimum learning rate via + min_lr = initial_lr/final_div_factor + Default: 1e4 + +2. **Learning_rate Finder:** + + - For training deep neural networks, selecting a good learning rate is essential for both better performance and faster convergence. Even optimizers such as Adam that are self-adjusting the learning rate can benefit from more optimal choices. + + - To reduce the amount of guesswork concerning choosing a good initial learning rate, a learning rate finder can be used. As described in this [paper](https://arxiv.org/abs/1506.01186) a learning rate finder does a small run where the learning rate is increased after each processed batch and the corresponding loss is logged. The result of this is a lr vs. loss plot that can be used as guidance for choosing a optimal initial lr. + + - **Arguments to Initialize LrFinder class:** + - **model** (`tf.keras.Model`): wrapped model + - **optimizer** (`tf.keras.optimizers`): wrapped optimizer + - **loss_fn** (t`f.keras.losses`): loss function + + - **Arguments to start range test:** + - **trn_ds** (`tf.data.Dataset`): the train dataset. + - **start_lr** (`float, optional`): the starting learning rate for the range test. + Default:1e-07. + - **end_lr** (`float, optional`): the maximum learning rate to test. Default: 10. + - **num_iter** (`int, optional`): the number of steps over which the test + occurs. Default: 100. + - **beta** (`float, optional`): the loss smoothing factor within the [0, 1] + interval. The loss is smoothed using exponential smoothing. + Default: 0.98. + + +## References & Citations: + ``` + @misc{smith2015cyclical, + title={Cyclical Learning Rates for Training Neural Networks}, + author={Leslie N. Smith}, + year={2015}, + eprint={1506.01186}, + archivePrefix={arXiv}, + primaryClass={cs.CV} + } + ``` + ``` + @misc{howard2018fastai, + title={fastai}, + author={Howard, Jeremy and others}, + year={2018}, + publisher={GitHub}, + howpublished={\url{https://github.com/fastai/fastai}}, + } + ``` + ``` + @incollection{NEURIPS2019_9015, + title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, + author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith}, + booktitle = {Advances in Neural Information Processing Systems 32}, + editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, + pages = {8024--8035}, + year = {2019}, + publisher = {Curran Associates, Inc.}, + url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf} + } + ``` diff --git a/Interface/GUI/Data/Utils/docs/2_README.md b/Interface/GUI/Data/Utils/docs/2_README.md index c610a15..5c42240 100644 --- a/Interface/GUI/Data/Utils/docs/2_README.md +++ b/Interface/GUI/Data/Utils/docs/2_README.md @@ -1,152 +1,152 @@ -# Python-color-print-V2 -![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue) - -A Python function to print colored text to the console using advanced terminal colors. - -## Function Signature -```python -def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): -``` - -## Parameters -- `Input` (str): The input string to be printed. '' is used to specify the color of the following text. -- `print_END` (str): The string appended after the final output. Default is '\\n'. -- `start_char` (str): The character used as the start of the color specifier. Default is '<'. -- `end_char` (str): The character used as the end of the color specifier. Default is '>'. - -## Usage -you can print a string in color. For example: -```python -print_Color('Hello, World!') -``` -This will print 'Hello, World!' in green. - -Or like: -```python -print_Color('hello hello in red hello in green') -``` - -## Special Characters -The '<>' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. - -## Code snippet -```python -import re - -def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. '' is used to specify the color of the following text. - print_END (str): The string appended after the final output. Default is '\\n'. - start_char (str): The character used as the start of the color specifier. Default is '<'. - end_char (str): The character used as the end of the color specifier. Default is '>'. - - Examples: - ~~~python - print_Color('Hello, World!') - # Prints 'Hello, World!' in normal color. - - print_Color('Hello in red Hello in green') - # Prints 'Hello in red' in red and 'Hello in green' in green. - - print_Color('~red!Hello in red', start_char='~', end_char='!') - # Prints 'Hello, World!' in normal color. - - Note: - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m' - } - pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) - substrings = re.split(pattern, Input) - current_color = 'normal' - for i, sub_str in enumerate(substrings): - if i % 2 == 0: - print(color_code[current_color] + sub_str + color_code['normal'], end='') - current_color = 'normal' - else: - color = sub_str.strip() - if color in color_code: - current_color = color - else: - print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") - print('', end=print_END) -``` - -## Supported Colors -#### you can use the key word like 'black' and... to set the text color. -~~~ -'black': '\x1b[0;30m', -'red': '\x1b[0;31m', -'green': '\x1b[0;32m', -'yellow': '\x1b[0;33m', -'blue': '\x1b[0;34m', -'magenta': '\x1b[0;35m', -'cyan': '\x1b[0;36m', -'white': '\x1b[0;37m', -'normal': '\x1b[0m', -'bg_black': '\x1b[40m', -'bg_red': '\x1b[41m', -'bg_green': '\x1b[42m', -'bg_yellow': '\x1b[43m', -'bg_blue': '\x1b[44m', -'bg_magenta': '\x1b[45m', -'bg_cyan': '\x1b[46m', -'bg_white': '\x1b[47m', -'bg_normal': '\x1b[49m', -'light_gray': '\x1b[0;90m', -'light_red': '\x1b[0;91m', -'light_green': '\x1b[0;92m', -'light_yellow': '\x1b[0;93m', -'light_blue': '\x1b[0;94m', -'light_magenta': '\x1b[0;95m', -'light_cyan': '\x1b[0;96m', -'light_white': '\x1b[0;97m', -'bg_light_gray': '\x1b[0;100m', -'bg_light_red': '\x1b[0;101m', -'bg_light_green': '\x1b[0;102m', -'bg_light_yellow': '\x1b[0;103m', -'bg_light_blue': '\x1b[0;104m', -'bg_light_magenta': '\x1b[0;105m', -'bg_light_cyan': '\x1b[0;106m', -'bg_light_white': '\x1b[0;107m', -'underline': '\x1b[4m', -'bold': '\x1b[1m', -'blink': '\x1b[5m' -~~~ +# Python-color-print-V2 +![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge&logo=python&logoColor=blue) + +A Python function to print colored text to the console using advanced terminal colors. + +## Function Signature +```python +def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): +``` + +## Parameters +- `Input` (str): The input string to be printed. '' is used to specify the color of the following text. +- `print_END` (str): The string appended after the final output. Default is '\\n'. +- `start_char` (str): The character used as the start of the color specifier. Default is '<'. +- `end_char` (str): The character used as the end of the color specifier. Default is '>'. + +## Usage +you can print a string in color. For example: +```python +print_Color('Hello, World!') +``` +This will print 'Hello, World!' in green. + +Or like: +```python +print_Color('hello hello in red hello in green') +``` + +## Special Characters +The '<>' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. + +## Code snippet +```python +import re + +def print_Color(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. '' is used to specify the color of the following text. + print_END (str): The string appended after the final output. Default is '\\n'. + start_char (str): The character used as the start of the color specifier. Default is '<'. + end_char (str): The character used as the end of the color specifier. Default is '>'. + + Examples: + ~~~python + print_Color('Hello, World!') + # Prints 'Hello, World!' in normal color. + + print_Color('Hello in red Hello in green') + # Prints 'Hello in red' in red and 'Hello in green' in green. + + print_Color('~red!Hello in red', start_char='~', end_char='!') + # Prints 'Hello, World!' in normal color. + + Note: + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m' + } + pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) + substrings = re.split(pattern, Input) + current_color = 'normal' + for i, sub_str in enumerate(substrings): + if i % 2 == 0: + print(color_code[current_color] + sub_str + color_code['normal'], end='') + current_color = 'normal' + else: + color = sub_str.strip() + if color in color_code: + current_color = color + else: + print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") + print('', end=print_END) +``` + +## Supported Colors +#### you can use the key word like 'black' and... to set the text color. +~~~ +'black': '\x1b[0;30m', +'red': '\x1b[0;31m', +'green': '\x1b[0;32m', +'yellow': '\x1b[0;33m', +'blue': '\x1b[0;34m', +'magenta': '\x1b[0;35m', +'cyan': '\x1b[0;36m', +'white': '\x1b[0;37m', +'normal': '\x1b[0m', +'bg_black': '\x1b[40m', +'bg_red': '\x1b[41m', +'bg_green': '\x1b[42m', +'bg_yellow': '\x1b[43m', +'bg_blue': '\x1b[44m', +'bg_magenta': '\x1b[45m', +'bg_cyan': '\x1b[46m', +'bg_white': '\x1b[47m', +'bg_normal': '\x1b[49m', +'light_gray': '\x1b[0;90m', +'light_red': '\x1b[0;91m', +'light_green': '\x1b[0;92m', +'light_yellow': '\x1b[0;93m', +'light_blue': '\x1b[0;94m', +'light_magenta': '\x1b[0;95m', +'light_cyan': '\x1b[0;96m', +'light_white': '\x1b[0;97m', +'bg_light_gray': '\x1b[0;100m', +'bg_light_red': '\x1b[0;101m', +'bg_light_green': '\x1b[0;102m', +'bg_light_yellow': '\x1b[0;103m', +'bg_light_blue': '\x1b[0;104m', +'bg_light_magenta': '\x1b[0;105m', +'bg_light_cyan': '\x1b[0;106m', +'bg_light_white': '\x1b[0;107m', +'underline': '\x1b[4m', +'bold': '\x1b[1m', +'blink': '\x1b[5m' +~~~ diff --git a/Interface/GUI/Data/Utils/docs/3_README.md b/Interface/GUI/Data/Utils/docs/3_README.md index 59ca165..d27dae1 100644 --- a/Interface/GUI/Data/Utils/docs/3_README.md +++ b/Interface/GUI/Data/Utils/docs/3_README.md @@ -1,71 +1,71 @@ -# Python-color-print - - -## Function Signature -```python -def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False): -``` - -## Parameters -- `Input` (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. -- `colors` (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. -- `print_END` (str): The string appended after the final output, default is '\\n'. -- `advanced_mode` (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. - -## Usage -In **normal mode**, you can print a string in a single color. For example: -```python -print_Color('Hello, World!', ['green']) -``` -This will print 'Hello, World!' in green. - -In **advanced mode**, you can print different parts of a string in different colors. For example: -```python -print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) -``` -This will print 'Hello in green' in green and 'Hello in red' in red. - -## Special Characters -The '~*' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. - -## Supported Colors -#### you can use the key word like 'black' and... to set the text color. -~~~ -'black': '\x1b[0;30m', -'red': '\x1b[0;31m', -'green': '\x1b[0;32m', -'yellow': '\x1b[0;33m', -'blue': '\x1b[0;34m', -'magenta': '\x1b[0;35m', -'cyan': '\x1b[0;36m', -'white': '\x1b[0;37m', -'normal': '\x1b[0m', -'bg_black': '\x1b[40m', -'bg_red': '\x1b[41m', -'bg_green': '\x1b[42m', -'bg_yellow': '\x1b[43m', -'bg_blue': '\x1b[44m', -'bg_magenta': '\x1b[45m', -'bg_cyan': '\x1b[46m', -'bg_white': '\x1b[47m', -'bg_normal': '\x1b[49m', -'light_gray': '\x1b[0;90m', -'light_red': '\x1b[0;91m', -'light_green': '\x1b[0;92m', -'light_yellow': '\x1b[0;93m', -'light_blue': '\x1b[0;94m', -'light_magenta': '\x1b[0;95m', -'light_cyan': '\x1b[0;96m', -'light_white': '\x1b[0;97m', -'bg_light_gray': '\x1b[0;100m', -'bg_light_red': '\x1b[0;101m', -'bg_light_green': '\x1b[0;102m', -'bg_light_yellow': '\x1b[0;103m', -'bg_light_blue': '\x1b[0;104m', -'bg_light_magenta': '\x1b[0;105m', -'bg_light_cyan': '\x1b[0;106m', -'bg_light_white': '\x1b[0;107m', -'underline': '\x1b[4m', -'bold': '\x1b[1m', -'blink': '\x1b[5m' -~~~ +# Python-color-print + + +## Function Signature +```python +def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False): +``` + +## Parameters +- `Input` (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. +- `colors` (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. +- `print_END` (str): The string appended after the final output, default is '\\n'. +- `advanced_mode` (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. + +## Usage +In **normal mode**, you can print a string in a single color. For example: +```python +print_Color('Hello, World!', ['green']) +``` +This will print 'Hello, World!' in green. + +In **advanced mode**, you can print different parts of a string in different colors. For example: +```python +print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) +``` +This will print 'Hello in green' in green and 'Hello in red' in red. + +## Special Characters +The '~*' characters are used as separators for different parts of the string that need to be printed in different colors when using advanced mode. + +## Supported Colors +#### you can use the key word like 'black' and... to set the text color. +~~~ +'black': '\x1b[0;30m', +'red': '\x1b[0;31m', +'green': '\x1b[0;32m', +'yellow': '\x1b[0;33m', +'blue': '\x1b[0;34m', +'magenta': '\x1b[0;35m', +'cyan': '\x1b[0;36m', +'white': '\x1b[0;37m', +'normal': '\x1b[0m', +'bg_black': '\x1b[40m', +'bg_red': '\x1b[41m', +'bg_green': '\x1b[42m', +'bg_yellow': '\x1b[43m', +'bg_blue': '\x1b[44m', +'bg_magenta': '\x1b[45m', +'bg_cyan': '\x1b[46m', +'bg_white': '\x1b[47m', +'bg_normal': '\x1b[49m', +'light_gray': '\x1b[0;90m', +'light_red': '\x1b[0;91m', +'light_green': '\x1b[0;92m', +'light_yellow': '\x1b[0;93m', +'light_blue': '\x1b[0;94m', +'light_magenta': '\x1b[0;95m', +'light_cyan': '\x1b[0;96m', +'light_white': '\x1b[0;97m', +'bg_light_gray': '\x1b[0;100m', +'bg_light_red': '\x1b[0;101m', +'bg_light_green': '\x1b[0;102m', +'bg_light_yellow': '\x1b[0;103m', +'bg_light_blue': '\x1b[0;104m', +'bg_light_magenta': '\x1b[0;105m', +'bg_light_cyan': '\x1b[0;106m', +'bg_light_white': '\x1b[0;107m', +'underline': '\x1b[4m', +'bold': '\x1b[1m', +'blink': '\x1b[5m' +~~~ diff --git a/Interface/GUI/Data/Utils/lr_find.py b/Interface/GUI/Data/Utils/lr_find.py index 4356d32..cc136ab 100644 --- a/Interface/GUI/Data/Utils/lr_find.py +++ b/Interface/GUI/Data/Utils/lr_find.py @@ -1,209 +1,209 @@ -import tempfile - -import matplotlib.pyplot as plt -import numpy as np - -import tensorflow as tf -from tensorflow import keras -from tqdm.auto import tqdm - -K = keras.backend - - -class Scheduler: - def __init__(self, vals, n_iter: int) -> None: - 'Used to "step" from start,end (`vals`) over `n_iter` s on a schedule defined by `func`' - self.start, self.end = ( - (vals[0], vals[1]) if isinstance(vals, tuple) else (vals, 0) - ) - self.n_iter = max(1, n_iter) - self.func = self._aannealing_exp - self.n = 0 - - @staticmethod - def _aannealing_exp(start: float, end: float, pct: float) -> float: - "Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return start * (end / start) ** pct - - def restart(self) -> None: - self.n = 0 - - def step(self) -> float: - self.n += 1 - return self.func(self.start, self.end, self.n / self.n_iter) - - @property - def is_done(self) -> bool: - "Return `True` if schedule completed." - return self.n >= self.n_iter - - -class LrFinder: - """ - [LrFinder Implemetation taken from Fast.ai] - (https://github.com/fastai/fastai/tree/master/fastai) - - The learning rate range test increases the learning rate in a pre-training run - between two boundaries in a linear or exponential manner. It provides valuable - information on how well the network can be trained over a range of learning rates - and what is the optimal learning rate. - - Args: - model (tf.keras.Model): wrapped model - optimizer (tf.keras.optimizers): wrapped optimizer - loss_fn (tf.keras.losses): loss function - - Example: - >>> lr_finder = LrFinder(model, optimizer, loss_fn) - >>> lr_finder.range_test(trn_ds, end_lr=100, num_iter=100) - >>> lr_finder.plot_lrs() # to inspect the loss-learning rate graph - """ - - def __init__(self, - model: tf.keras.Model, - optimizer: tf.keras.optimizers.Optimizer, - loss_fn: tf.keras.losses.Loss, - ) -> None: - - self.lrs = [] - self.losses = [] - self.model = model - self.optimizer = optimizer - self.loss_fn = loss_fn - self.mw = self.model.get_weights() - self.init_lr = K.get_value(self.optimizer.lr) - self.iteration = 0 - self.weightsFile = tempfile.mkstemp()[1] - - @tf.function - def trn_step(self, xb, yb): - """performs 1 trainig step""" - with tf.GradientTape() as tape: - logits = self.model(xb, training=True) - main_loss = tf.reduce_mean(self.loss_fn(yb, logits)) - loss = tf.add_n([main_loss] + self.model.losses) - grads = tape.gradient(loss, self.model.trainable_variables) - return loss, grads - - def range_test(self, - trn_ds: tf.data.Dataset, - start_lr: float = 1e-7, - end_lr: float = 10, - num_iter: int = 100, - beta=0.98, - ) -> None: - """ - Explore lr from `start_lr` to `end_lr` over `num_it` s in `model`. - - Args: - trn_ds (tf.data.Dataset) - start_lr (float, optional): the starting learning rate for the range test. - Default:1e-07. - end_lr (float, optional): the maximum learning rate to test. Default: 10. - num_iter (int, optional): the number of s over which the test - occurs. Default: 100. - beta (float, optional): the loss smoothing factor within the [0, 1] - interval. The loss is smoothed using exponential smoothing. - Default: 0.98. - """ - # save original model weights - try: - self.model.save_weights(self.weightsFile) - except: - print("Unable to save initial weights, weights of model will change. Re-instantiate model to load previous weights ...") - # start scheduler - sched = Scheduler((start_lr, end_lr), num_iter) - avg_loss, best_loss, = 0.0, 0.0 - # set the startig lr - K.set_value(self.optimizer.lr, sched.start) - - print(f"Finding best initial lr over {num_iter} steps") - # initialize tqdm bar - bar = tqdm(iterable=range(num_iter)) - - # iterate over the batches - for (xb, yb) in trn_ds: - self.iteration += 1 - loss, grads = self.trn_step(xb, yb) - # compute smoothed loss - avg_loss = beta * avg_loss + (1 - beta) * loss - smoothed_loss = avg_loss / (1 - beta ** self.iteration) - - # record best loss - if self.iteration == 1 or smoothed_loss < best_loss: - best_loss = smoothed_loss - - # stop if loss is exploding - if sched.is_done or ( - smoothed_loss > 4 * best_loss or np.isnan(smoothed_loss) - ): - break - - # append losses and lrs - self.losses.append(smoothed_loss) - self.lrs.append(K.get_value(self.optimizer.lr)) - - # update weights - self.optimizer.apply_gradients( - zip(grads, self.model.trainable_variables)) - - # update lr - K.set_value(self.optimizer.lr, sched.step()) - - # update tqdm - bar.update(1) - - # clean-up - bar.close() - sched.restart() - self._print_prompt() - - def _print_prompt(self) -> None: - "Cleanup model weights disturbed during LRFinder exploration." - try: - self.model.load_weights(self.weightsFile) - except: - print( - "Unable to load inital weights. Re-instantiate model to load previous weights ...") - K.set_value(self.optimizer.lr, self.init_lr) - print( - "LR Finder is complete, type {LrFinder}.plot_lrs() to see the graph.") - - @staticmethod - def _split_list(vals, skip_start: int, skip_end: int) -> list: - return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:] - - def plot_lrs(self, - skip_start: int = 10, - skip_end: int = 5, - suggestion: bool = False, - show_grid: bool = False, - ) -> None: - """ - Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. - Optionally plot and return min gradient - """ - lrs = self._split_list(self.lrs, skip_start, skip_end) - losses = self._split_list(self.losses, skip_start, skip_end) - _, ax = plt.subplots(1, 1) - ax.plot(lrs, losses) - ax.set_ylabel("Loss") - ax.set_xlabel("Learning Rate") - ax.set_xscale("log") - if show_grid: - plt.grid(True, which="both", ls="-") - ax.xaxis.set_major_formatter(plt.FormatStrFormatter("%.0e")) - if suggestion: - try: - mg = (np.gradient(np.array(losses))).argmin() - except: - print( - "Failed to compute the gradients, there might not be enough points." - ) - return - print(f"Min numerical gradient: {lrs[mg]:.2E}") - ax.plot(lrs[mg], losses[mg], markersize=10, - marker="o", color="red") - self.min_grad_lr = lrs[mg] - ml = np.argmin(losses) - print(f"Min loss divided by 10: {lrs[ml]/10:.2E}") +import tempfile + +import matplotlib.pyplot as plt +import numpy as np + +import tensorflow as tf +from tensorflow import keras +from tqdm.auto import tqdm + +K = keras.backend + + +class Scheduler: + def __init__(self, vals, n_iter: int) -> None: + 'Used to "step" from start,end (`vals`) over `n_iter` s on a schedule defined by `func`' + self.start, self.end = ( + (vals[0], vals[1]) if isinstance(vals, tuple) else (vals, 0) + ) + self.n_iter = max(1, n_iter) + self.func = self._aannealing_exp + self.n = 0 + + @staticmethod + def _aannealing_exp(start: float, end: float, pct: float) -> float: + "Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0." + return start * (end / start) ** pct + + def restart(self) -> None: + self.n = 0 + + def step(self) -> float: + self.n += 1 + return self.func(self.start, self.end, self.n / self.n_iter) + + @property + def is_done(self) -> bool: + "Return `True` if schedule completed." + return self.n >= self.n_iter + + +class LrFinder: + """ + [LrFinder Implemetation taken from Fast.ai] + (https://github.com/fastai/fastai/tree/master/fastai) + + The learning rate range test increases the learning rate in a pre-training run + between two boundaries in a linear or exponential manner. It provides valuable + information on how well the network can be trained over a range of learning rates + and what is the optimal learning rate. + + Args: + model (tf.keras.Model): wrapped model + optimizer (tf.keras.optimizers): wrapped optimizer + loss_fn (tf.keras.losses): loss function + + Example: + >>> lr_finder = LrFinder(model, optimizer, loss_fn) + >>> lr_finder.range_test(trn_ds, end_lr=100, num_iter=100) + >>> lr_finder.plot_lrs() # to inspect the loss-learning rate graph + """ + + def __init__(self, + model: tf.keras.Model, + optimizer: tf.keras.optimizers.Optimizer, + loss_fn: tf.keras.losses.Loss, + ) -> None: + + self.lrs = [] + self.losses = [] + self.model = model + self.optimizer = optimizer + self.loss_fn = loss_fn + self.mw = self.model.get_weights() + self.init_lr = K.get_value(self.optimizer.lr) + self.iteration = 0 + self.weightsFile = tempfile.mkstemp()[1] + + @tf.function + def trn_step(self, xb, yb): + """performs 1 trainig step""" + with tf.GradientTape() as tape: + logits = self.model(xb, training=True) + main_loss = tf.reduce_mean(self.loss_fn(yb, logits)) + loss = tf.add_n([main_loss] + self.model.losses) + grads = tape.gradient(loss, self.model.trainable_variables) + return loss, grads + + def range_test(self, + trn_ds: tf.data.Dataset, + start_lr: float = 1e-7, + end_lr: float = 10, + num_iter: int = 100, + beta=0.98, + ) -> None: + """ + Explore lr from `start_lr` to `end_lr` over `num_it` s in `model`. + + Args: + trn_ds (tf.data.Dataset) + start_lr (float, optional): the starting learning rate for the range test. + Default:1e-07. + end_lr (float, optional): the maximum learning rate to test. Default: 10. + num_iter (int, optional): the number of s over which the test + occurs. Default: 100. + beta (float, optional): the loss smoothing factor within the [0, 1] + interval. The loss is smoothed using exponential smoothing. + Default: 0.98. + """ + # save original model weights + try: + self.model.save_weights(self.weightsFile) + except: + print("Unable to save initial weights, weights of model will change. Re-instantiate model to load previous weights ...") + # start scheduler + sched = Scheduler((start_lr, end_lr), num_iter) + avg_loss, best_loss, = 0.0, 0.0 + # set the startig lr + K.set_value(self.optimizer.lr, sched.start) + + print(f"Finding best initial lr over {num_iter} steps") + # initialize tqdm bar + bar = tqdm(iterable=range(num_iter)) + + # iterate over the batches + for (xb, yb) in trn_ds: + self.iteration += 1 + loss, grads = self.trn_step(xb, yb) + # compute smoothed loss + avg_loss = beta * avg_loss + (1 - beta) * loss + smoothed_loss = avg_loss / (1 - beta ** self.iteration) + + # record best loss + if self.iteration == 1 or smoothed_loss < best_loss: + best_loss = smoothed_loss + + # stop if loss is exploding + if sched.is_done or ( + smoothed_loss > 4 * best_loss or np.isnan(smoothed_loss) + ): + break + + # append losses and lrs + self.losses.append(smoothed_loss) + self.lrs.append(K.get_value(self.optimizer.lr)) + + # update weights + self.optimizer.apply_gradients( + zip(grads, self.model.trainable_variables)) + + # update lr + K.set_value(self.optimizer.lr, sched.step()) + + # update tqdm + bar.update(1) + + # clean-up + bar.close() + sched.restart() + self._print_prompt() + + def _print_prompt(self) -> None: + "Cleanup model weights disturbed during LRFinder exploration." + try: + self.model.load_weights(self.weightsFile) + except: + print( + "Unable to load inital weights. Re-instantiate model to load previous weights ...") + K.set_value(self.optimizer.lr, self.init_lr) + print( + "LR Finder is complete, type {LrFinder}.plot_lrs() to see the graph.") + + @staticmethod + def _split_list(vals, skip_start: int, skip_end: int) -> list: + return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:] + + def plot_lrs(self, + skip_start: int = 10, + skip_end: int = 5, + suggestion: bool = False, + show_grid: bool = False, + ) -> None: + """ + Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. + Optionally plot and return min gradient + """ + lrs = self._split_list(self.lrs, skip_start, skip_end) + losses = self._split_list(self.losses, skip_start, skip_end) + _, ax = plt.subplots(1, 1) + ax.plot(lrs, losses) + ax.set_ylabel("Loss") + ax.set_xlabel("Learning Rate") + ax.set_xscale("log") + if show_grid: + plt.grid(True, which="both", ls="-") + ax.xaxis.set_major_formatter(plt.FormatStrFormatter("%.0e")) + if suggestion: + try: + mg = (np.gradient(np.array(losses))).argmin() + except: + print( + "Failed to compute the gradients, there might not be enough points." + ) + return + print(f"Min numerical gradient: {lrs[mg]:.2E}") + ax.plot(lrs[mg], losses[mg], markersize=10, + marker="o", color="red") + self.min_grad_lr = lrs[mg] + ml = np.argmin(losses) + print(f"Min loss divided by 10: {lrs[ml]/10:.2E}") diff --git a/Interface/GUI/Data/Utils/one_cycle.py b/Interface/GUI/Data/Utils/one_cycle.py index 63739cd..dd3adf8 100644 --- a/Interface/GUI/Data/Utils/one_cycle.py +++ b/Interface/GUI/Data/Utils/one_cycle.py @@ -1,243 +1,243 @@ -from tensorflow import keras -import tensorflow as tf -import math -import matplotlib.pyplot as plt -import numpy as np - -K = keras.backend - - -class OneCycleLr(keras.callbacks.Callback): - """ - Sets the learning rate of each parameter group according to the - 1cycle learning rate policy. The 1cycle policy anneals the learning - rate from an initial learning rate to some maximum learning rate and then - from that maximum learning rate to some minimum learning rate much lower - than the initial learning rate. - This policy was initially described in the paper `Super-Convergence: - Very Fast Training of Neural Networks Using Large Learning Rates`_. - - [Implementation taken from PyTorch: - (https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR)] - - Note also that the total number of steps in the cycle can be determined in one - of two ways (listed in order of precedence): - - #. A value for total_steps is explicitly provided. - #. A number of epochs (epochs) and a number of steps per epoch - (steps_per_epoch) are provided. - In this case, the number of total steps is inferred by - total_steps = epochs * steps_per_epoch - You must either provide a value for total_steps or provide a value for both - epochs and steps_per_epoch. - - Args: - max_lr (float): Upper learning rate boundaries in the cycle. - total_steps (int): The total number of steps in the cycle. Note that - if a value is not provided here, then it must be inferred by providing - a value for epochs and steps_per_epoch. - Default: None - epochs (int): The number of epochs to train for. This is used along - with steps_per_epoch in order to infer the total number of steps in the cycle - if a value for total_steps is not provided. - Default: None - steps_per_epoch (int): The number of steps per epoch to train for. This is - used along with epochs in order to infer the total number of steps in the - cycle if a value for total_steps is not provided. - Default: None - pct_start (float): The percentage of the cycle (in number of steps) spent - increasing the learning rate. - Default: 0.3 - anneal_strategy (str): {'cos', 'linear'} - Specifies the annealing strategy: "cos" for cosine annealing, "linear" for - linear annealing. - Default: 'cos' - cycle_momentum (bool): If ``True``, momentum is cycled inversely - to learning rate between 'base_momentum' and 'max_momentum'. - Default: True - base_momentum (float): Lower momentum boundaries in the cycle - for each parameter group. Note that momentum is cycled inversely - to learning rate; at the peak of a cycle, momentum is - 'base_momentum' and learning rate is 'max_lr'. - Default: 0.85 - max_momentum (float or list): Upper momentum boundaries in the cycle - for each parameter group. Functionally, - it defines the cycle amplitude (max_momentum - base_momentum). - Note that momentum is cycled inversely - to learning rate; at the start of a cycle, momentum is 'max_momentum' - and learning rate is 'base_lr' - Default: 0.95 - div_factor (float): Determines the initial learning rate via - initial_lr = max_lr/div_factor - Default: 25 - final_div_factor (float): Determines the minimum learning rate via - min_lr = initial_lr/final_div_factor - Default: 1e4 - """ - - def __init__(self, - max_lr: float, - total_steps: int = None, - epochs: int = None, - steps_per_epoch: int = None, - pct_start: float = 0.3, - anneal_strategy: str = "cos", - cycle_momentum: bool = True, - base_momentum: float = 0.85, - max_momentum: float = 0.95, - div_factor: float = 25.0, - final_div_factor: float = 1e4, - ) -> None: - - super(OneCycleLr, self).__init__() - - # validate total steps: - if total_steps is None and epochs is None and steps_per_epoch is None: - raise ValueError( - "You must define either total_steps OR (epochs AND steps_per_epoch)" - ) - elif total_steps is not None: - if total_steps <= 0 or not isinstance(total_steps, int): - raise ValueError( - "Expected non-negative integer total_steps, but got {}".format( - total_steps - ) - ) - self.total_steps = total_steps - else: - if epochs <= 0 or not isinstance(epochs, int): - raise ValueError( - "Expected non-negative integer epochs, but got {}".format( - epochs) - ) - if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int): - raise ValueError( - "Expected non-negative integer steps_per_epoch, but got {}".format( - steps_per_epoch - ) - ) - # Compute total steps - self.total_steps = epochs * steps_per_epoch - - self.step_num = 0 - self.step_size_up = float(pct_start * self.total_steps) - 1 - self.step_size_down = float(self.total_steps - self.step_size_up) - 1 - - # Validate pct_start - if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): - raise ValueError( - "Expected float between 0 and 1 pct_start, but got {}".format( - pct_start) - ) - - # Validate anneal_strategy - if anneal_strategy not in ["cos", "linear"]: - raise ValueError( - "anneal_strategy must by one of 'cos' or 'linear', instead got {}".format( - anneal_strategy - ) - ) - elif anneal_strategy == "cos": - self.anneal_func = self._annealing_cos - elif anneal_strategy == "linear": - self.anneal_func = self._annealing_linear - - # Initialize learning rate variables - self.initial_lr = max_lr / div_factor - self.max_lr = max_lr - self.min_lr = self.initial_lr / final_div_factor - - # Initial momentum variables - self.cycle_momentum = cycle_momentum - if self.cycle_momentum: - self.m_momentum = max_momentum - self.momentum = max_momentum - self.b_momentum = base_momentum - - # Initialize variable to learning_rate & momentum - self.track_lr = [] - self.track_mom = [] - - def _annealing_cos(self, start, end, pct) -> float: - "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." - cos_out = math.cos(math.pi * pct) + 1 - return end + (start - end) / 2.0 * cos_out - - def _annealing_linear(self, start, end, pct) -> float: - "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." - return (end - start) * pct + start - - def set_lr_mom(self) -> None: - """Update the learning rate and momentum""" - if self.step_num <= self.step_size_up: - # update learining rate - computed_lr = self.anneal_func( - self.initial_lr, self.max_lr, self.step_num / self.step_size_up - ) - K.set_value(self.model.optimizer.lr, computed_lr) - # update momentum if cycle_momentum - if self.cycle_momentum: - computed_momentum = self.anneal_func( - self.m_momentum, self.b_momentum, self.step_num / self.step_size_up - ) - try: - K.set_value(self.model.optimizer.momentum, - computed_momentum) - except: - K.set_value(self.model.optimizer.beta_1, computed_momentum) - else: - down_step_num = self.step_num - self.step_size_up - # update learning rate - computed_lr = self.anneal_func( - self.max_lr, self.min_lr, down_step_num / self.step_size_down - ) - K.set_value(self.model.optimizer.lr, computed_lr) - # update momentum if cycle_momentum - if self.cycle_momentum: - computed_momentum = self.anneal_func( - self.b_momentum, - self.m_momentum, - down_step_num / self.step_size_down, - ) - try: - K.set_value(self.model.optimizer.momentum, - computed_momentum) - except: - K.set_value(self.model.optimizer.beta_1, computed_momentum) - - def on_train_begin(self, logs=None) -> None: - # Set initial learning rate & momentum values - K.set_value(self.model.optimizer.lr, self.initial_lr) - if self.cycle_momentum: - try: - K.set_value(self.model.optimizer.momentum, self.momentum) - except: - K.set_value(self.model.optimizer.beta_1, self.momentum) - - def on_train_batch_end(self, batch, logs=None) -> None: - # Grab the current learning rate & momentum - lr = float(K.get_value(self.model.optimizer.lr)) - try: - mom = float(K.get_value(self.model.optimizer.momentum)) - except: - mom = float(K.get_value(self.model.optimizer.beta_1)) - # Append to the list - self.track_lr.append(lr) - self.track_mom.append(mom) - # Update learning rate & momentum - self.set_lr_mom() - # increment step_num - self.step_num += 1 - - def plot_lrs_moms(self, axes=None) -> None: - if axes == None: - _, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) - else: - try: - ax1, ax2 = axes - except: - ax1, ax2 = axes[0], axes[1] - ax1.plot(self.track_lr) - ax1.set_title("Learning Rate vs Steps") - ax2.plot(self.track_mom) - ax2.set_title("Momentum (or beta_1) vs Steps") +from tensorflow import keras +import tensorflow as tf +import math +import matplotlib.pyplot as plt +import numpy as np + +K = keras.backend + + +class OneCycleLr(keras.callbacks.Callback): + """ + Sets the learning rate of each parameter group according to the + 1cycle learning rate policy. The 1cycle policy anneals the learning + rate from an initial learning rate to some maximum learning rate and then + from that maximum learning rate to some minimum learning rate much lower + than the initial learning rate. + This policy was initially described in the paper `Super-Convergence: + Very Fast Training of Neural Networks Using Large Learning Rates`_. + + [Implementation taken from PyTorch: + (https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR)] + + Note also that the total number of steps in the cycle can be determined in one + of two ways (listed in order of precedence): + + #. A value for total_steps is explicitly provided. + #. A number of epochs (epochs) and a number of steps per epoch + (steps_per_epoch) are provided. + In this case, the number of total steps is inferred by + total_steps = epochs * steps_per_epoch + You must either provide a value for total_steps or provide a value for both + epochs and steps_per_epoch. + + Args: + max_lr (float): Upper learning rate boundaries in the cycle. + total_steps (int): The total number of steps in the cycle. Note that + if a value is not provided here, then it must be inferred by providing + a value for epochs and steps_per_epoch. + Default: None + epochs (int): The number of epochs to train for. This is used along + with steps_per_epoch in order to infer the total number of steps in the cycle + if a value for total_steps is not provided. + Default: None + steps_per_epoch (int): The number of steps per epoch to train for. This is + used along with epochs in order to infer the total number of steps in the + cycle if a value for total_steps is not provided. + Default: None + pct_start (float): The percentage of the cycle (in number of steps) spent + increasing the learning rate. + Default: 0.3 + anneal_strategy (str): {'cos', 'linear'} + Specifies the annealing strategy: "cos" for cosine annealing, "linear" for + linear annealing. + Default: 'cos' + cycle_momentum (bool): If ``True``, momentum is cycled inversely + to learning rate between 'base_momentum' and 'max_momentum'. + Default: True + base_momentum (float): Lower momentum boundaries in the cycle + for each parameter group. Note that momentum is cycled inversely + to learning rate; at the peak of a cycle, momentum is + 'base_momentum' and learning rate is 'max_lr'. + Default: 0.85 + max_momentum (float or list): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (max_momentum - base_momentum). + Note that momentum is cycled inversely + to learning rate; at the start of a cycle, momentum is 'max_momentum' + and learning rate is 'base_lr' + Default: 0.95 + div_factor (float): Determines the initial learning rate via + initial_lr = max_lr/div_factor + Default: 25 + final_div_factor (float): Determines the minimum learning rate via + min_lr = initial_lr/final_div_factor + Default: 1e4 + """ + + def __init__(self, + max_lr: float, + total_steps: int = None, + epochs: int = None, + steps_per_epoch: int = None, + pct_start: float = 0.3, + anneal_strategy: str = "cos", + cycle_momentum: bool = True, + base_momentum: float = 0.85, + max_momentum: float = 0.95, + div_factor: float = 25.0, + final_div_factor: float = 1e4, + ) -> None: + + super(OneCycleLr, self).__init__() + + # validate total steps: + if total_steps is None and epochs is None and steps_per_epoch is None: + raise ValueError( + "You must define either total_steps OR (epochs AND steps_per_epoch)" + ) + elif total_steps is not None: + if total_steps <= 0 or not isinstance(total_steps, int): + raise ValueError( + "Expected non-negative integer total_steps, but got {}".format( + total_steps + ) + ) + self.total_steps = total_steps + else: + if epochs <= 0 or not isinstance(epochs, int): + raise ValueError( + "Expected non-negative integer epochs, but got {}".format( + epochs) + ) + if steps_per_epoch <= 0 or not isinstance(steps_per_epoch, int): + raise ValueError( + "Expected non-negative integer steps_per_epoch, but got {}".format( + steps_per_epoch + ) + ) + # Compute total steps + self.total_steps = epochs * steps_per_epoch + + self.step_num = 0 + self.step_size_up = float(pct_start * self.total_steps) - 1 + self.step_size_down = float(self.total_steps - self.step_size_up) - 1 + + # Validate pct_start + if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float): + raise ValueError( + "Expected float between 0 and 1 pct_start, but got {}".format( + pct_start) + ) + + # Validate anneal_strategy + if anneal_strategy not in ["cos", "linear"]: + raise ValueError( + "anneal_strategy must by one of 'cos' or 'linear', instead got {}".format( + anneal_strategy + ) + ) + elif anneal_strategy == "cos": + self.anneal_func = self._annealing_cos + elif anneal_strategy == "linear": + self.anneal_func = self._annealing_linear + + # Initialize learning rate variables + self.initial_lr = max_lr / div_factor + self.max_lr = max_lr + self.min_lr = self.initial_lr / final_div_factor + + # Initial momentum variables + self.cycle_momentum = cycle_momentum + if self.cycle_momentum: + self.m_momentum = max_momentum + self.momentum = max_momentum + self.b_momentum = base_momentum + + # Initialize variable to learning_rate & momentum + self.track_lr = [] + self.track_mom = [] + + def _annealing_cos(self, start, end, pct) -> float: + "Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0." + cos_out = math.cos(math.pi * pct) + 1 + return end + (start - end) / 2.0 * cos_out + + def _annealing_linear(self, start, end, pct) -> float: + "Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0." + return (end - start) * pct + start + + def set_lr_mom(self) -> None: + """Update the learning rate and momentum""" + if self.step_num <= self.step_size_up: + # update learining rate + computed_lr = self.anneal_func( + self.initial_lr, self.max_lr, self.step_num / self.step_size_up + ) + K.set_value(self.model.optimizer.lr, computed_lr) + # update momentum if cycle_momentum + if self.cycle_momentum: + computed_momentum = self.anneal_func( + self.m_momentum, self.b_momentum, self.step_num / self.step_size_up + ) + try: + K.set_value(self.model.optimizer.momentum, + computed_momentum) + except: + K.set_value(self.model.optimizer.beta_1, computed_momentum) + else: + down_step_num = self.step_num - self.step_size_up + # update learning rate + computed_lr = self.anneal_func( + self.max_lr, self.min_lr, down_step_num / self.step_size_down + ) + K.set_value(self.model.optimizer.lr, computed_lr) + # update momentum if cycle_momentum + if self.cycle_momentum: + computed_momentum = self.anneal_func( + self.b_momentum, + self.m_momentum, + down_step_num / self.step_size_down, + ) + try: + K.set_value(self.model.optimizer.momentum, + computed_momentum) + except: + K.set_value(self.model.optimizer.beta_1, computed_momentum) + + def on_train_begin(self, logs=None) -> None: + # Set initial learning rate & momentum values + K.set_value(self.model.optimizer.lr, self.initial_lr) + if self.cycle_momentum: + try: + K.set_value(self.model.optimizer.momentum, self.momentum) + except: + K.set_value(self.model.optimizer.beta_1, self.momentum) + + def on_train_batch_end(self, batch, logs=None) -> None: + # Grab the current learning rate & momentum + lr = float(K.get_value(self.model.optimizer.lr)) + try: + mom = float(K.get_value(self.model.optimizer.momentum)) + except: + mom = float(K.get_value(self.model.optimizer.beta_1)) + # Append to the list + self.track_lr.append(lr) + self.track_mom.append(mom) + # Update learning rate & momentum + self.set_lr_mom() + # increment step_num + self.step_num += 1 + + def plot_lrs_moms(self, axes=None) -> None: + if axes == None: + _, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) + else: + try: + ax1, ax2 = axes + except: + ax1, ax2 = axes[0], axes[1] + ax1.plot(self.track_lr) + ax1.set_title("Learning Rate vs Steps") + ax2.plot(self.track_mom) + ax2.set_title("Momentum (or beta_1) vs Steps") diff --git a/Interface/GUI/Data/Utils/print_color_V1_OLD.py b/Interface/GUI/Data/Utils/print_color_V1_OLD.py index 15cd24a..7f37955 100644 --- a/Interface/GUI/Data/Utils/print_color_V1_OLD.py +++ b/Interface/GUI/Data/Utils/print_color_V1_OLD.py @@ -1,89 +1,89 @@ -#the print_Color func -def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False, return_str: bool = False): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. - colors (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. - print_END (str): The string appended after the final output. Default is '\\n'. - advanced_mode (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. - return_str (bool): If True, returns the colored string instead of printing it. Default is False. - Examples: - ~~~python - print_Color('Hello, World!', ['green']) - # Prints 'Hello, World!' in green. - - print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) - # Prints 'Hello in green' in green and 'Hello in red' in red. - - Note: - The advanced terminal colors can be used by providing the escape sequences directly in the colors list. - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m', - 'bold': '\x1b[1m', - 'underline': '\x1b[4m', - 'blink': '\x1b[5m' - } - return_temp = '' - if not advanced_mode: - if colors[0] in color_code: - if return_str: - return color_code[colors[0]] + Input + '\x1b[0m' - print(color_code[colors[0]] + Input + '\x1b[0m', end=print_END) - else: - print("[print_Color] ERROR: Invalid color input!!!") - else: - substrings = Input.split('~*') - if len(substrings) != len(colors) + 1: - print( - "[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") - else: - for sub_str, color in zip(substrings, ['normal'] + colors): - if color in color_code: - if return_str: - return_temp += color_code[color] + sub_str + '\x1b[0m' - else: - print(color_code[color] + sub_str + '\x1b[0m', end='') - else: - print( - f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") - print('', end=print_END) - if return_str: - return return_temp +#the print_Color func +def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False, return_str: bool = False): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. In advanced mode, '~*' is used to separate different parts of the string to be printed in different colors. + colors (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. + print_END (str): The string appended after the final output. Default is '\\n'. + advanced_mode (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. + return_str (bool): If True, returns the colored string instead of printing it. Default is False. + Examples: + ~~~python + print_Color('Hello, World!', ['green']) + # Prints 'Hello, World!' in green. + + print_Color('~*Hello in green~*Hello in red', ['green', 'red'], advanced_mode=True) + # Prints 'Hello in green' in green and 'Hello in red' in red. + + Note: + The advanced terminal colors can be used by providing the escape sequences directly in the colors list. + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m', + 'bold': '\x1b[1m', + 'underline': '\x1b[4m', + 'blink': '\x1b[5m' + } + return_temp = '' + if not advanced_mode: + if colors[0] in color_code: + if return_str: + return color_code[colors[0]] + Input + '\x1b[0m' + print(color_code[colors[0]] + Input + '\x1b[0m', end=print_END) + else: + print("[print_Color] ERROR: Invalid color input!!!") + else: + substrings = Input.split('~*') + if len(substrings) != len(colors) + 1: + print( + "[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") + else: + for sub_str, color in zip(substrings, ['normal'] + colors): + if color in color_code: + if return_str: + return_temp += color_code[color] + sub_str + '\x1b[0m' + else: + print(color_code[color] + sub_str + '\x1b[0m', end='') + else: + print( + f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") + print('', end=print_END) + if return_str: + return return_temp #the func end \ No newline at end of file diff --git a/Interface/GUI/Data/Utils/print_color_V2_NEW.py b/Interface/GUI/Data/Utils/print_color_V2_NEW.py index 603d673..15b73a3 100644 --- a/Interface/GUI/Data/Utils/print_color_V2_NEW.py +++ b/Interface/GUI/Data/Utils/print_color_V2_NEW.py @@ -1,76 +1,76 @@ -import re - -def print_Color_V2(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): - """ - Prints colored text to the console using advanced terminal colors. - - Args: - Input (str): The input string to be printed. '' is used to specify the color of the following text. - print_END (str): The string appended after the final output. Default is '\\n'. - start_char (str): The character used as the start of the color specifier. Default is '<'. - end_char (str): The character used as the end of the color specifier. Default is '>'. - - Examples: - ~~~python - print_Color('Hello, World!') - # Prints 'Hello, World!' in normal color. - - print_Color('Hello in red Hello in green') - # Prints 'Hello in red' in red and 'Hello in green' in green. - - print_Color('~red!Hello in red', start_char='~', end_char='!') - # Prints 'Hello, World!' in normal color. - - Note: - If an invalid color is provided, an error message will be printed. - """ - color_code = { - 'black': '\x1b[0;30m', - 'red': '\x1b[0;31m', - 'green': '\x1b[0;32m', - 'yellow': '\x1b[0;33m', - 'blue': '\x1b[0;34m', - 'magenta': '\x1b[0;35m', - 'cyan': '\x1b[0;36m', - 'white': '\x1b[0;37m', - 'normal': '\x1b[0m', - 'bg_black': '\x1b[40m', - 'bg_red': '\x1b[41m', - 'bg_green': '\x1b[42m', - 'bg_yellow': '\x1b[43m', - 'bg_blue': '\x1b[44m', - 'bg_magenta': '\x1b[45m', - 'bg_cyan': '\x1b[46m', - 'bg_white': '\x1b[47m', - 'bg_normal': '\x1b[49m', - 'light_gray': '\x1b[0;90m', - 'light_red': '\x1b[0;91m', - 'light_green': '\x1b[0;92m', - 'light_yellow': '\x1b[0;93m', - 'light_blue': '\x1b[0;94m', - 'light_magenta': '\x1b[0;95m', - 'light_cyan': '\x1b[0;96m', - 'light_white': '\x1b[0;97m', - 'bg_light_gray': '\x1b[0;100m', - 'bg_light_red': '\x1b[0;101m', - 'bg_light_green': '\x1b[0;102m', - 'bg_light_yellow': '\x1b[0;103m', - 'bg_light_blue': '\x1b[0;104m', - 'bg_light_magenta': '\x1b[0;105m', - 'bg_light_cyan': '\x1b[0;106m', - 'bg_light_white': '\x1b[0;107m' - } - pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) - substrings = re.split(pattern, Input) - current_color = 'normal' - for i, sub_str in enumerate(substrings): - if i % 2 == 0: - print(color_code[current_color] + sub_str + color_code['normal'], end='') - current_color = 'normal' - else: - color = sub_str.strip() - if color in color_code: - current_color = color - else: - print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") - print('', end=print_END) +import re + +def print_Color_V2(Input: str, print_END: str = '\n', start_char: str = '<', end_char: str = '>'): + """ + Prints colored text to the console using advanced terminal colors. + + Args: + Input (str): The input string to be printed. '' is used to specify the color of the following text. + print_END (str): The string appended after the final output. Default is '\\n'. + start_char (str): The character used as the start of the color specifier. Default is '<'. + end_char (str): The character used as the end of the color specifier. Default is '>'. + + Examples: + ~~~python + print_Color('Hello, World!') + # Prints 'Hello, World!' in normal color. + + print_Color('Hello in red Hello in green') + # Prints 'Hello in red' in red and 'Hello in green' in green. + + print_Color('~red!Hello in red', start_char='~', end_char='!') + # Prints 'Hello, World!' in normal color. + + Note: + If an invalid color is provided, an error message will be printed. + """ + color_code = { + 'black': '\x1b[0;30m', + 'red': '\x1b[0;31m', + 'green': '\x1b[0;32m', + 'yellow': '\x1b[0;33m', + 'blue': '\x1b[0;34m', + 'magenta': '\x1b[0;35m', + 'cyan': '\x1b[0;36m', + 'white': '\x1b[0;37m', + 'normal': '\x1b[0m', + 'bg_black': '\x1b[40m', + 'bg_red': '\x1b[41m', + 'bg_green': '\x1b[42m', + 'bg_yellow': '\x1b[43m', + 'bg_blue': '\x1b[44m', + 'bg_magenta': '\x1b[45m', + 'bg_cyan': '\x1b[46m', + 'bg_white': '\x1b[47m', + 'bg_normal': '\x1b[49m', + 'light_gray': '\x1b[0;90m', + 'light_red': '\x1b[0;91m', + 'light_green': '\x1b[0;92m', + 'light_yellow': '\x1b[0;93m', + 'light_blue': '\x1b[0;94m', + 'light_magenta': '\x1b[0;95m', + 'light_cyan': '\x1b[0;96m', + 'light_white': '\x1b[0;97m', + 'bg_light_gray': '\x1b[0;100m', + 'bg_light_red': '\x1b[0;101m', + 'bg_light_green': '\x1b[0;102m', + 'bg_light_yellow': '\x1b[0;103m', + 'bg_light_blue': '\x1b[0;104m', + 'bg_light_magenta': '\x1b[0;105m', + 'bg_light_cyan': '\x1b[0;106m', + 'bg_light_white': '\x1b[0;107m' + } + pattern = re.escape(start_char) + r'([^' + re.escape(end_char) + r']*)' + re.escape(end_char) + substrings = re.split(pattern, Input) + current_color = 'normal' + for i, sub_str in enumerate(substrings): + if i % 2 == 0: + print(color_code[current_color] + sub_str + color_code['normal'], end='') + current_color = 'normal' + else: + color = sub_str.strip() + if color in color_code: + current_color = color + else: + print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}'") + print('', end=print_END) diff --git a/Interface/GUI/Data/model_info.json b/Interface/GUI/Data/model_info.json index a295bd3..9fbd72a 100644 --- a/Interface/GUI/Data/model_info.json +++ b/Interface/GUI/Data/model_info.json @@ -1,112 +1,112 @@ -{ - "951f72260abe755b1be2feabfd656399ce44191531c8be7c2a8434b6657aa02a": { - "name": "PAI_model.h5", - "Ver": "V1", - "stored_type": "Full" - }, - "73b5585a4f6a5594f1912effef04482ffc52ca499818b64d0c76aa8e1d8d4914": { - "name": "PAI_model.h5", - "Ver": "V2", - "stored_type": "Full" - }, - "518c5b8980236023f6c202a69da50037144cd42c89d7d902ef24b62d52fd5a5c": { - "name": "PAI_model.h5", - "Ver": "V3", - "stored_type": "Full" - }, - "b7468f69964eab7c1d90cfc310c2239913962660ec61b7a2dcfdc4b4fd7410c1": { - "name": "PAI_model_weights.h5", - "Ver": "V3", - "stored_type": "Weight" - }, - "8984fd1795a62071eb6f533cb6daecaa667629857b435edbfb5fc53579a1895d": { - "name": "PAI_model_T.h5", - "Ver": "V4", - "stored_type": "Full" - }, - "3670c64fe36b8dec1550741df96f3de448fe9a41d79c748d5d5131c5552abe0a": { - "name": "PAI_model_T_BL.h5", - "Ver": "V4", - "stored_type": "Full" - }, - "add13add185e4e9ae513eaf7cd1e619b8f63fc5dc10fc40e2155d5a8395f64ed": { - "name": "PAI_model_T.h5", - "Ver": "V5", - "stored_type": "Full" - }, - "684c7a6d164754416472653a05ec412bc46d600b16a3657a9d90f475dc01eace": { - "name": "PAI_model_T_BL.h5", - "Ver": "V5", - "stored_type": "Full" - }, - "591322c2db2b2775ea459e06425ef9ec5525df59c939efd92f6f52494f41aa6c": { - "name": "PAI_model_weights.h5", - "Ver": "V5", - "stored_type": "Weight" - }, - "128b50d85bddf789f502de94e120199f41ce623698a884e45597903f249af206": { - "name": "PAI_model_weights_BL.h5", - "Ver": "V5", - "stored_type": "Weight" - }, - "2efcb19eb939c385732379fd8676c8b571a69f49ed4b649bdbcb8b4fc591da11": { - "name": "PAI_model_T.h5", - "Ver": "V5 Beta", - "stored_type": "Full" - }, - "82db69de62bbff849f879bfa3d2c3d78b63ced157118dd4f43f563c1980bc399": { - "name": "PAI_model_T_BL.h5", - "Ver": "V5 Beta", - "stored_type": "Full" - }, - "3123992c0e560c64fe1f34ddc9942e6c042a83e9e58727886f1abb7703dae61c": { - "name": "PAI_model_weights.h5", - "Ver": "V5 Beta", - "stored_type": "Weight" - }, - "5c16fd6b267cc423e326763a5589bdcb1139a9bcb79717957d9abaa870a38740": { - "name": "PAI_model_weights_BL.h5", - "Ver": "V5 Beta", - "stored_type": "Weight" - }, - "9f1d5a99a42c32adba4bd8f209551a1543e2648e8f20109b6b7c48c8c55a5ccc": { - "name": "PAI_model_T.h5", - "Ver": "V6", - "stored_type": "Full" - }, - "ee08478a334bd4828e9cdb9a0f3275deb77c1081a364d182b65130044c6fdf20": { - "name": "PAI_model_T_BL.h5", - "Ver": "V6", - "stored_type": "Full" - }, - "991c445c20fd539fcf4ee9840b61505a2210243c4ba3afd04383f76d852eb880": { - "name": "PAI_model_weights.h5", - "Ver": "V6", - "stored_type": "Weight" - }, - "b3afee50de221daf1d979de01ee4c4383baa3c80e6c7c3f7caa703399f81514b": { - "name": "PAI_model_weights_BL.h5", - "Ver": "V6", - "stored_type": "Weight" - }, - "5f168e87918f14a2c051cd9c915385b31afba2884c4b131577e2c78a888a2816": { - "name": "PAI_model_light_T.h5", - "Ver": "V7 light", - "stored_type": "Full" - }, - "9e50c2f062b1d6b699a17457b65d8ca507b4ad1c5e55a3072edd7bd7f6bf7841": { - "name": "PAI_model_light_T_BL.h5", - "Ver": "V7 light", - "stored_type": "Full" - }, - "c1d7b5d70ebcd0c76b671ff480194eed9581747430595a0881d610d9b2a686c8": { - "name": "PAI_model_light_weights.h5", - "Ver": "V7 light", - "stored_type": "Weight" - }, - "75f011b700aadafbd348be361323f68c1d8456fa25aeba0b455c93e796238807": { - "name": "PAI_model_light_weights_BL.h5", - "Ver": "V7 light", - "stored_type": "Weight" - } +{ + "951f72260abe755b1be2feabfd656399ce44191531c8be7c2a8434b6657aa02a": { + "name": "PAI_model.h5", + "Ver": "V1", + "stored_type": "Full" + }, + "73b5585a4f6a5594f1912effef04482ffc52ca499818b64d0c76aa8e1d8d4914": { + "name": "PAI_model.h5", + "Ver": "V2", + "stored_type": "Full" + }, + "518c5b8980236023f6c202a69da50037144cd42c89d7d902ef24b62d52fd5a5c": { + "name": "PAI_model.h5", + "Ver": "V3", + "stored_type": "Full" + }, + "b7468f69964eab7c1d90cfc310c2239913962660ec61b7a2dcfdc4b4fd7410c1": { + "name": "PAI_model_weights.h5", + "Ver": "V3", + "stored_type": "Weight" + }, + "8984fd1795a62071eb6f533cb6daecaa667629857b435edbfb5fc53579a1895d": { + "name": "PAI_model_T.h5", + "Ver": "V4", + "stored_type": "Full" + }, + "3670c64fe36b8dec1550741df96f3de448fe9a41d79c748d5d5131c5552abe0a": { + "name": "PAI_model_T_BL.h5", + "Ver": "V4", + "stored_type": "Full" + }, + "add13add185e4e9ae513eaf7cd1e619b8f63fc5dc10fc40e2155d5a8395f64ed": { + "name": "PAI_model_T.h5", + "Ver": "V5", + "stored_type": "Full" + }, + "684c7a6d164754416472653a05ec412bc46d600b16a3657a9d90f475dc01eace": { + "name": "PAI_model_T_BL.h5", + "Ver": "V5", + "stored_type": "Full" + }, + "591322c2db2b2775ea459e06425ef9ec5525df59c939efd92f6f52494f41aa6c": { + "name": "PAI_model_weights.h5", + "Ver": "V5", + "stored_type": "Weight" + }, + "128b50d85bddf789f502de94e120199f41ce623698a884e45597903f249af206": { + "name": "PAI_model_weights_BL.h5", + "Ver": "V5", + "stored_type": "Weight" + }, + "2efcb19eb939c385732379fd8676c8b571a69f49ed4b649bdbcb8b4fc591da11": { + "name": "PAI_model_T.h5", + "Ver": "V5 Beta", + "stored_type": "Full" + }, + "82db69de62bbff849f879bfa3d2c3d78b63ced157118dd4f43f563c1980bc399": { + "name": "PAI_model_T_BL.h5", + "Ver": "V5 Beta", + "stored_type": "Full" + }, + "3123992c0e560c64fe1f34ddc9942e6c042a83e9e58727886f1abb7703dae61c": { + "name": "PAI_model_weights.h5", + "Ver": "V5 Beta", + "stored_type": "Weight" + }, + "5c16fd6b267cc423e326763a5589bdcb1139a9bcb79717957d9abaa870a38740": { + "name": "PAI_model_weights_BL.h5", + "Ver": "V5 Beta", + "stored_type": "Weight" + }, + "9f1d5a99a42c32adba4bd8f209551a1543e2648e8f20109b6b7c48c8c55a5ccc": { + "name": "PAI_model_T.h5", + "Ver": "V6", + "stored_type": "Full" + }, + "ee08478a334bd4828e9cdb9a0f3275deb77c1081a364d182b65130044c6fdf20": { + "name": "PAI_model_T_BL.h5", + "Ver": "V6", + "stored_type": "Full" + }, + "991c445c20fd539fcf4ee9840b61505a2210243c4ba3afd04383f76d852eb880": { + "name": "PAI_model_weights.h5", + "Ver": "V6", + "stored_type": "Weight" + }, + "b3afee50de221daf1d979de01ee4c4383baa3c80e6c7c3f7caa703399f81514b": { + "name": "PAI_model_weights_BL.h5", + "Ver": "V6", + "stored_type": "Weight" + }, + "5f168e87918f14a2c051cd9c915385b31afba2884c4b131577e2c78a888a2816": { + "name": "PAI_model_light_T.h5", + "Ver": "V7 light", + "stored_type": "Full" + }, + "9e50c2f062b1d6b699a17457b65d8ca507b4ad1c5e55a3072edd7bd7f6bf7841": { + "name": "PAI_model_light_T_BL.h5", + "Ver": "V7 light", + "stored_type": "Full" + }, + "c1d7b5d70ebcd0c76b671ff480194eed9581747430595a0881d610d9b2a686c8": { + "name": "PAI_model_light_weights.h5", + "Ver": "V7 light", + "stored_type": "Weight" + }, + "75f011b700aadafbd348be361323f68c1d8456fa25aeba0b455c93e796238807": { + "name": "PAI_model_light_weights_BL.h5", + "Ver": "V7 light", + "stored_type": "Weight" + } } \ No newline at end of file diff --git a/Interface/GUI/Data/requirements.txt b/Interface/GUI/Data/requirements.txt index 78872da..ad8f137 100644 --- a/Interface/GUI/Data/requirements.txt +++ b/Interface/GUI/Data/requirements.txt @@ -1,13 +1,13 @@ -numpy -keras -Pillow -py-cpuinfo -tensorflow -efficientnet -tqdm -matplotlib -opencv-python -loguru -PySimpleGUI -asyncio +numpy +keras +Pillow +py-cpuinfo +tensorflow +efficientnet +tqdm +matplotlib +opencv-python +loguru +PySimpleGUI +asyncio pydicom \ No newline at end of file diff --git a/Interface/GUI/GUI.cmd b/Interface/GUI/GUI.cmd index 356ef3e..c10e5bd 100644 --- a/Interface/GUI/GUI.cmd +++ b/Interface/GUI/GUI.cmd @@ -1,105 +1,105 @@ -@echo off -REM Conf: -setlocal enabledelayedexpansion -TITLE Pneumonia-Detection-Ai-GUI -set python_min_VER=10 -set DEBUG=0 -set Full_Auto=1 -set arg=%1 -set PV_filepath="Data\\Python Ver.tmp" -set python_path=python -set pip_path=pip - -REM Check if the fast start flag is used -if "%arg%"=="-f" ( - goto :FAST_START -) - -REM Check if Python is installed -"%python_path%" --version 2>nul >nul -if errorlevel 1 goto :errorNoPython - -@REM Geting the Python path and Python install time -for /f "delims=" %%p in ('where "%python_path%" 2^>^&1 ^| findstr /v "INFO:"') do ( - set "python_path_env=%%p" -) -for %%A in ("%python_path_env%") do ( - set Python_INSTALLTIME=%%~tA -) - -REM Check if the Python version file exists and matches the current Python version -for /F "delims=" %%i IN ('"%python_path%" --version 2^>^&1') DO set current_python_version=%%i -set "current_python_version=%current_python_version% %Python_INSTALLTIME%" -if not exist %PV_filepath% ( - goto :PASS_PVF_CHECK -) -set /p file_python_version=<%PV_filepath% -if "%file_python_version%"=="%current_python_version% " ( - goto :FAST_START -) - -:PASS_PVF_CHECK -REM Write the current Python version to the file -echo Checking Python version... -REM Ensure Python version is %python_min_VER% or higher -for /F "tokens=2 delims=." %%i IN ('"%python_path%" --version 2^>^&1') DO set python_version_major=%%i -if %python_version_major% LSS %python_min_VER% ( - echo Warning: Please update your Python version to 3.%python_min_VER%.x or higher! - pause - exit /B -) - -REM Check if the required packages are installed -echo Checking the required packages... -for /F "usebackq delims==" %%i in ("Data\requirements.txt") do ( - call :check_install %%i -) -REM Write the current Python version + Python install time to the file -echo %current_python_version% > %PV_filepath% -@REM Pause for user input -echo Press any key to load the GUI... -pause > nul - -:FAST_START -REM Print the appropriate loading message -if "%arg%"=="-f" ( - echo Loading the GUI fast... -) else ( - echo Loading the GUI... -) - -:restart -REM Clear the terminal and start the Python GUI script -timeout /t 1 >nul -cls -"%python_path%" "Data\GUI_main.py" - -goto :EOF - -REM errorNoPython -:errorNoPython -echo Error: Python is not installed -pause -goto :EOF - -:check_install -REM Check if a package is installed and offer to install it if not -set userinput=Y -"%pip_path%" show %1 >nul -if ERRORLEVEL 1 ( - if not if "%Full_Auto%"=="1" ( - echo Package %1 not found. Do you want to automatically install it? [Y/n] - set /p userinput="Answer: " - ) - if /I "%userinput%"=="Y" or "%Full_Auto%"=="1"( - echo Installing package %1 - "%pip_path%" install %1 - if ERRORLEVEL 1 ( - echo Failed to install package %1. - exit /B - ) - ) -) else if "%DEBUG%"=="1" ( - echo Package %1 is already installed. -) -GOTO:EOF +@echo off +REM Conf: +setlocal enabledelayedexpansion +TITLE Pneumonia-Detection-Ai-GUI +set python_min_VER=10 +set DEBUG=0 +set Full_Auto=1 +set arg=%1 +set PV_filepath="Data\\Python Ver.tmp" +set python_path=python +set pip_path=pip + +REM Check if the fast start flag is used +if "%arg%"=="-f" ( + goto :FAST_START +) + +REM Check if Python is installed +"%python_path%" --version 2>nul >nul +if errorlevel 1 goto :errorNoPython + +@REM Geting the Python path and Python install time +for /f "delims=" %%p in ('where "%python_path%" 2^>^&1 ^| findstr /v "INFO:"') do ( + set "python_path_env=%%p" +) +for %%A in ("%python_path_env%") do ( + set Python_INSTALLTIME=%%~tA +) + +REM Check if the Python version file exists and matches the current Python version +for /F "delims=" %%i IN ('"%python_path%" --version 2^>^&1') DO set current_python_version=%%i +set "current_python_version=%current_python_version% %Python_INSTALLTIME%" +if not exist %PV_filepath% ( + goto :PASS_PVF_CHECK +) +set /p file_python_version=<%PV_filepath% +if "%file_python_version%"=="%current_python_version% " ( + goto :FAST_START +) + +:PASS_PVF_CHECK +REM Write the current Python version to the file +echo Checking Python version... +REM Ensure Python version is %python_min_VER% or higher +for /F "tokens=2 delims=." %%i IN ('"%python_path%" --version 2^>^&1') DO set python_version_major=%%i +if %python_version_major% LSS %python_min_VER% ( + echo Warning: Please update your Python version to 3.%python_min_VER%.x or higher! + pause + exit /B +) + +REM Check if the required packages are installed +echo Checking the required packages... +for /F "usebackq delims==" %%i in ("Data\requirements.txt") do ( + call :check_install %%i +) +REM Write the current Python version + Python install time to the file +echo %current_python_version% > %PV_filepath% +@REM Pause for user input +echo Press any key to load the GUI... +pause > nul + +:FAST_START +REM Print the appropriate loading message +if "%arg%"=="-f" ( + echo Loading the GUI fast... +) else ( + echo Loading the GUI... +) + +:restart +REM Clear the terminal and start the Python GUI script +timeout /t 1 >nul +cls +"%python_path%" "Data\GUI_main.py" + +goto :EOF + +REM errorNoPython +:errorNoPython +echo Error: Python is not installed +pause +goto :EOF + +:check_install +REM Check if a package is installed and offer to install it if not +set userinput=Y +"%pip_path%" show %1 >nul +if ERRORLEVEL 1 ( + if not if "%Full_Auto%"=="1" ( + echo Package %1 not found. Do you want to automatically install it? [Y/n] + set /p userinput="Answer: " + ) + if /I "%userinput%"=="Y" or "%Full_Auto%"=="1"( + echo Installing package %1 + "%pip_path%" install %1 + if ERRORLEVEL 1 ( + echo Failed to install package %1. + exit /B + ) + ) +) else if "%DEBUG%"=="1" ( + echo Package %1 is already installed. +) +GOTO:EOF diff --git a/Interface/GUI/LICENSE b/Interface/GUI/LICENSE index 861128e..6bb4d95 100644 --- a/Interface/GUI/LICENSE +++ b/Interface/GUI/LICENSE @@ -1,21 +1,21 @@ -MIT License - -Copyright (c) 2023 Aydin hamedi - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +MIT License + +Copyright (c) 2023 Aydin hamedi + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Interface/GUI/Run.vbs b/Interface/GUI/Run.vbs index 2e80a18..421c387 100644 --- a/Interface/GUI/Run.vbs +++ b/Interface/GUI/Run.vbs @@ -1,3 +1,3 @@ -Set WshShell = CreateObject("WScript.Shell") -WshShell.Run chr(34) & "GUI.cmd" & Chr(34), 0 -Set WshShell = Nothing +Set WshShell = CreateObject("WScript.Shell") +WshShell.Run chr(34) & "GUI.cmd" & Chr(34), 0 +Set WshShell = Nothing \ No newline at end of file diff --git a/LICENSE b/LICENSE index 861128e..6bb4d95 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,21 @@ -MIT License - -Copyright (c) 2023 Aydin hamedi - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +MIT License + +Copyright (c) 2023 Aydin hamedi + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Make_model_info.py b/Make_model_info.py index 777e662..eac4a98 100644 --- a/Make_model_info.py +++ b/Make_model_info.py @@ -1,38 +1,38 @@ -import os -import hashlib -import json - -def calculate_hash(file_path): - with open(file_path, 'rb') as f: - bytes = f.read() - readable_hash = hashlib.sha256(bytes).hexdigest() - return readable_hash - -def check_file_type(file_name): - if 'weight' in file_name.lower(): - return 'Weight' - else: - return 'Full' - -def main(): - base_folder_path = 'models\Ready' - model_info = {} - for dir_name in os.listdir(base_folder_path): - folder_path = os.path.join(base_folder_path, dir_name) - if os.path.isdir(folder_path): - for file_name in os.listdir(folder_path): - file_path = os.path.join(folder_path, file_name) - file_hash = calculate_hash(file_path) - file_type = check_file_type(file_name) - data = { - 'name': file_name, - 'Ver': dir_name, - 'stored_type': file_type - } - model_info[file_hash] = data - - with open('model_info.json', 'w') as json_file: - json.dump(model_info, json_file) - -if __name__ == "__main__": - main() +import os +import hashlib +import json + +def calculate_hash(file_path): + with open(file_path, 'rb') as f: + bytes = f.read() + readable_hash = hashlib.sha256(bytes).hexdigest() + return readable_hash + +def check_file_type(file_name): + if 'weight' in file_name.lower(): + return 'Weight' + else: + return 'Full' + +def main(): + base_folder_path = 'models\Ready' + model_info = {} + for dir_name in os.listdir(base_folder_path): + folder_path = os.path.join(base_folder_path, dir_name) + if os.path.isdir(folder_path): + for file_name in os.listdir(folder_path): + file_path = os.path.join(folder_path, file_name) + file_hash = calculate_hash(file_path) + file_type = check_file_type(file_name) + data = { + 'name': file_name, + 'Ver': dir_name, + 'stored_type': file_type + } + model_info[file_hash] = data + + with open('model_info.json', 'w') as json_file: + json.dump(model_info, json_file) + +if __name__ == "__main__": + main() diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb index 2399064..3a896d1 100644 --- a/Model_T&T.ipynb +++ b/Model_T&T.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:44.939427800Z", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.128539500Z", @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:47.139048Z", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:27:48.287855100Z", @@ -209,15 +209,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "SAVE_TYPE = 'H5'\n", "Use_mixed_float16 = False\n", @@ -239,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.059139500Z", @@ -249,29 +241,7 @@ "groupValue": "12" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mUsing Def IDG...\u001b[0m\n", - "Found 23681 images belonging to 2 classes.\n", - "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", - "\u001b[0;33mMaking categorical data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mNormalizing image data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n", - "\u001b[0;33mSetting LNTS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", - "\u001b[0;33mshuffling data...\u001b[0m\n", - "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d23-h12_m17_s17\u001b[0m\n", - "\u001b[0;32mDone.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "#Z_SCORE_normalize\n", "def Z_SCORE_normalize(arr):\n", @@ -678,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:27.380088800Z", @@ -878,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-27T17:34:12.077394600Z", @@ -888,2164 +858,7 @@ "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n", - " 1[0][0]'] \n", - " \n", - " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " (BatchNormalization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n", - " 2[0][0]'] \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -3139,2471 +952,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total base_model1 layers: 806\n", - "Total base_model2 layers: 132\n", - "Total model layers: 15\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " efficientnet-b7 (Functional) (None, 7, 7, 2560) 64097680 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| stem_conv (Conv2D) (None, 112, 112, 64 1728 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_bn (BatchNormalization) (None, 112, 112, 64 256 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_activation (Activation) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1a_bn (BatchNormalization (None, 112, 112, 64 256 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_activation (Activation (None, 112, 112, 64 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_se_squeeze (GlobalAver (None, 64) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 [] Y |\n", - "| |\n", - "| block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 [] Y |\n", - "| |\n", - "| block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 [] Y |\n", - "| |\n", - "| block1a_se_excite (Multiply) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1b_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1b_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1c_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1c_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1c_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1d_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1d_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1d_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 [] Y |\n", - "| 2) |\n", - "| |\n", - "| block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 [] Y |\n", - "| ization) 2) |\n", - "| |\n", - "| block2a_expand_activation (Act (None, 112, 112, 19 0 [] Y |\n", - "| ivation) 2) |\n", - "| |\n", - "| block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2a_bn (BatchNormalization (None, 56, 56, 192) 768 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_activation (Activation (None, 56, 56, 192) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_se_squeeze (GlobalAver (None, 192) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 [] Y |\n", - "| |\n", - "| block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 [] Y |\n", - "| |\n", - "| block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 [] Y |\n", - "| |\n", - "| block2a_se_excite (Multiply) (None, 56, 56, 192) 0 [] Y |\n", - "| |\n", - "| block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 [] Y |\n", - "| |\n", - "| block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2b_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2b_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2b_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2c_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2c_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2c_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2d_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2d_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2d_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2e_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2e_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2e_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2f_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2f_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2f_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2g_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2g_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2g_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3a_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_activation (Activation (None, 28, 28, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block3a_se_excite (Multiply) (None, 28, 28, 288) 0 [] Y |\n", - "| |\n", - "| block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 [] Y |\n", - "| |\n", - "| block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3b_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3b_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3b_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3c_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3c_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3c_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3d_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3d_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3d_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3e_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3e_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3e_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3f_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3f_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3f_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3g_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3g_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3g_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4a_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_activation (Activation (None, 14, 14, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block4a_se_excite (Multiply) (None, 14, 14, 480) 0 [] Y |\n", - "| |\n", - "| block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 [] Y |\n", - "| |\n", - "| block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4b_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4b_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4b_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4c_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4c_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4c_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4d_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4d_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4d_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4e_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4e_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4e_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4f_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4f_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4f_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4g_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4g_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4g_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4h_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4h_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4h_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4i_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4i_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4i_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4j_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4j_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4j_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block5a_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block5a_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 [] Y |\n", - "| |\n", - "| block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5b_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5b_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5b_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5c_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5c_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5c_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5d_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5d_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5d_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5e_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5e_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5e_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5f_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5f_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5f_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5g_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5g_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5g_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5h_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5h_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5h_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5i_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5i_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5i_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5j_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5j_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5j_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block6a_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_activation (Activation (None, 7, 7, 1344) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 [] Y |\n", - "| |\n", - "| block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6b_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6b_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6c_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6c_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6d_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6d_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6e_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6e_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6f_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6f_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6g_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6g_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6h_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6h_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6i_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6i_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6j_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6j_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6k_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6k_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6l_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6l_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6m_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6m_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7a_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 [] Y |\n", - "| |\n", - "| block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7b_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7b_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7c_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7c_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7d_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7d_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| top_conv (Conv2D) (None, 7, 7, 2560) 1638400 [] Y |\n", - "| |\n", - "| top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 [] Y |\n", - "| |\n", - "| top_activation (Activation) (None, 7, 7, 2560) 0 [] Y |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " xception (Functional) (None, 7, 7, 2048) 20861480 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| block1_conv1 (Conv2D) (None, 111, 111, 32 864 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv1_bn (BatchNormaliz (None, 111, 111, 32 128 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv1_act (Activation) (None, 111, 111, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2 (Conv2D) (None, 109, 109, 64 18432 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2_bn (BatchNormaliz (None, 109, 109, 64 256 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv2_act (Activation) (None, 109, 109, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2_sepconv1 (SeparableConv (None, 109, 109, 12 8768 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv1_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| block2_sepconv2_act (Activatio (None, 109, 109, 12 0 [] Y |\n", - "| n) 8) |\n", - "| |\n", - "| block2_sepconv2 (SeparableConv (None, 109, 109, 12 17536 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv2_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| conv2d (Conv2D) (None, 55, 55, 128) 8192 [] Y |\n", - "| |\n", - "| block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| batch_normalization (BatchNorm (None, 55, 55, 128) 512 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add (Add) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| block3_sepconv1_act (Activatio (None, 55, 55, 128) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv1 (SeparableConv (None, 55, 55, 256) 33920 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv1_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3_sepconv2_act (Activatio (None, 55, 55, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv2 (SeparableConv (None, 55, 55, 256) 67840 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv2_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 [] Y |\n", - "| |\n", - "| block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| batch_normalization_1 (BatchNo (None, 28, 28, 256) 1024 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_1 (Add) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| block4_sepconv1_act (Activatio (None, 28, 28, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv1 (SeparableConv (None, 28, 28, 728) 188672 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv1_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4_sepconv2_act (Activatio (None, 28, 28, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv2 (SeparableConv (None, 28, 28, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv2_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 [] Y |\n", - "| |\n", - "| block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| batch_normalization_2 (BatchNo (None, 14, 14, 728) 2912 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_2 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block5_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_3 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block6_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_4 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block7_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_5 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block8_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_6 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block9_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_7 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block10_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_8 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block11_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_9 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block12_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_10 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block13_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block13_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block13_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv2 (SeparableCon (None, 14, 14, 1024 752024 [] Y |\n", - "| v2D) ) |\n", - "| |\n", - "| block13_sepconv2_bn (BatchNorm (None, 14, 14, 1024 4096 [] Y |\n", - "| alization) ) |\n", - "| |\n", - "| conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 [] Y |\n", - "| |\n", - "| block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_11 (Add) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 [] Y |\n", - "| on) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (Glob (None, 2560) 0 ['efficientnet-b7[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " global_average_pooling2d_1 (Gl (None, 2048) 0 ['xception[0][0]'] Y \n", - " obalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dense_1 (Dense) (None, 512) 1049088 ['global_average_pooling2d_1[0] Y \n", - " [0]'] \n", - " \n", - " concatenate (Concatenate) (None, 1024) 0 ['dense[0][0]', Y \n", - " 'dense_1[0][0]'] \n", - " \n", - " dense_2 (Dense) (None, 1024) 1049600 ['concatenate[0][0]'] Y \n", - " \n", - " dropout (Dropout) (None, 1024) 0 ['dense_2[0][0]'] Y \n", - " \n", - " batch_normalization_4 (BatchNo (None, 1024) 4096 ['dropout[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_3 (Dense) (None, 512) 524800 ['batch_normalization_4[0][0]'] Y \n", - " \n", - " batch_normalization_5 (BatchNo (None, 512) 2048 ['dense_3[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_4 (Dense) (None, 128) 65664 ['batch_normalization_5[0][0]'] Y \n", - " \n", - " dense_5 (Dense) (None, 2) 258 ['dense_4[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 88,965,946\n", - "Trainable params: 88,597,626\n", - "Non-trainable params: 368,320\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "from keras.applications.xception import Xception\n", @@ -5692,4150 +1043,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n", - " ) 'stack_0_block0_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n", - " ) 'stack_0_block1_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n", - " ) 'stack_0_block2_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n", - " ) 'stack_0_block3_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_1_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n", - " 'stack_1_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n", - " 'stack_1_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n", - " 'stack_1_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n", - " 'stack_1_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n", - " 'stack_1_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n", - " 'stack_1_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_2_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n", - " 'stack_2_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n", - " 'stack_2_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n", - " 'stack_2_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n", - " 'stack_2_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n", - " 'stack_2_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n", - " 'stack_2_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " a) [0]'] \n", - " \n", - " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_1[0][0]'] \n", - " \n", - " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_3[0][0]'] \n", - " \n", - " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_3_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_5[0][0]'] \n", - " \n", - " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n", - " 'stack_3_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_7[0][0]'] \n", - " \n", - " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n", - " 'stack_3_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_9[0][0]'] \n", - " \n", - " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n", - " 'stack_3_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_11[0][0]'] \n", - " \n", - " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n", - " 'stack_3_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_13[0][0]'] \n", - " \n", - " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n", - " 'stack_3_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_15[0][0]'] \n", - " \n", - " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n", - " 'stack_3_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_17[0][0]'] \n", - " \n", - " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n", - " 'stack_3_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_19[0][0]'] \n", - " \n", - " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n", - " 'stack_3_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_21[0][0]'] \n", - " \n", - " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n", - " 'stack_3_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_23[0][0]'] \n", - " \n", - " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n", - " 'stack_3_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_25[0][0]'] \n", - " \n", - " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n", - " 'stack_3_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_27[0][0]'] \n", - " \n", - " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n", - " 'stack_3_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_29[0][0]'] \n", - " \n", - " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n", - " 'stack_3_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_31[0][0]'] \n", - " \n", - " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n", - " 'stack_3_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_33[0][0]'] \n", - " \n", - " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " onv2D) ) '] \n", - " \n", - " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_35[0][0]'] \n", - " \n", - " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_4_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_37[0][0]'] \n", - " \n", - " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n", - " 'stack_4_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_39[0][0]'] \n", - " \n", - " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n", - " 'stack_4_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_41[0][0]'] \n", - " \n", - " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n", - " 'stack_4_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_43[0][0]'] \n", - " \n", - " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n", - " 'stack_4_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_45[0][0]'] \n", - " \n", - " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n", - " 'stack_4_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_47[0][0]'] \n", - " \n", - " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n", - " 'stack_4_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_49[0][0]'] \n", - " \n", - " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n", - " 'stack_4_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_51[0][0]'] \n", - " \n", - " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n", - " 'stack_4_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_53[0][0]'] \n", - " \n", - " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n", - " 'stack_4_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_55[0][0]'] \n", - " \n", - " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n", - " 'stack_4_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_57[0][0]'] \n", - " \n", - " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n", - " 'stack_4_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_59[0][0]'] \n", - " \n", - " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n", - " 'stack_4_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_61[0][0]'] \n", - " \n", - " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n", - " 'stack_4_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_63[0][0]'] \n", - " \n", - " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n", - " 'stack_4_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_65[0][0]'] \n", - " \n", - " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n", - " 'stack_4_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_67[0][0]'] \n", - " \n", - " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n", - " 'stack_4_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_69[0][0]'] \n", - " \n", - " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n", - " 'stack_4_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_71[0][0]'] \n", - " \n", - " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n", - " 'stack_4_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_73[0][0]'] \n", - " \n", - " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n", - " 'stack_4_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_75[0][0]'] \n", - " \n", - " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n", - " 'stack_4_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_77[0][0]'] \n", - " \n", - " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n", - " 'stack_4_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_79[0][0]'] \n", - " \n", - " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n", - " 'stack_4_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_81[0][0]'] \n", - " \n", - " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_83[0][0]'] \n", - " \n", - " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_5_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_85[0][0]'] \n", - " \n", - " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n", - " 'stack_5_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_87[0][0]'] \n", - " \n", - " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n", - " 'stack_5_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_89[0][0]'] \n", - " \n", - " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n", - " 'stack_5_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_91[0][0]'] \n", - " \n", - " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n", - " 'stack_5_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_93[0][0]'] \n", - " \n", - " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n", - " 'stack_5_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_95[0][0]'] \n", - " \n", - " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n", - " 'stack_5_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_97[0][0]'] \n", - " \n", - " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n", - " 'stack_5_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_99[0][0]'] \n", - " \n", - " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n", - " 'stack_5_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_101[0][0]'] \n", - " \n", - " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n", - " 'stack_5_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_103[0][0]'] \n", - " \n", - " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n", - " 'stack_5_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_105[0][0]'] \n", - " \n", - " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n", - " 'stack_5_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_107[0][0]'] \n", - " \n", - " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n", - " 'stack_5_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_109[0][0]'] \n", - " \n", - " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n", - " 'stack_5_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_111[0][0]'] \n", - " \n", - " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n", - " 'stack_5_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_113[0][0]'] \n", - " \n", - " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n", - " 'stack_5_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_115[0][0]'] \n", - " \n", - " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n", - " 'stack_5_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_117[0][0]'] \n", - " \n", - " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n", - " 'stack_5_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_119[0][0]'] \n", - " \n", - " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n", - " 'stack_5_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_121[0][0]'] \n", - " \n", - " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n", - " 'stack_5_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_123[0][0]'] \n", - " \n", - " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n", - " 'stack_5_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_125[0][0]'] \n", - " \n", - " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n", - " 'stack_5_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_127[0][0]'] \n", - " \n", - " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n", - " 'stack_5_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_129[0][0]'] \n", - " \n", - " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n", - " 'stack_5_block24_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_131[0][0]'] \n", - " \n", - " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n", - " 'stack_5_block25_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_133[0][0]'] \n", - " \n", - " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n", - " 'stack_5_block26_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_135[0][0]'] \n", - " \n", - " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n", - " 'stack_5_block27_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_137[0][0]'] \n", - " \n", - " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n", - " 'stack_5_block28_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_139[0][0]'] \n", - " \n", - " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n", - " 'stack_5_block29_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_141[0][0]'] \n", - " \n", - " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n", - " 'stack_5_block30_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_143[0][0]'] \n", - " \n", - " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n", - " 'stack_5_block31_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_145[0][0]'] \n", - " \n", - " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_147[0][0]'] \n", - " \n", - " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_6_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_149[0][0]'] \n", - " \n", - " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n", - " 'stack_6_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_151[0][0]'] \n", - " \n", - " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n", - " 'stack_6_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_153[0][0]'] \n", - " \n", - " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n", - " 'stack_6_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_155[0][0]'] \n", - " \n", - " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n", - " 'stack_6_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_157[0][0]'] \n", - " \n", - " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n", - " 'stack_6_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_159[0][0]'] \n", - " \n", - " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n", - " 'stack_6_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n", - " \n", - " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", - " \n", - " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", - " \n", - " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", - " 2D) \n", - " \n", - " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", - " \n", - " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 207,618,394\n", - "Trainable params: 206,841,370\n", - "Non-trainable params: 777,024\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras_efficientnet_v2 import EfficientNetV2XL\n", "\n", @@ -9872,1276 +1082,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total layers in the base model: 467\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 475\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1b_add[0][0]'] Y \n", - " 4) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 4) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 4) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 32) 4608 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 192) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 192) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 192) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 32) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 192) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 192) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 192) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 32) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 192) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 192) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 192) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 32) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2d_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 192) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 192) 4800 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 192) 768 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 192) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 192) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 192) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 56) 10752 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 336) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 336) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 336) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 56) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 336) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 336) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 336) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 56) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 336) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 336) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 336) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 56) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3d_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 336) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 336) 3024 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 336) 1344 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 336) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 336) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 336) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 112) 37632 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 672) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 672) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 672) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 112) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 672) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 672) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 672) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 112) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 672) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 672) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 672) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 112) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 672) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 672) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 672) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 112) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 672) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 672) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 672) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 112) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4f_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 672) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 672) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 672) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 160) 107520 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5a_project_bn[0][0]'] Y \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 960) 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 960) 0 ['block5b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 960) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5b_activation[0][0]', Y \n", - " 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 160) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5b_add[0][0]'] Y \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 960) 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 960) 0 ['block5c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 960) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5c_activation[0][0]', Y \n", - " 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 160) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5c_add[0][0]'] Y \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 960) 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 960) 0 ['block5d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 960) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5d_activation[0][0]', Y \n", - " 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 160) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5d_add[0][0]'] Y \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 960) 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 960) 0 ['block5e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 960) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5e_activation[0][0]', Y \n", - " 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 160) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5e_add[0][0]'] Y \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 960) 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 960) 0 ['block5f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 960) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5f_activation[0][0]', Y \n", - " 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 160) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5f_add[0][0]'] Y \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block6a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 960) 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 960) 24000 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 960) 3840 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 960) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 960) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 960) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 272) 261120 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 1632) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 1632) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 1632) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 272) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 1632) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 1632) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 1632) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 272) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 1632) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 1632) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 1632) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 272) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 1632) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 1632) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 1632) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 272) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 1632) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 1632) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 1632) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 272) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 1632) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 1632) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 1632) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 272) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 1632) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 1632) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 1632) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 272) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6h_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 1632) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 14688 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 1632) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 1632) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 448) 731136 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 2688) 1204224 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 2688) 10752 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 2688) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 2688) 24192 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 2688) 10752 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 2688) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 2688) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 2688) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 112) 301168 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 2688) 303744 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 2688) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 448) 1204224 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 448) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 448) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 1792) 802816 ['block7b_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 1792) 7168 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 1792) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 1792) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 918016 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 18,924,506\n", - "Trainable params: 18,797,258\n", - "Non-trainable params: 127,248\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB4 as KENB4\n", "# FUNC\n", @@ -11286,2169 +1229,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T02:31:32.994176700Z", "start_time": "2023-12-28T02:31:27.381088600Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model_1\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " eragePooling2D) \n", - " \n", - " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n", - " \n", - " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L1 (BatchNorm (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-Pooling-L1[0][0]'] Y \n", - " \n", - " FC_C_Avg-Pooling-L2 (BatchNorm (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n", - " alization) \n", - " \n", - " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-Pooling-L2[0][0]'] Y \n", - " \n", - " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from efficientnet.keras import EfficientNetB7 as KENB7\n", "# FUNC\n", @@ -13534,1145 +1322,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total model layers: 11\n", - "Model: \"model\"\n", - "____________________________________________________________________________\n", - " Layer (type) Output Shape Param # Trainable \n", - "============================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3)] 0 Y \n", - " \n", - " lambda (Lambda) (None, 224, 224, 3) 0 Y \n", - " \n", - " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n", - " ) 8 \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n", - "| |\n", - "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n", - "| malization (Normalization) |\n", - "| |\n", - "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n", - "| ntial) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n", - "|| Conv2D) ||\n", - "|| ||\n", - "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n", - "|| orm (LayerNormalization) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n", - "| ambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n", - "| g_block_0 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n", - "|| g_layernorm_0 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n", - "|| g_conv_0 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n", - "| g_block_1 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n", - "|| g_layernorm_1 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n", - "|| g_conv_1 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_3_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_3_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_3_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_3_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_3_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_3_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_3_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_4_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_4_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_4_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_4_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_4_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_4_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_4_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_5_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_5_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_5_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_5_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_5_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_5_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_5_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_6_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_6_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_6_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_6_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_6_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_6_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_6_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_7_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_7_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_7_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_7_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_7_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_7_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_7_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_8_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_8_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_8_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_8_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_8_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_8_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_8_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_9_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_9_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_9_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_9_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_9_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_9_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_9_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_10_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_10_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_10_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_10_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_10_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_10_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_10_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_11_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_11_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_11_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_11_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_11_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_11_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_11_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_12_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_12_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_12_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_12_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_12_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_12_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_12_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_13_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_13_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_13_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_13_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_13_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_13_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_13_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_14_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_14_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_14_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_14_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_14_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_14_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_14_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_15_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_15_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_15_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_15_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_15_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_15_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_15_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_16_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_16_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_16_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_16_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_16_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_16_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_16_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_17_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_17_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_17_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_17_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_17_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_17_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_17_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_18_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_18_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_18_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_18_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_18_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_18_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_18_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_19_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_19_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_19_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_19_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_19_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_19_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_19_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_20_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_20_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_20_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_20_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_20_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_20_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_20_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_21_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_21_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_21_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_21_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_21_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_21_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_21_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_22_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_22_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_22_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_22_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_22_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_22_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_22_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_23_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_23_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_23_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_23_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_23_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_23_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_23_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_24_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_24_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_24_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_24_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_24_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_24_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_24_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_25_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_25_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_25_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_25_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_25_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_25_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_25_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_26_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_26_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_26_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_26_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_26_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_26_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_26_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n", - "| g_block_2 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n", - "|| g_layernorm_2 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n", - "|| g_conv_2 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n", - "| ormalization) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (G (None, 2048) 0 Y \n", - " lobalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1049088 Y \n", - " \n", - " dropout (Dropout) (None, 512) 0 Y \n", - " \n", - " batch_normalization (BatchN (None, 512) 2048 Y \n", - " ormalization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 Y \n", - " \n", - " batch_normalization_1 (Batc (None, 512) 2048 Y \n", - " hNormalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 Y \n", - " \n", - "============================================================================\n", - "Total params: 349,529,730\n", - "Trainable params: 349,527,682\n", - "Non-trainable params: 2,048\n", - "____________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "from keras.applications import ConvNeXtXLarge\n", "from keras.layers import Lambda\n", @@ -14722,10 +1374,10 @@ "metadata": {}, "outputs": [], "source": [ - "from efficientnet.keras import EfficientNetB4 as KENB4\n", + "from efficientnet.keras import EfficientNetB5 as KENB5\n", "# FUNC\n", - "def Eff_B4_NS(freeze_layers):\n", - " base_model = KENB4(input_shape=(\n", + "def Eff_B5_NS(freeze_layers):\n", + " base_model = KENB5(input_shape=(\n", " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", " print('Total layers in the base model: ', len(base_model.layers))\n", " print(f'Freezing {freeze_layers} layers in the base model...')\n", @@ -14747,19 +1399,19 @@ " base_model_FT = GlobalAveragePooling2D(name='FC_INPUT_Avg-Pooling')(base_model.output)\n", " #Dense\n", " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02),\n", + " kernel_regularizer=l2(0.008),\n", " name='FC_C_Dense-L1-512'\n", " )(base_model_FT)\n", " #Dropout\n", - " Dropout_L1 = Dropout(0.1,\n", + " Dropout_L1 = Dropout(0.125,\n", " name='FC_C_Dropout-L1-0.1'\n", " )(Dense_L1)\n", " #BatchNormalization\n", " BatchNorm_L2 = BatchNormalization(name='FC_C_Avg-BatchNormalization-L1'\n", " )(Dropout_L1)\n", " #Dense\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01),\n", + " Dense_L2 = Dense(512, activation='swish',\n", + " kernel_regularizer=l2(0.004),\n", " name='FC_C_Dense-L2-512'\n", " )(BatchNorm_L2)\n", " #BatchNormalization\n", @@ -14792,7 +1444,7 @@ "print('Creating the model...')\n", "# Main\n", "freeze_layers = 0\n", - "model = Eff_B4_NS(freeze_layers)\n", + "model = Eff_B5_NS(freeze_layers)\n", "model.summary(show_trainable=True, expand_nested=True)\n", "print('done.')" ] @@ -14852,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14932,2162 +1584,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], + "outputs": [], "source": [ "import efficientnet.tfkeras\n", "# Configuration\n", @@ -17166,18 +1665,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "for layer in model.layers[-7:]:\n", " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", @@ -17220,1109 +1710,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:23.573633300Z", "start_time": "2023-12-28T02:31:32.468641900Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the model...\n", - "\u001b[0;33m\n", - "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d23-h15_m21_s03]\u001b[0m\u001b[0;36m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0;33mSetup Verbose END.\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d23-h15_m27_s55\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - "256/256 [==============================] - 94s 302ms/step - loss: 8.9813 - accuracy: 0.6421 - val_loss: 7.5518 - val_accuracy: 0.8782\n", - "Epoch 2/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 6.1605 - accuracy: 0.8184 - val_loss: 4.7986 - val_accuracy: 0.8141\n", - "Epoch 3/6\n", - "256/256 [==============================] - 74s 287ms/step - loss: 3.8019 - accuracy: 0.8838 - val_loss: 2.9593 - val_accuracy: 0.9071\n", - "Epoch 4/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 2.4911 - accuracy: 0.9016 - val_loss: 2.1548 - val_accuracy: 0.8942\n", - "Epoch 5/6\n", - "256/256 [==============================] - 73s 285ms/step - loss: 1.8127 - accuracy: 0.9175 - val_loss: 1.6893 - val_accuracy: 0.8846\n", - "Epoch 6/6\n", - "256/256 [==============================] - 73s 284ms/step - loss: 1.5209 - accuracy: 0.9370 - val_loss: 1.5838 - val_accuracy: 0.8974\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-003-0.9071.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m2.9593\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.000000 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.907051\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m2.9592649937\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m897.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m462.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m434.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "256/256 [==============================] - 80s 295ms/step - loss: 2.8681 - accuracy: 0.8806 - val_loss: 2.4747 - val_accuracy: 0.9135\n", - "Epoch 8/12\n", - "256/256 [==============================] - 74s 287ms/step - loss: 2.1103 - accuracy: 0.8884 - val_loss: 1.6685 - val_accuracy: 0.9006\n", - "Epoch 9/12\n", - "256/256 [==============================] - 75s 293ms/step - loss: 1.4251 - accuracy: 0.9038 - val_loss: 1.1568 - val_accuracy: 0.9199\n", - "Epoch 10/12\n", - "256/256 [==============================] - 73s 284ms/step - loss: 1.0017 - accuracy: 0.9233 - val_loss: 0.9066 - val_accuracy: 0.9215\n", - "Epoch 11/12\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.7535 - accuracy: 0.9421 - val_loss: 0.7657 - val_accuracy: 0.9087\n", - "Epoch 12/12\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.6424 - accuracy: 0.9558 - val_loss: 0.7111 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9247.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7111\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.907051 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.924679\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m2.9592649937 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.7111035585\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m543.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m449.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "256/256 [==============================] - 78s 290ms/step - loss: 0.7519 - accuracy: 0.8962 - val_loss: 0.6257 - val_accuracy: 0.9375\n", - "Epoch 14/18\n", - "256/256 [==============================] - 72s 280ms/step - loss: 0.6196 - accuracy: 0.9102 - val_loss: 0.5282 - val_accuracy: 0.9167\n", - "Epoch 15/18\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.4699 - accuracy: 0.9268 - val_loss: 0.4071 - val_accuracy: 0.9407\n", - "Epoch 16/18\n", - "256/256 [==============================] - 72s 280ms/step - loss: 0.3683 - accuracy: 0.9404 - val_loss: 0.3678 - val_accuracy: 0.9391\n", - "Epoch 17/18\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.2945 - accuracy: 0.9490 - val_loss: 0.4586 - val_accuracy: 0.9087\n", - "Epoch 18/18\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.2461 - accuracy: 0.9580 - val_loss: 0.3428 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-015-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.924679 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.940705\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.7111035585 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4071271718\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m538.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m444.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.4613 - accuracy: 0.9072 - val_loss: 0.3596 - val_accuracy: 0.9375\n", - "Epoch 20/24\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.4214 - accuracy: 0.9070 - val_loss: 0.5467 - val_accuracy: 0.9054\n", - "Epoch 21/24\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.3277 - accuracy: 0.9272 - val_loss: 0.2573 - val_accuracy: 0.9423\n", - "Epoch 22/24\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.2878 - accuracy: 0.9353 - val_loss: 0.2760 - val_accuracy: 0.9439\n", - "Epoch 23/24\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1992 - accuracy: 0.9561 - val_loss: 0.2420 - val_accuracy: 0.9439\n", - "Epoch 24/24\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1700 - accuracy: 0.9692 - val_loss: 0.2573 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-022-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2760\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.940705 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.943910\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.4071271718 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2759856582\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "256/256 [==============================] - 81s 296ms/step - loss: 0.3227 - accuracy: 0.9119 - val_loss: 0.2258 - val_accuracy: 0.9487\n", - "Epoch 26/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.3038 - accuracy: 0.9199 - val_loss: 0.2747 - val_accuracy: 0.8990\n", - "Epoch 27/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2345 - accuracy: 0.9358 - val_loss: 0.2601 - val_accuracy: 0.9247\n", - "Epoch 28/30\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1907 - accuracy: 0.9468 - val_loss: 0.1984 - val_accuracy: 0.9551\n", - "Epoch 29/30\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1505 - accuracy: 0.9604 - val_loss: 0.1831 - val_accuracy: 0.9519\n", - "Epoch 30/30\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1158 - accuracy: 0.9722 - val_loss: 0.1972 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-028-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1984\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.943910 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.955128\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2759856582 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1984292269\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "256/256 [==============================] - 81s 298ms/step - loss: 0.2811 - accuracy: 0.9155 - val_loss: 0.2328 - val_accuracy: 0.9215\n", - "Epoch 32/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2584 - accuracy: 0.9199 - val_loss: 0.2153 - val_accuracy: 0.9167\n", - "Epoch 33/36\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1972 - accuracy: 0.9470 - val_loss: 0.2884 - val_accuracy: 0.9599\n", - "Epoch 34/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1848 - accuracy: 0.9500 - val_loss: 0.1846 - val_accuracy: 0.9471\n", - "Epoch 35/36\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1391 - accuracy: 0.9648 - val_loss: 0.1701 - val_accuracy: 0.9519\n", - "Epoch 36/36\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1067 - accuracy: 0.9727 - val_loss: 0.1741 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2884\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.955128 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.959936\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1984292269. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m553.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.2490 - accuracy: 0.9263 - val_loss: 0.1767 - val_accuracy: 0.9567\n", - "Epoch 38/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2297 - accuracy: 0.9290 - val_loss: 0.2202 - val_accuracy: 0.9359\n", - "Epoch 39/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.2021 - accuracy: 0.9382 - val_loss: 0.1495 - val_accuracy: 0.9567\n", - "Epoch 40/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1596 - accuracy: 0.9570 - val_loss: 0.1926 - val_accuracy: 0.9567\n", - "Epoch 41/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1204 - accuracy: 0.9685 - val_loss: 0.1584 - val_accuracy: 0.9487\n", - "Epoch 42/42\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0946 - accuracy: 0.9758 - val_loss: 0.1562 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1767\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9599359035. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1984292269 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1766962409\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "256/256 [==============================] - 81s 295ms/step - loss: 0.2312 - accuracy: 0.9297 - val_loss: 0.1708 - val_accuracy: 0.9647\n", - "Epoch 44/48\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.2093 - accuracy: 0.9390 - val_loss: 0.1545 - val_accuracy: 0.9535\n", - "Epoch 45/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1908 - accuracy: 0.9436 - val_loss: 0.2027 - val_accuracy: 0.9247\n", - "Epoch 46/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1480 - accuracy: 0.9600 - val_loss: 0.1526 - val_accuracy: 0.9599\n", - "Epoch 47/48\n", - "256/256 [==============================] - 73s 287ms/step - loss: 0.1102 - accuracy: 0.9729 - val_loss: 0.1835 - val_accuracy: 0.9551\n", - "Epoch 48/48\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0904 - accuracy: 0.9758 - val_loss: 0.1743 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9647.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1708\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m 0.959936 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m 0.964744\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1766962409 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1707910001\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m553.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2180 - accuracy: 0.9312 - val_loss: 0.1975 - val_accuracy: 0.9615\n", - "Epoch 50/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.2168 - accuracy: 0.9275 - val_loss: 0.1622 - val_accuracy: 0.9599\n", - "Epoch 51/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1757 - accuracy: 0.9480 - val_loss: 0.1945 - val_accuracy: 0.9471\n", - "Epoch 52/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1616 - accuracy: 0.9519 - val_loss: 0.1737 - val_accuracy: 0.9439\n", - "Epoch 53/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1107 - accuracy: 0.9707 - val_loss: 0.2138 - val_accuracy: 0.9551\n", - "Epoch 54/54\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0950 - accuracy: 0.9761 - val_loss: 0.2240 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-049-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1975\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1707910001. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2157 - accuracy: 0.9365 - val_loss: 0.1587 - val_accuracy: 0.9583\n", - "Epoch 56/60\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.2221 - accuracy: 0.9285 - val_loss: 0.2882 - val_accuracy: 0.9375\n", - "Epoch 57/60\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1853 - accuracy: 0.9402 - val_loss: 0.2852 - val_accuracy: 0.9343\n", - "Epoch 58/60\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.1912 - val_accuracy: 0.9471\n", - "Epoch 59/60\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.1129 - accuracy: 0.9702 - val_loss: 0.1927 - val_accuracy: 0.9487\n", - "Epoch 60/60\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0713 - accuracy: 0.9817 - val_loss: 0.2114 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-055-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1587\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1707910001 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1586984992\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m451.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "256/256 [==============================] - 80s 293ms/step - loss: 0.2160 - accuracy: 0.9370 - val_loss: 0.2318 - val_accuracy: 0.9183\n", - "Epoch 62/66\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.2069 - accuracy: 0.9297 - val_loss: 0.2511 - val_accuracy: 0.8766\n", - "Epoch 63/66\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1770 - accuracy: 0.9417 - val_loss: 0.1905 - val_accuracy: 0.9503\n", - "Epoch 64/66\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1392 - accuracy: 0.9619 - val_loss: 0.1880 - val_accuracy: 0.9503\n", - "Epoch 65/66\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1214 - accuracy: 0.9673 - val_loss: 0.2390 - val_accuracy: 0.9439\n", - "Epoch 66/66\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0832 - accuracy: 0.9807 - val_loss: 0.1751 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-066-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1752\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m450.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2145 - accuracy: 0.9341 - val_loss: 0.2676 - val_accuracy: 0.9471\n", - "Epoch 68/72\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1984 - accuracy: 0.9331 - val_loss: 0.1683 - val_accuracy: 0.9215\n", - "Epoch 69/72\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1689 - accuracy: 0.9465 - val_loss: 0.1710 - val_accuracy: 0.9583\n", - "Epoch 70/72\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1663 - val_accuracy: 0.9519\n", - "Epoch 71/72\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0932 - accuracy: 0.9739 - val_loss: 0.1488 - val_accuracy: 0.9535\n", - "Epoch 72/72\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0588 - accuracy: 0.9856 - val_loss: 0.1736 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-069-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1709\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m454.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.2089 - accuracy: 0.9321 - val_loss: 0.1373 - val_accuracy: 0.9471\n", - "Epoch 74/78\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.2021 - accuracy: 0.9385 - val_loss: 0.1914 - val_accuracy: 0.9535\n", - "Epoch 75/78\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1662 - accuracy: 0.9519 - val_loss: 0.3171 - val_accuracy: 0.9487\n", - "Epoch 76/78\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1388 - accuracy: 0.9597 - val_loss: 0.1874 - val_accuracy: 0.9359\n", - "Epoch 77/78\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0989 - accuracy: 0.9734 - val_loss: 0.2316 - val_accuracy: 0.9423\n", - "Epoch 78/78\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.0733 - accuracy: 0.9824 - val_loss: 0.2070 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-074-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1914\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.2117 - accuracy: 0.9314 - val_loss: 0.1623 - val_accuracy: 0.9551\n", - "Epoch 80/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1814 - accuracy: 0.9426 - val_loss: 0.1552 - val_accuracy: 0.9487\n", - "Epoch 81/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1780 - accuracy: 0.9436 - val_loss: 0.1850 - val_accuracy: 0.9471\n", - "Epoch 82/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1360 - accuracy: 0.9580 - val_loss: 0.2193 - val_accuracy: 0.9503\n", - "Epoch 83/84\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.1582 - val_accuracy: 0.9535\n", - "Epoch 84/84\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0647 - accuracy: 0.9827 - val_loss: 0.1968 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1623\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m560.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.2043 - accuracy: 0.9319 - val_loss: 0.2282 - val_accuracy: 0.9311\n", - "Epoch 86/90\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1907 - accuracy: 0.9375 - val_loss: 0.2106 - val_accuracy: 0.9471\n", - "Epoch 87/90\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1522 - accuracy: 0.9514 - val_loss: 0.2437 - val_accuracy: 0.9327\n", - "Epoch 88/90\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1215 - accuracy: 0.9653 - val_loss: 0.3168 - val_accuracy: 0.9183\n", - "Epoch 89/90\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.0905 - accuracy: 0.9753 - val_loss: 0.2246 - val_accuracy: 0.9503\n", - "Epoch 90/90\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.0612 - accuracy: 0.9868 - val_loss: 0.1948 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1948\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m458.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "256/256 [==============================] - 83s 306ms/step - loss: 0.1922 - accuracy: 0.9399 - val_loss: 0.1989 - val_accuracy: 0.9487\n", - "Epoch 92/96\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1595 - accuracy: 0.9524 - val_loss: 0.3425 - val_accuracy: 0.9295\n", - "Epoch 93/96\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1470 - accuracy: 0.9570 - val_loss: 0.2482 - val_accuracy: 0.9215\n", - "Epoch 94/96\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1097 - accuracy: 0.9692 - val_loss: 0.2973 - val_accuracy: 0.9327\n", - "Epoch 95/96\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0764 - accuracy: 0.9773 - val_loss: 0.3116 - val_accuracy: 0.9279\n", - "Epoch 96/96\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0454 - accuracy: 0.9890 - val_loss: 0.2784 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1989\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1586984992. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m457.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "256/256 [==============================] - 81s 296ms/step - loss: 0.1904 - accuracy: 0.9355 - val_loss: 0.1833 - val_accuracy: 0.9519\n", - "Epoch 98/102\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1852 - accuracy: 0.9448 - val_loss: 0.2454 - val_accuracy: 0.9391\n", - "Epoch 99/102\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1449 - accuracy: 0.9514 - val_loss: 0.4665 - val_accuracy: 0.9231\n", - "Epoch 100/102\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1387 - accuracy: 0.9629 - val_loss: 0.1466 - val_accuracy: 0.9551\n", - "Epoch 101/102\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0675 - accuracy: 0.9819 - val_loss: 0.3165 - val_accuracy: 0.9391\n", - "Epoch 102/102\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0555 - accuracy: 0.9871 - val_loss: 0.3152 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-100-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1467\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1586984992 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1466508806\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "256/256 [==============================] - 80s 296ms/step - loss: 0.1963 - accuracy: 0.9402 - val_loss: 0.3182 - val_accuracy: 0.9103\n", - "Epoch 104/108\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1846 - accuracy: 0.9417 - val_loss: 0.2220 - val_accuracy: 0.9327\n", - "Epoch 105/108\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1474 - accuracy: 0.9563 - val_loss: 0.2642 - val_accuracy: 0.9327\n", - "Epoch 106/108\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1230 - accuracy: 0.9666 - val_loss: 0.5477 - val_accuracy: 0.8926\n", - "Epoch 107/108\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.0984 - accuracy: 0.9749 - val_loss: 0.1391 - val_accuracy: 0.9631\n", - "Epoch 108/108\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0597 - accuracy: 0.9863 - val_loss: 0.3226 - val_accuracy: 0.9215\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-107-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1466508806 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1390501112\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.1888 - accuracy: 0.9426 - val_loss: 0.1798 - val_accuracy: 0.9423\n", - "Epoch 110/114\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1749 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9551\n", - "Epoch 111/114\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1898 - val_accuracy: 0.9551\n", - "Epoch 112/114\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1019 - accuracy: 0.9714 - val_loss: 0.1749 - val_accuracy: 0.9567\n", - "Epoch 113/114\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0842 - accuracy: 0.9778 - val_loss: 0.2070 - val_accuracy: 0.9551\n", - "Epoch 114/114\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0572 - accuracy: 0.9851 - val_loss: 0.2040 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-112-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1748\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m557.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m452.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "256/256 [==============================] - 82s 303ms/step - loss: 0.1987 - accuracy: 0.9419 - val_loss: 0.2110 - val_accuracy: 0.9519\n", - "Epoch 116/120\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1816 - accuracy: 0.9480 - val_loss: 0.1721 - val_accuracy: 0.9551\n", - "Epoch 117/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1515 - accuracy: 0.9561 - val_loss: 0.1748 - val_accuracy: 0.9535\n", - "Epoch 118/120\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1192 - accuracy: 0.9675 - val_loss: 0.1692 - val_accuracy: 0.9519\n", - "Epoch 119/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0736 - accuracy: 0.9834 - val_loss: 0.1637 - val_accuracy: 0.9487\n", - "Epoch 120/120\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0594 - accuracy: 0.9878 - val_loss: 0.1796 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-116-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1721\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m456.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "256/256 [==============================] - 81s 297ms/step - loss: 0.1849 - accuracy: 0.9373 - val_loss: 0.2483 - val_accuracy: 0.9151\n", - "Epoch 122/126\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1633 - accuracy: 0.9548 - val_loss: 0.2154 - val_accuracy: 0.9327\n", - "Epoch 123/126\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.4623 - val_accuracy: 0.9151\n", - "Epoch 124/126\n", - "256/256 [==============================] - 74s 286ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.6142 - val_accuracy: 0.8349\n", - "Epoch 125/126\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0777 - accuracy: 0.9785 - val_loss: 0.1497 - val_accuracy: 0.9519\n", - "Epoch 126/126\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0460 - accuracy: 0.9895 - val_loss: 0.2228 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-125-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1497\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m556.72 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m451.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "256/256 [==============================] - 80s 297ms/step - loss: 0.1778 - accuracy: 0.9446 - val_loss: 0.1662 - val_accuracy: 0.9599\n", - "Epoch 128/132\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1505 - accuracy: 0.9541 - val_loss: 0.1411 - val_accuracy: 0.9615\n", - "Epoch 129/132\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.1151 - accuracy: 0.9666 - val_loss: 0.1438 - val_accuracy: 0.9599\n", - "Epoch 130/132\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0878 - accuracy: 0.9753 - val_loss: 0.1397 - val_accuracy: 0.9599\n", - "Epoch 131/132\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.0624 - accuracy: 0.9814 - val_loss: 0.1991 - val_accuracy: 0.9583\n", - "Epoch 132/132\n", - "256/256 [==============================] - 73s 283ms/step - loss: 0.0397 - accuracy: 0.9905 - val_loss: 0.1805 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1411\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m566.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m449.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "256/256 [==============================] - 79s 291ms/step - loss: 0.1854 - accuracy: 0.9392 - val_loss: 0.1833 - val_accuracy: 0.9375\n", - "Epoch 134/138\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1595 - accuracy: 0.9458 - val_loss: 0.2810 - val_accuracy: 0.9359\n", - "Epoch 135/138\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.1267 - accuracy: 0.9612 - val_loss: 0.2568 - val_accuracy: 0.9327\n", - "Epoch 136/138\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.1031 - accuracy: 0.9712 - val_loss: 0.2219 - val_accuracy: 0.9471\n", - "Epoch 137/138\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.0668 - accuracy: 0.9841 - val_loss: 0.2431 - val_accuracy: 0.9519\n", - "Epoch 138/138\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.0502 - accuracy: 0.9856 - val_loss: 0.2001 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-138-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2001\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "256/256 [==============================] - 79s 292ms/step - loss: 0.1864 - accuracy: 0.9465 - val_loss: 0.1530 - val_accuracy: 0.9535\n", - "Epoch 140/144\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.1584 - accuracy: 0.9531 - val_loss: 0.2170 - val_accuracy: 0.9519\n", - "Epoch 141/144\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.1250 - accuracy: 0.9653 - val_loss: 0.3537 - val_accuracy: 0.9279\n", - "Epoch 142/144\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0960 - accuracy: 0.9714 - val_loss: 0.3708 - val_accuracy: 0.9263\n", - "Epoch 143/144\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0655 - accuracy: 0.9849 - val_loss: 0.1498 - val_accuracy: 0.9535\n", - "Epoch 144/144\n", - "256/256 [==============================] - 73s 284ms/step - loss: 0.0452 - accuracy: 0.9915 - val_loss: 0.1787 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-139-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1529\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m548.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "256/256 [==============================] - 80s 292ms/step - loss: 0.1591 - accuracy: 0.9497 - val_loss: 0.2199 - val_accuracy: 0.9519\n", - "Epoch 146/150\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.1562 - accuracy: 0.9509 - val_loss: 0.1797 - val_accuracy: 0.9551\n", - "Epoch 147/150\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1233 - accuracy: 0.9641 - val_loss: 0.1523 - val_accuracy: 0.9583\n", - "Epoch 148/150\n", - "256/256 [==============================] - 72s 281ms/step - loss: 0.0972 - accuracy: 0.9739 - val_loss: 0.2174 - val_accuracy: 0.9455\n", - "Epoch 149/150\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0707 - accuracy: 0.9814 - val_loss: 0.2697 - val_accuracy: 0.9471\n", - "Epoch 150/150\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.0596 - accuracy: 0.9866 - val_loss: 0.2078 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-147-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1523\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m563.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "256/256 [==============================] - 80s 293ms/step - loss: 0.1757 - accuracy: 0.9404 - val_loss: 0.1719 - val_accuracy: 0.9487\n", - "Epoch 152/156\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1540 - accuracy: 0.9568 - val_loss: 0.3150 - val_accuracy: 0.9391\n", - "Epoch 153/156\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1258 - accuracy: 0.9622 - val_loss: 0.2205 - val_accuracy: 0.9375\n", - "Epoch 154/156\n", - "256/256 [==============================] - 73s 285ms/step - loss: 0.0939 - accuracy: 0.9734 - val_loss: 0.3004 - val_accuracy: 0.9343\n", - "Epoch 155/156\n", - "256/256 [==============================] - 74s 291ms/step - loss: 0.0705 - accuracy: 0.9832 - val_loss: 0.3185 - val_accuracy: 0.9215\n", - "Epoch 156/156\n", - "256/256 [==============================] - 72s 283ms/step - loss: 0.0448 - accuracy: 0.9900 - val_loss: 0.3741 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-151-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1719\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m448.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "256/256 [==============================] - 81s 298ms/step - loss: 0.1679 - accuracy: 0.9468 - val_loss: 0.1615 - val_accuracy: 0.9599\n", - "Epoch 158/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1576 - accuracy: 0.9490 - val_loss: 0.3046 - val_accuracy: 0.9535\n", - "Epoch 159/162\n", - "256/256 [==============================] - 75s 291ms/step - loss: 0.1247 - accuracy: 0.9626 - val_loss: 0.4555 - val_accuracy: 0.8958\n", - "Epoch 160/162\n", - "256/256 [==============================] - 74s 288ms/step - loss: 0.0983 - accuracy: 0.9705 - val_loss: 0.3122 - val_accuracy: 0.9487\n", - "Epoch 161/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0695 - accuracy: 0.9824 - val_loss: 0.2654 - val_accuracy: 0.9423\n", - "Epoch 162/162\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0435 - accuracy: 0.9900 - val_loss: 0.3325 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1615}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3325\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.1753 - accuracy: 0.9492 - val_loss: 0.1844 - val_accuracy: 0.9327\n", - "Epoch 164/168\n", - "256/256 [==============================] - 76s 294ms/step - loss: 0.1455 - accuracy: 0.9521 - val_loss: 0.1986 - val_accuracy: 0.9471\n", - "Epoch 165/168\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1178 - accuracy: 0.9663 - val_loss: 0.1527 - val_accuracy: 0.9535\n", - "Epoch 166/168\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0860 - accuracy: 0.9773 - val_loss: 0.2426 - val_accuracy: 0.9247\n", - "Epoch 167/168\n", - "256/256 [==============================] - 76s 295ms/step - loss: 0.0597 - accuracy: 0.9846 - val_loss: 0.2100 - val_accuracy: 0.9599\n", - "Epoch 168/168\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0460 - accuracy: 0.9900 - val_loss: 0.2788 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9599}, \u001b[0m\u001b[0;33mloss{0.1527}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2788\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m586.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m458.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "256/256 [==============================] - 81s 299ms/step - loss: 0.1764 - accuracy: 0.9473 - val_loss: 0.1456 - val_accuracy: 0.9535\n", - "Epoch 170/174\n", - "256/256 [==============================] - 73s 286ms/step - loss: 0.1427 - accuracy: 0.9553 - val_loss: 0.2146 - val_accuracy: 0.9487\n", - "Epoch 171/174\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1340 - accuracy: 0.9644 - val_loss: 0.1950 - val_accuracy: 0.9439\n", - "Epoch 172/174\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0793 - accuracy: 0.9788 - val_loss: 0.1637 - val_accuracy: 0.9455\n", - "Epoch 173/174\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0587 - accuracy: 0.9856 - val_loss: 0.2221 - val_accuracy: 0.9503\n", - "Epoch 174/174\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.0444 - accuracy: 0.9897 - val_loss: 0.2128 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1456}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2128\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m579.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m454.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "256/256 [==============================] - 82s 299ms/step - loss: 0.1776 - accuracy: 0.9434 - val_loss: 0.1638 - val_accuracy: 0.9551\n", - "Epoch 176/180\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1480 - accuracy: 0.9539 - val_loss: 0.1788 - val_accuracy: 0.9471\n", - "Epoch 177/180\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.1091 - accuracy: 0.9668 - val_loss: 0.4104 - val_accuracy: 0.9311\n", - "Epoch 178/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0808 - accuracy: 0.9758 - val_loss: 0.3871 - val_accuracy: 0.9215\n", - "Epoch 179/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0559 - accuracy: 0.9854 - val_loss: 0.2503 - val_accuracy: 0.9423\n", - "Epoch 180/180\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0421 - accuracy: 0.9890 - val_loss: 0.2214 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1638}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2214\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m580.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "256/256 [==============================] - 82s 301ms/step - loss: 0.1767 - accuracy: 0.9487 - val_loss: 0.1722 - val_accuracy: 0.9503\n", - "Epoch 182/186\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1454 - accuracy: 0.9568 - val_loss: 0.1515 - val_accuracy: 0.9519\n", - "Epoch 183/186\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.1120 - accuracy: 0.9683 - val_loss: 0.2423 - val_accuracy: 0.9391\n", - "Epoch 184/186\n", - "256/256 [==============================] - 74s 287ms/step - loss: 0.0786 - accuracy: 0.9795 - val_loss: 0.2353 - val_accuracy: 0.9487\n", - "Epoch 185/186\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0570 - accuracy: 0.9854 - val_loss: 0.2281 - val_accuracy: 0.9503\n", - "Epoch 186/186\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0359 - accuracy: 0.9910 - val_loss: 0.2425 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1515}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2424\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m587.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m455.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "256/256 [==============================] - 82s 300ms/step - loss: 0.1724 - accuracy: 0.9485 - val_loss: 0.1654 - val_accuracy: 0.9551\n", - "Epoch 188/192\n", - "256/256 [==============================] - 75s 292ms/step - loss: 0.1345 - accuracy: 0.9614 - val_loss: 0.2014 - val_accuracy: 0.9503\n", - "Epoch 189/192\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.1049 - accuracy: 0.9692 - val_loss: 0.2510 - val_accuracy: 0.9567\n", - "Epoch 190/192\n", - "256/256 [==============================] - 75s 290ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.2297 - val_accuracy: 0.9455\n", - "Epoch 191/192\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.0492 - accuracy: 0.9863 - val_loss: 0.1745 - val_accuracy: 0.9519\n", - "Epoch 192/192\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0297 - accuracy: 0.9937 - val_loss: 0.2283 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.1654}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2283\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m594.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m457.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m137.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "256/256 [==============================] - 83s 305ms/step - loss: 0.1587 - accuracy: 0.9551 - val_loss: 0.1971 - val_accuracy: 0.9583\n", - "Epoch 194/198\n", - "256/256 [==============================] - 74s 290ms/step - loss: 0.1452 - accuracy: 0.9531 - val_loss: 0.1756 - val_accuracy: 0.9375\n", - "Epoch 195/198\n", - "256/256 [==============================] - 74s 289ms/step - loss: 0.0976 - accuracy: 0.9741 - val_loss: 0.2353 - val_accuracy: 0.9231\n", - "Epoch 196/198\n", - "256/256 [==============================] - 75s 293ms/step - loss: 0.0591 - accuracy: 0.9832 - val_loss: 0.1904 - val_accuracy: 0.9471\n", - "Epoch 197/198\n", - "256/256 [==============================] - 76s 298ms/step - loss: 0.0411 - accuracy: 0.9900 - val_loss: 0.2127 - val_accuracy: 0.9503\n", - "Epoch 198/198\n", - "256/256 [==============================] - 75s 294ms/step - loss: 0.0302 - accuracy: 0.9932 - val_loss: 0.2056 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1756}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9647}, loss{0.1391}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2056\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436142. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1390501112. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m591.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m460.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m130.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\n", - "KeyboardInterrupt.\n", - "Training done.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import gc\n", "# Garbage Collection (memory)\n", @@ -18995,7 +2390,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19014,7 +2409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19034,75 +2429,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-12-28T07:04:52.565658900Z", "start_time": "2023-12-28T07:04:51.032425100Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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yJaqrq2OcxY8//jiOOOIIXHDBBSnnffrpp2PRokV4+OGH9ec6OjqwZcuWhDpWxPyECvgegKxZs0bPa/3iF7+IM888E+eddx7eeeedrE9YPp8PALK6AF1yySW45JJLMD09jXfeeQf33XcfHnnkEZx33nno6OhICASyobm5OaeidW+88Qauu+46vPXWWzEWQiASrLhcLt06e+ihh6YcR7tpveyyy1JuMzU1hZqamrTz+drXvoYLL7wQPM+juroahxxyCMxmc8rtx8bGEAgEsHr16oTnDj74YKiqioGBARxyyCFp9xtPX18fAKQc99lnn82rYFlVVVXGwDWeJUuWJDxWU1MDj8eT0zj5jnnppZfCYDBgx44daGpqinlu9+7d2LFjBxoaGpLuRyv219fXB57nsWLFipjnkx3ffOnr68OqVasSgg3Nmq19pldeeSX+8pe/4JxzzkFzczPOPPNMXHTRRTEdPX784x/jU5/6FA466CAceuihOPvss3HppZfi8MMPL9p8CYIgZguKdyje0aB4Z4ZKiXceeughLFu2DGazGV1dXQCAFStWwGaz4eGHH9bbtXd3d2PRokUxIlo83d3d4Hkea9euzXr/2bBs2bKEx4LBIH7xi1/g3nvvxdDQEBhj+nPRtaW6u7vxmc98Ju34PM/j85//PO688069OPfDDz8Mi8Wi10Qi5jckxhD47Gc/iyuuuAK7du3K+iTa0dEBAFi5cmXW+6mqqsIZZ5yBM844A0ajEffffz/eeecdnHTSSSmDovgcSo1oVToT3d3dOO2007BmzRrccsstWLx4MUwmE/75z3/i1ltvzWplR0Pb9qabbkrZklFbRUvHqlWrcPrpp2e937nGmjVr0NbWhnA4nHUQGb/yoBF9kcv1e5LNmBqf/vSn8cADD+D2229PKJimqioOO+ww3HLLLUnHW7x4cdLHZ5PGxka0tbXh2WefxTPPPINnnnkG9957L774xS/qxX5PPPFEdHd34+9//zuee+45/OEPf8Ctt96Ku+66q2K7GRAEQeQLxTsU7xQbinfyY3p6Gk8//TRCoRBWrVqV8PwjjzyCn/3sZwW5WnIhl9/ff/zHf+Dee+/FN7/5TRx33HFwuVzgOA4XX3xxTr8xjS9+8Yu46aab8OSTT+KSSy7BI488gk984hNwuVw5j0XMPUiMIRAMBgEgbaeAaBRFwSOPPAKbzYbjjz8+r30eddRRuP/++7F3714A0FdW4rscaCsZhfD0009DFEU89dRTMSsHmtVSQ1P3Ozo6UgZd2jZVVVVlDS4aGhpgs9n0SvfRdHZ2gud5/QKZy4VLs6amGre+vj6vNn7nnXce3nrrLTz++OMx9tNCif6eRLecLMb35D/+4z+wcuVKXHvttXC5XHqhNiDyube3t+O0005Le3xbW1uhqiq6u7tjAv1kxzdfWltbsWXLFqiqGuOO6ezs1J/XMJlMOO+883DeeedBVVVceeWV+N3vfocf/ehH+ne8trYWl19+OS6//HL4fD6ceOKJuP7660mMIQhi3kHxTgSKdxLHpXgnQrninSeeeAKhUAh33nkn6uvrY57buXMnrrnmGrzxxhs4/vjjsWLFCjz77LNwu90p3TErVqyAqqrYvn17SvEQiBzX+N9eOBzWf5/Z8Nhjj+Gyyy7DzTffrD8WCoUSxl2xYoUu5qbj0EMPxfr16/Hwww+jpaUF/f39uOOOO7KeDzG3oZoxBxCatTAaSZLwwAMPwGq1ZmXtUxQFV111FXbs2IGrrroKVVVVKbcNBAJ46623kj73zDPPAJixM2oX/c2bN8fs6/e//33GOWVCWy2ItxHee++9MdudeeaZcDqd+MUvfoFQKBTznPbaDRs2YMWKFfjVr36lW5ejGRsbK3i+yRAEAWeeeSb+/ve/x7TOGxkZwSOPPILjjz9e/yy0YCKb9p0LFy7EunXrcP/998ds39HRgeeeew7nnntuXvP9+te/joULF+K//uu/sGvXroTnR0dH8dOf/jTncZN9T/x+f9K2zvnwox/9CN/5znfw/e9/P6YV6EUXXYShoSHcfffdCa8JBoPw+/0AgHPOOQcA8Otf/zpmm9tuu60o8wOAc889F/v27cOf//xn/TFZlnHHHXfA4XDoLTknJiZiXsfzvJ5+pLWnjN/G4XBg5cqVCe0rCYIg5hIU71C8Ew/FO7HMdrzz0EMPYfny5fj617+Oz372szH/fec734HD4dDrqHzmM58BYww33HBDwjja9/X8888Hz/P48Y9/nOBOif49rFixIuaYAsDvf//7lM6YZAiCkOA4uuOOOxLG+MxnPoP29nb87W9/SzlvjUsvvRTPPfccbrvtNtTV1enHl5j/kDPmAOKKK67A9PQ0TjzxRDQ3N2Pfvn14+OGH0dnZiZtvvjnBbjo1NYWHHnoIQCTQ6OrqwhNPPIHu7m5cfPHF+MlPfpJ2f4FAABs3bsSxxx6Ls88+G4sXL8bk5CSefPJJvPbaazj//POxfv16AMAhhxyCY489Ft///vd15ftPf/oTZFku+H2feeaZukPgiiuugM/nw913343GxsYYJbyqqgq33norvvKVr+Doo4/G5z73OdTU1KC9vR2BQAD3338/eJ7HH/7wB5xzzjk45JBDcPnll6O5uRlDQ0N4+eWXUVVVhaeffrrgOSfjpz/9KZ5//nkcf/zxuPLKK2EwGPC73/0Ooijif/7nf/Tt1q1bB0EQcOONN2JqagpmsxmnnnoqGhsbk45700034ZxzzsFxxx2HL3/5ywgGg7jjjjvgcrlw/fXX5zXXmpoa/O1vf8O5556LdevW4Qtf+AI2bNgAAPjwww/x6KOP4rjjjst53DPPPBNLlizBl7/8ZXz3u9+FIAi455570NDQgP7+/rzmGs9NN92Eqakp/H//3/8Hp9OJL3zhC7j00kvxl7/8BV//+tfx8ssvY9OmTVAUBZ2dnfjLX/6CZ599FkcddRTWrVuHSy65BL/97W8xNTWFjRs34sUXX9RzobPlxRdfTAiQgUiw8bWvfQ2/+93v8KUvfQkffPABli5disceewxvvPEGbrvtNr2uwVe+8hW43W6ceuqpaGlpQV9fH+644w6sW7dOry+zdu1anHzyydiwYQNqa2vx/vvv47HHHsM3vvGNwg8kQRDELEHxDsU7yaB4J5bZineGh4fx8ssvJxT/1TCbzTjrrLPw17/+Fb/+9a9xyimn4NJLL8Wvf/1r7N69G2effTZUVcVrr72GU045Bd/4xjewcuVK/PCHP8RPfvITnHDCCfj0pz8Ns9mM9957D4sWLdLTsb7yla/g61//Oj7zmc/gjDPOQHt7O5599tkEd046PvGJT+DBBx+Ey+XC2rVr8dZbb+GFF15IaNf+3e9+F4899hguvPBC/L//9/+wYcMGuN1uPPXUU7jrrrtwxBFH6Nt+7nOfw9VXX42//e1v+Pd//3cYjcas50PMcWajhRMxOzz66KPs9NNPZwsWLGAGg4HV1NSw008/nf39739P2FZrp6j953A42KpVq9gXvvAF9txzz2W1P0mS2N13383OP/981traysxmM7PZbGz9+vXspptuYqIoxmzf3d3NTj/9dGY2m9mCBQvYD37wA/b8888nbfWYqg1lqlaPTz31FDv88MOZxWJhS5cuZTfeeCO75557kraXfOqpp9jGjRuZ1WplVVVV7JhjjmGPPvpozDYfffQR+/SnP83q6uqY2Wxmra2t7KKLLmIvvvhi2mOSTetiDSRpv/fhhx+ys846izkcDmaz2dgpp5zC3nzzzYTX3n333Wz58uVMEISs2j6+8MILbNOmTfp7Pu+889j27dtjtsml1aPG8PAw+9a3vsUOOuggZrFYmM1mYxs2bGA/+9nP2NTUlL5da2tr0hbLJ510UkJrvw8++IB97GMfYyaTiS1ZsoTdcsstKVs9ZjNmdKtHDUVR2CWXXMIMBgN78sknGWORFow33ngjO+SQQ5jZbGY1NTVsw4YN7IYbboh5L8FgkF111VWsrq6O2e12dt5557GBgYGcWlun+u/BBx9kjDE2MjLCLr/8clZfX89MJhM77LDD2L333hsz1mOPPcbOPPNM1tjYqB+rK664gu3du1ff5qc//Sk75phjWHV1NbNarWzNmjXsZz/7GQuHw2nnSRAEUclQvEPxTioo3pn9eOfmm29mANJ+h+677z4GQP/NyrLMbrrpJrZmzRpmMplYQ0MDO+eccxLax99zzz1s/fr1+rxPOukk9vzzz8e83//+7/9m9fX1zGazsbPOOot1dXWlbG0dfaw0PB6PHoM5HA521llnsc7OzoQxGGNsYmKCfeMb32DNzc3MZDKxlpYWdtlll7Hx8fGEcc8991wGIOn3nJi/cIwlqexEEARBEARBEARBEETJueCCC7B169ac3dTE3IZqxhAEQRAEQRAEQRDELLB371784x//wKWXXjrbUyHKDNWMIQiCIAiCIAiCIIgy0tvbizfeeAN/+MMfYDQaccUVV8z2lIgyQ84YgiAIgiAIgiAIgigjr776Ki699FL09vbi/vvvR1NT02xPiSgzVDOGIAiCIAiCIAiCIAiijJAzhiAIgiAIgiAIgiAIooyQGEMQBEEQBEEQBEEQBFFGSIwhCIIgCIIgCIIgCIIoIyTGEARBEARBEARBEARBlBESYwiCIAiCIAiCIAiCIMoIiTEEQRAEQRAEQRAEQRBlhMQYgiAIgiAIgiAIgiCIMkJiDEEQBEEQBEEQBEEQRBkhMYYgCIIgCIIgCIIgCKKMkBhDEARBEARBEARBEARRRkiMIQiCIAiCIAiCIAiCKCMkxhAEQRAEQRAEQRAEQZQREmMIgiAIgiAIgiAIgiDKCIkxBEEQBEEQBEEQBEEQZYTEGIIgCIIgCIIgCIIgiDJCYgxBEARBEARBEARBEEQZITGGIAiCIAiCIAiCIAiijJAYQxAEQRAEQRAEQRAEUUZIjCEIgiAIgiAIgiAIgigjJMYQBEEQBEEQBEEQBEGUERJjCIIgCIIgCIIgCIIgygiJMQRBEARBEARBEARBEGWExBiCIAiCIAiCIAiCIIgyQmIMQRAEQRAEQRAEQRBEGSExhiAIgiAIgiAIgiAIooyQGEMQBEEQBEEQBEEQBFFGSIwhCIIgCIIgCIIgCIIoIyTGEARBEARBEARBEARBlBESYwiCIAiCIAiCIAiCIMoIiTEEQRAEQRAEQRAEQRBlhMQYgiAIgiAIgiAIgiCIMkJiDEEQBEEQBEEQBEEQRBkhMYYgCIIgCIIgCIIgCKKMkBhDEARBEARBEARBEARRRkiMIQiCIAiCIAiCIAiCKCMkxhAEQRAEQRAEQRAEQZQREmMIgiAIgiAIgiAIgiDKCIkxBEEQBEEQBEEQBEEQZYTEGIIgCIIgCIIgCIIgiDJCYgxBEARBEARBEARBEEQZMcz2BAhirqKqKgCA4zhwHDfLsyEIgiAIgqh8tPiJ52lNmCCIAxsSYwgiRxhjUBQFoigiHA7DYDDo/wmCQOIMQRAEQRBEHIwxyLKMUCgEWZZhNBr12EkQBBJnCII44OAYY2y2J0EQcwXGGCRJgqIokGUZsizrj2sijCAIMBqNFFwQBEEQBEEg4oaRJEn/v6IoMc9zHBezsGUwGGhhiyCIeQ+JMQSRJaqqIhwOQ1VV8DyvCzKa2MIYA2MMqqpC+1nxPB8TXJA4QxAEQRDEgYIWF2lCDM/zMf+O3kaLo7TFrWTOY4IgiPkEiTEEkQEtLUmSJDDGwPM8OI7TnTGpxBUSZwiCIAiCOFCJdhMD0MWUeDEm2evixRme5xOcxyTOEAQx1yExhiDSoKoqZFmOCSS0i78m0GQrpsQLM7IsY3x8HIsXLyZxhiAIgiCIeUN0OpK2iKUR/Xg2xIszoVAIwWAQTU1NMfETiTMEQcw1qIAvQSQh2lYbbZktBK2ejIYoiujq6kJTUxMkSdK3oZxpgiAIgiDmIpqbWJZl3f1SzPiJMYZAIID+/n7U1tZCFEXdORPvPKb4iSCISofEGIKIQ6v2rxXnLVV3JI7jwBiDwWDQ96sJQOFwmGy5BEEQBEHMGeLTkoohxMQTHZMZDAbdbazV9SNxhiCIuQSJMQQRhaqqGBsbg8FggM1mS2uh1eyy+RIfFMQ7Z1KJMxRcEARBEARRSSiKgr1798LpdMJsNpc0NokeW/t3tHMGiMRzoigiHA4DSF6zj+IngiBmGxJjCAKxttquri40NDTA4XCUdJ+aMybd88mCC1r5IQiCIAiiEoh2E2/btg2HH344LBZLSfeZLn6KFmcEQdAXzhhjEEUxJn7SXMcGg6EkLh6CIIhMkBhDHPAks9WWs661VpMmHelWfkicIQiCIAii3CRrcpAN5YxLotOa4sWZUCikb6OJM1r8ROIMQRDlgMQY4oAmWbX/TI6VYlHIRZ5suQRBEARBzAapmhxkGz8VGmMVEqdlK85ojhkSZwiCKCUkxhAHJFpakhZIRF9ky32xzcYZk4lMttxocYZsuQRBEARB5EO6JgflWswqJqnEGVVVSZwhCKLkkBhDHHBkqvbPcRxUVS35PEpd3I5suQRBEARBFItoN7EWQ0RTTmdxqfaTTpwRRRGhUAg8zyc4jyl+IggiH0iMIQ4oNDeMqqopL5yz4YwpNSTOEARBEASRD9FNDjLFT3PNGZOJ6PgJmOmkqSgKFEVJWRA4/nUEQRDJIDGGOCCIt9WmExmyDSaKlVo0G2QSZ0ZHR8HzPJqammjlhyAIgiAOUDK5iaOZD86YbPYd7QqKFmdkWcbg4CBcLhdqa2tj4icSZwiCSAaJMcS8R7PVaqlHmS6I5b7IV8IqUrw44/f79XQtsuUSBEEQxIGH1rExnRsmmnLGT5UQOwGJ4ozH44HZbIYsy5AkSX8+Wc0ZgiAIEmOIeUt0tf9sAwmg8GCiL9iP3mAfTq49IeN+KhkteADS23JJnCEIgiCI+UO6JgfpmAvdKMuBFhsBM/FTMnFGS2sicYYgDlxIjCHmJbnYauMpJE3pxYlX8PfR/8M65xEZxZjouVY66Wy50eKMtvJDOdMEQRAEMfeYrfgpV+ZC7ASkjp80cQZAwsIWiTMEceBAYgwx74iu9p+PUyOflR2/4scDQ49iq28bAGBKns5qP3OVTMFFKlsuiTMEQRAEUXlEu4kZY3ldr8kZk5lU8ZMkSQiHwwBInCGIAwkSY4h5Q7bV/jORSzDBcRy6Az24Z/BBeORJ/fHpLMQYjbmyupOOXMQZsuUSBEEQROUQ3+Qg34WTA7FmTKEki580UUxzznAcFyPOaM5jgiDmPiTGEPOCQmy18WiFazOhMhXPjr+Af4w/CxWx22cjxsznC2m24gyt/BAEQRDE7BHf5KCQ63C2YowmOOS7r/keP2n1+gASZwhivkNiDDHn0YrMFeKGiSabYGJKmsYf+u7HDl9n0ueDagiSKsHIGzPub76s7qSDbLkEQRAEUTnk2+QgHdnETx6PB21tbZAkCdXV1aipqUFNTQ0cDkdO1/wDIXYC0osz4XBYj63incckzhDE3IDEGGLOEm2rzaXafyYyBRM7vJ34Q9/9GevCTMnTqDfVpd3PgUomcYacMwRBEARRGorpJo4mXfzEGENPTw96enqwcuVKVFVVYWpqCh6PB319fQAQI87Y7faUczrQ46dsxJn4+OlAPmYEUcmQGEPMSVRVhSzLRQ8kgNTBhMpU/H3fP/DPkWfBkHlFZlr2phVjNA6U1Z10UM40QRAEQZSeQpscpCNV/CSKIrZs2YJgMIhjjjkGdrsdkiTB6XSipaUFjDH4fD54PB643W709PSA53ldmKmpqYHVao2ZK8VOEaLFGe2YqKqKcDisd7okcYYgKhcSY4g5RTGq/WciWTDhCU/i9333YLe/O+txMjlntHlTQJFIupWfvXv3IhAIYOnSpUm7NREEQRAEEYvW5ECLn4otxADJ46fx8XFs2bIFdXV1WL9+PQwGg14oOPp1TqcTTqcTS5Ysgaqq8Hq9cLvdGBkZwe7du2E0GnVhxmQyFXXe8wXt80wlzvT29sJoNKKpqYnEGYKoEEiMIeYM8bbaUrVJjg8mtkx14J7+B+FTfDmNc6AX8S0m0eKMJEnw+XwJaU2UM00QBEEQiZQqLSme6PhJVVV0d3djz549OPjgg9Hc3Jz1Pnmeh8vlgsvlwrJly6AoCqanp+HxeDA8PIzp6WkwxrBjxw5doDGbzUV/P3OdeHEmEAjAbDaDMQZRFNPW7KP4iSDKA4kxxJyglLbaeLRgQmYKHh9+Ei+MvZxVWlI82ba3JmdM7qSy5VLONEEQBEHMUOwmB+nQ4qdQKIT29nZIkoRjjz0WTqezoHEFQdBFFwCYnp7Ghx9+CKPRiIGBAWzfvh12u13fprq6GkZj5gYKBxqao1yLibR6fcnEGW1hy2AwlPx7QxAHMiTGEBWNZquVZbksgQQQCSam2DRu3H0zegN9eY8zLXuz2heRG1owoZHJlks50wRBEMSBRnSTA6B0bphoOI6Dz+fDG2+8gcbGRhx88MEwGIp/q6GNuXLlSgARx+zk5CQ8Hg96enrg9/vhdDp1ccblcpVkHnOdaId5vDgTCoX0bTRxRoufSJwhiOJBZyaiYtFstVu3btXziMtx8u+UduEpPINwIFzQOIOeIQyqg6ipqYHNZks5d3LGFJdM4gzZcgmCIIj5jOYUffvtt7Fy5UrU1dWV/Pqmqiqmpqbg9/tx2GGHYdGiRSXdXzRGoxENDQ1oaGgAECkYrIkzu3btQigUShBnouvSHSjEL2bFQ+IMQZQfEmOIikS7cY7umlTqE72kSvjz0ON4xf8aUIRdhQQRY2Nj6OrqgsFgSOgKAJAzJh8yBRPxJBNnMtlySZwhCIIg5hrRxe61+CnXa2Y+BAIBtLe3QxRFNDc3l1yISddCGwDMZjMWLFiABQsWAACCwaAuzuzYsQOSJKGqqgo1NTWora2F0+nUuzkSM2QrzsQ3UyBxhiCyh8QYoqJIVu2f53moqlrS/e4LjeB3e/6IgdBQ0cYM8SLWr1+vrxZ5PB7s3bsXO3fuhNlsRk1NDVRVhSiKsNvtRdvvfKdQJxGt/BAEQRDzjVRFekvtvt23bx86OjqwaNEiVFVVZZUOVO5rqdVqhdVqxcKFC8EYQzAYhMfjgcfjweDgIFRVRXV1tb5g5nA45uX1vlBhLlX8pMWyoVBIj9tJnCGI7CAxhqgYUgUSPM+XNJh4y/0uHhr8E0RVLOq4XtkLlUXq3EQXnpNlWRdnGGNoa2ujwnM5UsyLOokzBEEQxFwmVZODUi5mKYqCnTt3Ynh4GIceeiiampqwY8eOsqReFyIycRwHm80Gm82G5uZmMMbg9/t1caa3txccx8WIM3a7fd5c70sVPwEzzmNFUaAoSsqafRQ/EcQMJMYQs060rVZT7eMLtJYimBDVMB4e/DPedL9d9LEBQIUKv+KH0xDbRcBgMKCurg51dXUYHh7GYYcdBlmWqfBclpQ60CNbLkEQBDEXyNTkoFTOGL/fj7a2NvA8j40bN8Jms5V0f6koRgoWx3FwOBxwOBxYvHgxVFWFz+eDx+PBxMQEuru7Y7o5aanmc/F6X674SUv5SiXOaPGT9v/4uJ8gDiToDo+YVeKr/Sc7IZfi4j4UHMZde/6IveK+oo4bz5TsTRBjouE4Tq8nE114Tluh2blzJ0RR1HObNXHmQM9tLudFO50tVxNnyJZLEARBlJNUbuJoSrGYNTw8jG3btmHx4sU46KCDYuKRbPdXDAGlVPA8j6qqKlRVVaG1tRWqqmJ6ehoejwcjIyPYtWsXTCZTjDhjsVhKNp9iMxvxU7w4I8syJEnSn49f3CJxhjiQIDGGmDWii8wBSCkwFDtN6dXx1/HnoccQZlLRxkzFtDwNIH0hu/j3Zjab0dTUhKamJgCIyW0eHh6GLMtwuVx6EHCgFZ6b7e5TqcQZbeWnu7sbDocDdXV1JM4QBEEQRUerrZfMDRNNMeMnWZaxY8cOjI6OYt26dfoCUjRz0RmTCZ7nUV1djerqaixbtgyKouip5kNDQ+js7ITFYtFjslLXOCyESomfUokzPT09WLRoEZxOZ0xDhQMpxiUOPEiMIcpOfLX/TDepHMfpKz+FEFJCeGDgEbw7+UHBY2VLRIxJTTZBhFZ4btGiRWCMIRAI6OJMf38/GGMxKzTzKbc5FZX0/uKDi8nJSRgMhqQ501pwQbZcgiAIIlei3cRak4NM8VMxxAGv14u2tjaYTCZs2rQppRMkFzGm0EKys4UgCKitrUVtbS2AiEildWrq6+uDz+dDd3c3pqenK7IOYCXFHfHx0/j4OBobGxOcM9ELWyTOEPMNEmOIspKNrTaeYqzs7A314k9Dj2Obt7egcXJlWvZm3CaX98ZxHOx2O+x2O1paWsAY03Ob3W43enp6YgoGz+Xc5lSUYyWsELQAWavzE+2ckWWZbLkEQRBEzmitqssZPzHGMDg4iM7OTixduhQrVqxIeyM8G86Y2cZgMKC+vh719fUAgPfffx9VVVVgjKG7uxuBQCCmDmB1dTUEQZiVuVbC8UqHqqp6bATMxE+SJCEcDgNAQko4iTPEXIfEGKJspKr2n4lCV3be8/wTL088BIXJ2FhzMN70jOc9Vq5MFcEZk+n1TqcTTqcTS5Ysgaqq8Hq9cLvd8yK3eS4SLxblkjNNtlyCIAgimkxNDtJRiDgiyzI6Ojrgdrtx5JFHoq6urqT7y4VKXrjQas5oqeaVVgewko9dtvGTJs6Qc4aYD5AYQ5QczRWgBRK51s7I9+IeVHz4x8hvsdv/vv6YinDO4xSCV/Zl3KaYgQvP83C5XHC5XFnlNtfU1MBkMhVt/+VgLjhjMtnGSZwhCIIgMpFNk4N05LuYNTU1hfb2dlitVmzatAlmszmnOZeLSnd6AJVVB7DSj1c+8ZMmVEqSpG8TLc5oaeEEUamQGEOUlHzSkuLJx2Y7GNyJv++7HdNyrAuGofDaM7kQZunFn1KvImXKbd62bRvsdjtqampQW1uL6upqaqNdIJrgmC2ZxBmAbLkEQRAHGtFu4uhrRC7kGj8xxtDf349du3Zh+fLlWL58eU4xW7EbLqRiLt9cZ1MHsLq6WhdnHA5H0d7vXF/MikdbuIp+vfa70ZwzPM8nTQsniEqB7rqIkpFttf9M5LKywxjD256/Y/PEn6EmEV5kpfQdlKKR1PT7K/cFIT63ORwO6+JMV1cXgsEgnE4nGGOwWCxQFGXWcptTMd+CiXjIlksQBHHgEl1jrJzxkyRJ2Lp1K6anp3HUUUehpqYmr32WU4ypdKdHJtLVAfR4POjt7QXHcTFuZpvNVtExUCEUI37KVpyJdh7P1+NJzA1IjCGKTrytttCWvtmutPjlKTw98hv0BtpTbhMI+gCULy1HyqJ99mwGEyaTCY2NjWhsbAQAhEIh3TXjdruxefPmGPtsVVUV3fRnoNhiEdlyCYIgDgyK4SaOJtv4yePxoL29HU6nExs3bsw7fbncBXznG6nqAHo8HoyNjaGrqwsGgyGhSUO2zPfFrHiixRnte5lMnIlf3KrkY0TMP0iMIYpKdMtqIPf85mRks7LTF+jAU/vugE/xpN3OaBaAMppjwhXmjMmExWLBwoUL4fV6wXEcmpub9RWawcFBKIoSY591Op1lfw+5pgGVm1IHO9ms/ESLM2TLJQiCqHxUVUU4HC7YDRNNJnGEMYbe3l50d3dj1apVaG1tLdiZQM6Y4hFdB3Dp0qVQVVWvA7h3717s3LkTZrM5RpzJpb5PJaF9lqWK77TvTDJxJhwOQxRFEmeIWYHEGKIoRN8QFjOQANJf3FWm4nX3X/Gm+wkwZHFR5lUA5Uu7qXRnTDo4joPNZoPNZkNzczMYY/D7/bo409fXBwAx4ozdbj/gL1rlXnkiWy5BEMTcpdAmB+lIt5gVDoexZcsW+P1+HHPMMXC5XEXZX6XGNPMBnuf1eAuI1AHUxJmBgQFs374dNpstRpwxGo366yvZGaN9b8o1v3TijCiKaVtpV+oxJOYmJMYQBVNsW208qWy2XtmNp/b9Gv3B7VmPpTAZgDHjdsVCVuW0z8+lEzrHcXA4HHA4HFi8eDFUVdVzmycmJtDd3Z1gn7VYLEV/j5UcTACzP79M4kwwGEQoFEJTUxMFFwRBELNIOeInbexoJiYmsGXLFtTU1GDjxo0xN+yFQM6Y8mIwGFBXV6e3HZckSa8D2Nvbi46ODjgcDj0mq+TjVW4xJp5ocUYQBL1eH2NMF2empqbA8zzq6+v17Sh+IgqFxBiiIKKr/Rc7iNBItrLT7f8IT4/8BkHFm9NYMgsDyD6/tlDCc9gZkwme51FVVYWqqiq0traWzT5b6cdrtsWYeOJzpr1eL0ZHR1FbW0u2XIIgiFki2g1TjJTuZMTHT4wxdHd3o7e3F6tXr8bixYuLXqODmD2MRiMaGhrQ0NAAIOJ+0tzMu3fvRjAYRG9vL/x+P2pqauByuSqmScNsizHxRP8mNXFmbGwMgiDA4XDo2/A8D6PRqMdPpboXIuYvJMYQeVHMav+ZiHbGKEzGq+OP4p3J/wOySUuKQ8lCHCkmldZNqZTE22cVRdFXaLKxz+ZCJR83VVUrdn7aqqVW5JdypgmCIMpLfJODUgkxQGz8FAqFsGXLFoiiiI997GOoqqoq+v5y6d5U6H6Ayl+cmW1MJhMWLFiABQsWAADeffdduFwuiKKIHTt2IBwOV0yThkoTY+KJjp+MRmOMcyYUCunbkDhD5AqJMUTOlNpWG492cZ+SxvDkvtswHNqd91gMDALHQ2GlDxaAuV0zplAEQcjJPltdXQ2DIfMpqdKPV6U5Y+LRxFMgec50vC0XoJxpgiCIYhDf5KDUN77aDeTY2Bi2bt2K+vp6HHnkkVlda/PdXzmp9Hig0tDaZDc2NoIxhmAwWDFNGipdjAFi47tkzplk4oy2+EXiDJEKEmOInNBstaV2w0TDcRzGTLtxT/8fEVL9BY9n5gwIsHARZpYZiUlpb84PpBNyJvtsKBSC0+lEbW2tvkKTyj5bycdtLogx6b6PqYILURRjnDNaMWCDwUDBBUEQRBpK2eQgHRzHYWpqCiMjI1i7di2am5tLvr9yOGO0fRG5Ey0mVFKThrkgxkQvZsWTKn5SVVUXZ3ieT1jcoviJIDGGyArNVtvT04NgMIg1a9aU5eQhqxLeDP4FnTWvAkW6vht5oWhjZYPEJJg4U8rnD9SVnXj7bDAYxOTkJNxuN4aHhyHLcox91ul0pizmXElUuhiTS2vwbFd+yJZLEASRHM1N3NHRgaqqKrS0tJTl/BgMBjEwMABJknDcccfpdS5KCTljKpt0xyu+SYNWY87j8cDtdqOnpycmHb2mpgZWq7Von7km4lVy7FCM+ElRFCiKglAoROIMAYDEGCILVFWFLMv6CURbHS817vBePLnvNoyIvUUd18SV92svMQkmJBdj6IQ7g9VqhdVqxcKFC8EYQyAQ0FdoBgYGoKoqqqurEQ6H9QtbJR6/Sp2XRrqVnUyQLZcgCCJ7opscSJIESZLKci4cGRlBR0cH7HY77HZ7WYQYoLytremakh/ZHjeO4xKaNHi9XrjdboyMjGD37t0wGo0JHTTzpdJjJ6CwmoBa/KTFX/HiTCrncSlrShGVAYkxREqibbXaSVK7ASs126Zfx79Gf48wCxV9bANf3srxkioBaXZJKzuJcBynB5EtLS1gjOlttPv7+zE8PIzR0dEY+6zNZquIC1alBxTFLDBMtlyCIIhEkjU5EASh5Ck8qqpi586dGBoawiGHHAJZljEyMlLSfUZTTjEGoPgpVwo5XjzPw+VyweVyYdmyZVAURe+gOTQ0hM7OTlgslhhxxmRK7QpPNrdKjwsKWcyKJ504I8uy/nz84haJM/MPEmOIpMQX6dV+/KXOB5ZUEc+N3YMt0y+XbB/GMleKT9feutyBy1yF4zg4nU44nU54vV7Y7XbU1NTA4/FgbGwMXV1dMBgMCfbZ2SAXG+tsUMr5pRNnRFEkWy5BEPOeVE0OSh0/+f1+tLe3AwA2btwIm82GwcHBssYY5IypfIp13ARBQG1tLWprawEAsizr4kx/fz+2bdumx2pak4Z0HTQrPXYCyhM/xYszsizrjjpNnNGcM1r8RMxtSIwhEoi21cbfJPE8X7JgYkwcwJP7bsV4eLAk42sYufKeuDp3d0KukWZVIJhPaBdDbYVm6dKlUBQF09PT8Hg82Lt3L3bu3FnQCk0hcwMqO0hUVTVlYeRiE7+CQ7ZcgiDmM+maHJSy5tnevXuxbds2NDc3Y/Xq1foNWiljtmSQM6ayKeXxMhgMKTto9vT0wO/3w+l06jGZy+WK6eo1V5wx5ZojiTMHDiTGEDrJbLXxJ51SXdjbpl7E82P3Qi5DlyOhzGKMYBYSBILa2lpUV1eTM6ZICIKgX+CByAqNFgT09fXlvEKTL3NFjJmti3W2tlyO42AwGGAymciWSxBExaPdJMmyDABli58URcGOHTswMjKCww8/HI2NjTHPlzvGmGvOmLAqwcQXPxYoJSpTwRcQx5brWhrfQVMURb0O4M6dOyGKIqqqqvS4bC5c5ysxftLEGWAmLTy+oQJR2ZAYQwBIbauNp9jBhKgG8a+R32O7742ijZkJQ5lPTA0LG3HQypUxAkFvby/8fr9+ojSZTKiuri6bY2Euk83qicFgQH19Perr6wFEVmi0IKC7uxuBQCBmhaZYx34uiDGVZAVOFVz09fXB5/Ph4IMPTlkQmCAIohKIblkNJDoCNXie12+aioHP50NbWxsMBgM2btyY1HlbzlbT2v6yFWMKFW2KIfzs9vfhEOfKgsYoN6PhLjSZD8rrtbO5+Gc2m9HU1ISmpiYAMx00PR4Ptm3bpv82ent7UVtbq3fQrCTmQvykFe5esmSJvrClxU4UP1UmJMYQUFUV4XA4pRsmmmLabPeFevDkvtvgkfYVZbxsEVDeG2Vpf82YeIEgHA7jo48+gqIo+iqB1sq5Ui9EcxWj0YjGxkZ91TDTCo3L5crr2M8FMaacNttc0YILxpgePJAtlyCISiS6yUG28VMxhBHGGIaGhrBjxw60trZi5cqVKc+BpUyNSkY2AomqqtixYweGhob0626ymEdUZJiF0t6m7PD3zCkxRmEyhsXtBYkxlXL9j++gOTIygl27dsHv92NwcFDvoKnFZQ6HY9bnPlfiJy0+0s5PmtAVL85oaeHE7EJizAGMlh6gdUvKpohmsYKJDyefxQvjD0BJU9y2VPBlPvFIavL3aDKZYDab0djYiIULFyIYDOoCQfSFqLa2FjU1NbDb7XTSRHGCiegVGq0ts3bsh4eHIcsyXC6XfuydTmdW+5wLYkwlreykIvrGJhtbLq38EARRTrJ1E0dTjPhJlmVs374d4+PjWLdunZ4CkopKS1MKBAJoa2sDABx++OEIBALweDwYGBgAY0y/8a6trUVf2I+1NZEFlKAcxmsjfTizeVXW+8qGTl9PQa8vN9PyKKbk8i5glgOO42A2m2E0GnHooYeCMQa/36/HZb29veA4LsbNPBsx8WymKWVLdPwU7fiOFo/D4XBMSnh8tyaivJAYc4CSTyChbVdIMCEqXmwevw39gXex3LIEfaFphJmY93j5IJRbjMkgOGnigs1mg81mQ3Nzc0wr54mJCXR3d+uV62e7W9BsU+zAkuM4fYVm0aJFYIwhEAjA7XbrNWcAxKzQpAoC5oIYM5eCiXhSiTNacAGQOEMQRGlJ1+QgHYWmDE1PT6OtrQ0WiwUbN26ExWLJ+JpKKuA7OjqKLVu2YNGiRTjooIMgyzJqamrQ0tKixzxutxvj4+Po7u7Ge/IUsGgVwjYTvrHjJRxWsyBGjCmUoCJiT3Ao5rFX3a9B4AQcWbUONsFWtH0Viyl5GF55NO/Xl8oZw5gCjiss1Tt6bhzHweFwwOFwYPHixVBVVY+Jx8fH9Q6a0XGZ1Woteew1lxaz4slGnOF5PsF5XMnx7HyBxJgDjOgfn3biy+WHVkgwMRLagZfGfgnf/guJR+qBVViMsFxeMYYv83klnMIZA6S+aY9u5bxkyRKoqorp6Wm43e5Z7RZUKZTy4sBxHOx2O+x2OxYvXgzGGLxeb4IwFt9GOzoIreSLVyXbbDVUVc3q+5xMnCFbLkEQpSCbJgfpyFcYYYxhYGAAO3fuxLJly7BixYqcBKDZdsaoqordu3ejv78fhx56KBYuXJhwHKJjntbWViiKgtc+ehl75AB+1PESZDCMT0bSivNtgNDvH8Of976E7678NwDAaHgCYSbBK/vhNNgBAG9PvYuB0ADMvAlHu47K6b13+nqxxrEsp9fkyqS0F9MFiDFAaeITv7ILVqEVApe/gJVOKOJ5HlVVVaiqqkJra6seE3s8Hj29yWQyxcRl2YiVuTJX4qdsaiBmK87EL25V+vufi5AYcwARX+0/n8rl+eQfM8awZeoxvOe5HwxKzHNGrvwCQrk17WycMZngeR7V1dWorq4GENstaM+ePdi2bRscDofunJnPxYDLXYCO47isgwCHw6G/plKZyys7mSBbLkEQpSBfN3E0+cRPkiSho6MDk5OT2LBhA2pra3N6/WwX8A2FQmhvb4ckSTjuuOP0a2QmBEGAwWTENf1vQ8H+RQ6zERzH6UX4OY7D0NAQFEWBy+VKG/M8s+9DPLT3CZgEBoV9FgInYDTsBgC4pSldjBkPjwMAvLIv6/fslb34bf8f0eUL4u7Dv68/LqkyjHxxb7Om5L3wKxN5d1QqVfwUlPcAAByGg/MeIxehIzomXrZsGRRFwdTUFDweD4aGhtDZ2Qmr1RqT1lSMBcsDJX7SvidaTVFRFEmcKSEkxhwgxFf7z/dkkuvKTlCZxCtjN2Mw+H7S5wVW/pMax5X3Zj5VzRgg/5v2ZMWAPR4P3G73AVEMeDZP/umCgL179wIA3n777aIHAcViLqQpafb/QiFbLkEQhaLV1svHDRNNrvHT5OQk2tvbYbfbsWnTpryuI7NZwHd8fBxbtmxBfX09jjrqqJwWiO7a+Q6eHNyuCzEAEIKKgw6KFK4VRRHvvvsuwuEwtm/fnrLOG2MMN+5+DNuCH0AQAAVAf3AvltlaMCpOAADc0iRarYvgV/wIqkEAgFfxZjXPze438ffRv2BvyApZjXVibPPtwRHO7F1M2TAp74UKBX5lAk5D+npBqSjF9S2k7AEHriAxppAUKi2NXxMr4xcsfT4f7HZ7TFxmNObe0nwuOGOKET9p75HEmfJAYsw8J9dq/5nIJZjYG9yCl8b+BwFlIuU2YiAElPk+dS46YzJhMpmwYMECLFiwAIyxeV0MeDZbMyYjOghoamrCe++9h5UrV8a0MHc4HDFBgMEwe6feuRBMlEowIlsuQRDZEu0mzrbJQTqyjZ8YY9izZw+6urqwcuVKLF26NO/9zkaakqqq6OrqQm9vLw4++GC0tLQk3S4V3/vgWTwzvAvxjS8nwyH932azGYIgoLW1FdXV1XohYLfbrdd5M7isuEd+BWHjBKJ31x0YiIgx+50xE+EpAMB4eCZW9WVwxgTkIO7ovxNjUhdE1YCQYgSgIKSE9W1GRA/2miawyFKfdqxc0Ir3TsujeYkxJXPGKH0AilczplCSLVhq4ozmrHI6nTFxWSaxUKtRV+mLWaWIn5KJM9p/oiimrdlH8VNmSIyZxxTDVhtPNsEEYyo+nHwUH00+Aob027ocVZgMTxY0p9wpn2UXyK9mTCEcCMWAK/Xkrl2ok7mWPB4Pdu/ejVAoFBMEZLJXl2qOlUy53DtkyyUIIhmqqkKW5bLHT+FwGFu3boXX68XRRx+tpyaXcp/FRKtHODw8jI997GOoqqpKuW388VRUFV984zF0TI0kCDEAMC2FIKkKjLwQ8/roOm8tLS1QVRWbh7bif8ceh2AMJ4yzy9eL0+uPw+h+8WVCmgQAjO1PUQIAr5JajHl78gP8deQh8JwIjgOCyozDwqcEZ8aQA9jtHyqaGCOpIvyKe//8xlJuNyVN4e6h23Dhgs+j1ZrYtrsU16+gsgdcBYkx8ZhMJjQ2NqKxMdKdSxRFPS7T3ORam3UtLouPQbQYgeKn2DIXgiAkiDPR8ZPmOjYYDEU5j85HSIyZp+Rb7T8TmuU11UkzILvx0tj/YG+oPavxTELuNsHCKa8YI5fBGZOOfIoBVzKV5oyJJtnvItq1BCCmjfaOHTsQDof1lLKamhpUVVWV9EI6V5wx5a55RLZcgiAKbXKQjkzCiNvtxpYtW+ByubBp06a80ijiKaczxuPx4KOPPgIAbNy4MScHqDcs4rObH8W+UOr0IAbALQaxwDpTdybZe7un/wW8MvkyBEPy973N04V3Rt7BkC3iMpkQPQCAcSlKjJET5xFSRfy27/cYDu+IaQQRihJj/MqMe8enBOHx+3BS3REp31M6FKZAiOpQNCXvBfanbaUq4vuG53X8a+JhcJyM3YGtuhgzIg6izrSgJN8FxhSElEFwBd5SllKMicdsNqOpqQlNTU0AEOMmHx4e1tPetLjM6XTqv91Kv97PVvyUSpwJhUL6Npo4o8VPJM5EIDFmnqFV+9cCiWJ/0bWxkv3YBwIf4JWxmxBSp7Ier9ydjQDA550u6/7CacSY2TgJZVMM2Gg0wmKxYGJioiKLAVfqyTubYMJisWDhwoVYuHBh2pQyLQhwOBxFfb9zoWZMJcwxG1vuwMAAmpqaYLfbSZwhiDlOMZocpCNVMV3GGHp6etDT04ODDjoIS5YsKdp+y1HANzqtatmyZejq6sopZuj3e3Dx5r/AryS6WAycAoVxYPsTzCfEgC7GxB+jkBLGD3fcizG1F+kuH14hgLoljZge8wMAeicG8MHEB+h17NG38cU5Yz6Y2oJH990HjgvFpDwxFivG+ORgzL/7g/l3PuoP7cEy6wr9by1FCUBCe2tREXH30B0YCe/U59fpb8PpdRdE5u99HSfVfBxAYfGTyhTwcS2sRXUYDGGElH49BpJUP4y8PaexyynGxGO1WmG1WrFo0SIwxvS0N4/Hg/7+yPvSXF6BQABVVVUVe52vlPgpnTgjyzKGhoawfPlyEmdAYsy8ohRpSfFoP/BoMUZlCt7z3IctU48DyE11n43fnGDgALl8+0tXwBeYfadHstza7du3QxTFhGLA5XBuZGK2j1c6cg0mkqWU+f1+PQjo7e0Fx3ExriWbzVbQ73oupCkVq4BvMUkWXPT396Ouro5suQQxx4l2E2u/42KTzBkjiiK2bNmCYDCYMa0n330CpbvRlSQJW7duxfT0NI4++mhYrVZ0dXVlvb/3xgfx7+8+BZklF4wMggKzoMIvRorjToiBmOe1eKDbtw837P4DIGTXBanHOAx1f7wqW1UsrF4I96RbT4+aCk+jv78fTpcDD3r+gn5xS9J4VVQNYFE5VT4lCE2a8clB7Anuy9hV6dHhZ3HxwjMTjtfuQGeMGDMpD+v/jk5T6vBuwV9G7gbihKJRaRhuaRRVhlps8b6DY12nFRQ/qUzB256fY2Ptj2IeDyp7Is8jhLA6CrOwAN2Bv2GN4ws5jV8psUl82puW6j82NgaPx4O2tjZ9UbNYcVmxUFW1Yo5jNPHxkyRJ6O/vx9KlS3XnDM/zCc7jAyV+IjFmnlCsav+ZiL6wA4BXGsFLYzdiVNyR54jlTRkCAKvVAoSCmTcsEukK+Ja7uF42mEwm2Gw2OBwOrFy5EsFgEG63O6lzo7a2dlaKAVfqybnQgJfjODgcDjgcDixevBiqqsLr9cLj8WBsbAxdXV0wGAwx4kyu9X7mSppSpQUTyVAUBUajEUajkWy5BDEH0dzEsiyXJX6Kvt5r3Ybq6uqwfv36khR2195LKcSYqakptLW1weFwYOPGjTCZTHohz2zimr/1bcOPO15Ou4RnEFRYjNKMGBOeEWOGlSBWAfj78Dv4y8hTEAQl67m/Ndmm/3tS9qJpYROC/qC+UCdzMt73tOF138sQDGLKhcNoVwwQEWC0RG+fEoTMFPQE9mK1Y3HS14+Ibjw18hpaLAtwQu26mOe6AjtxZt3H9b+npBlnzLQ8CkVV8MDeP6An+H7K+e3wt8FlqEFA9UHc3ykqm+/BeHgXtnj/hBbL0Wg0rYHLsBQfTv0GQ+KbCCrjsAozdXCCcp/+75DSByNfix7/k1hlvwgCl32Hjtl0xqRDS/U3GAzYs2cPTjjhhLRxWVU14LAtmpW5Ftoxt1xoC/raOU+LnVRVhSiKCIVCB5Q4Q2LMHCfeVlvqL2q0M2aP/01sHr8VoprdSkRSUqyGlBKG7C/YxSBdAd+5gNVqRXNzc0IxYI/Hg56eHgiCoAsz5SgGXKkXbKD4c+N5Hi6XCy6XC0uXLoWqqjFttHfu3Amz2RwjzpjN5oxznCsX6kpGC3q0eVLONEHMLcrhJo5Gc8Zo3Yb6+vpw8MEHo7m5uWT7jU4tL9Z5nzGGgYEB7Ny5EytWrMCyZctiiulq26Tj5o7X8Mdd7yUt1BuNwKswGmditmhnzDZxCh8OPY0+1olcLxfdgQH93wpUjIc9mJKn9bmrjMMbln9ByDC/oBJ7G+VXgqhBJAby7k9Z2u0fTCnGbHZ/CAaGR4efxdGutbAIEfEioPgxLA7q242Hh7E33Kn/7ZU9+Gnvd6HAm9Zh3ulvh4GLzFFUQ1kv/g2LH8ErD6LTtwPt6iSMnAMSi8T60/JArBiz3xmj/durBBBmUwgpE7AbFma1P6CyYztg5jcUH5cpioLp6Wk9Ltuz7zUgdExOcVkx5whUvhijKEpMjBefEqrFToqiQFGUlM7jYqeSzhYkxsxholtWA8XPb04Gx3EAr+DdybuxM/jPgsdj5cwX0vdZXjFmrjlj0pFNMWCz2RzTqclkKnPv8lmk1MEEz/MxRZZlWdbFmYGBAWzfvh02m00//tXV1TFFILWVh0q/UM+VOQJIKRplI86cffbZ+O53v4sLL7ywPJMmCALATGHuUrthouF5Hoqi4L333oMkSTj22GPhdDpLvk+geOm9sixj27ZtcLvd2LBhA2pra2Oez0aM+cabf8eLe7syCjFARIwxCDOLdpoYszcwiTb7FtgRKEq6+55gPxgYFBUw8CpshuzixJAc64zxSgFgvxijdVZKVTeGMYZX3ZGCx25pGq95PsIZ9R8DAEzJkxDVECYlD7b6XsYW71/Bc5E5BRUjphQrGFIXO9YYCHWB7fce5eKM8cvDYMyNMIvU1dGEGADwykNYYF6v/x1SZpwxQaUPw+H3I4+rE7Bj/ogxqRaytAVJLS4b8b8FPtSMKY8UE5dFizPFKM6djGhhuZKJF2Pi0eKn6PNXtIuR4zgsXboU77zzDtasWVOuaZcMEmPmINpqjtfrhdVqLevK6pQ0jJHFf0G4gKJk0agZOg2VApWVVwBKVzOmki882ZCuGHBfXx+2bdsGh8OhO2eKUQy4ki/Y5U4BMhgMqKurQ11dHYBI/r52/Ht6euD3+2PaaGuBf6UeP425IMbkGvQkE2emp6fLtmJGEAR0MTQYDMJisZQ1fvJ4PFBVFXa7HQcffHBZ3H/RzphC8Xq9aGtrg9lsxsaNG5Oeu9KJMZKq4KKXHkbn9FhWQgwA8DwDx0WldokBvDmxE7/pfxh2a2LB33zpDw5CVgG7IZx1Y4mgbISK2PP/roEetIRNGBsbg08OwsgJ2DOdXNjp8PVgPDyp/z0cmunmNCVPwsIZ8ZL7txgJt4PnAJUBU4oVITX7BS41qhRAtDNGZgokVYJVsCS8Zpv3L9grvgqFiUnH9MqDMX9HO2O8ch/GwrsAACHVnfU8gcpPoc52fgr2orpaQkNdpJNVdFzW29uLjo4OPS7WFs2KlaJYTnG5EHJ1P8eLM+FwGNPT07DZbKWaYlkhMWaOoQkxbrcbH330EU455ZSy/ei6fK/g9fFfQ7IUr96Kwop3Mc2Wsosxs9zaupwkKwbs8Xjgdrv1YsBVVVW6c2O2iwEXm9kWioxGIxoaGtDQ0AAgUhxSSynTjj8A9Pf3o76+HlVVVRWXDjRX3DtakeFCPm9NLCMIovRo8dPg4CBGRkZw1FFHleV8raoqdu3ahYGBSGrMIYccUrbrRLGcMUNDQ9i+fTuWLl2KlStXppx/KjHGIwbxqRfux5joz2m/Aq9CjVJuekJd+O3AqzAIRYybmIrXJ1+G05hbPDotJYpRjnoXTOMm+MQAJE5Gfage7wWHMTAwgJqaGr3GHmMMfxl+Mea1Y2HPzJTYFFbZJYyE2wFECgVPytYE8ScXRHUmXXZUHMcNXbdghW0pvrPsCvBcZNz3J3+L3uD/IV0zDq88k+IlqZOQWaSDKo9a7JMm9deGlNzEmNmOnzKRbYq3pA5CVkdhFiJiTHxcpsXFHo8Hu3fvRigUilk0c7lcecdlcyF2Agpv0uD3R84j8yV+IjFmDhFd7V8QhLKpyLIq4s2JO7HT92zRx06lvJcSBTKyXpYpAulqxlTyhacYmEwmLFiwAAsWLACAohQDruQLdqXNzWw2o6mpCU1NTQAiK5vvvfceRFHEtm3bIMtyTKcsp9M56xfyTOk/lUImm202+P1+2O25tf8kCCJ3tCYHjLGyxk+BQADt7e1QVRVHHXUU3nnnnbJeJwp1xiiKgh07dmBkZATr1q3Tbygz7S9ajOmamsBFrzyMoJK7E5rnGRTGg4eMhTVe2ByBzC/KGgYjp8BlDkLgchN3VAb45UQxJoQwzGYzbAuqUDfhwjYPQ5hJGBofQXd3t17o9Tnswu5wX8xrR0UPwmoIf933S0wrOwAwMAZMKxYEVBMKjVsjzhgjOI6DV/FBgYpdgR50B/qwyr4Mr7t/juHQaxnTvrzKjDMmIHcDADi4MMUWIKDMdHwKqRP6vz+aeh7rXWekHbeQ34WkBmDkS+uSyPacISmDkNSRlM/Hx8WhUEgXZ3bs2IFwOJx3B9O5JMYUEj9pYozD4SjWlGYVEmPmAMmq/WvBRKnxhPvw4ugv4ZH2lGR8mZWvq5GGwmQApcnXTMaB5IzJRDGKAVfy8ao0MSYerX7P2rVrwXEcAoGAfvz7+/vBGItp1+hwOMr+fuZqAbp8CAQC8yaYIIhKJL7JAcdxZYuf9u3bh46ODixatAirV6/W91nuG6Z8a9P5/X60tbVBEARs3Lgxq+L88WLMa/t68e9vPQklz2YNPBhU8FixYAI2cxFdzUyF0yjCZsgvVd4nm2NaWs88HolpA6oITqpDmEXcLnUrW7HCXoepqSnsGR/Cm1MfJhQd9isDuHvwCvBcZIywKmBStkJBPtcZhnjxJqQGwe+Pfb3yTA2Yj6Y70B+8C24pefvueALKGBQmQuDMmBCfBQcnfKwFfiU2fSmkTIAxhr+PXIPR8GjJxJiBwMvYG7ge62oehUkoXRejbH63KgtDZmOQ1X1pt4vGYrFg4cKFWLhwIRhjCAaDelw2ODgIRVFi4jKn05nyOB0oYkwgEIDFYqn4RbtsITGmwklV7V+ryl/Km7+d3mfx5sSdkEvoXpHUILRiZ+UiUqemjGLMAeyMSUchxYAr9bhVuhijBcha/q3dbofdbkdLS0uCONbb2xtTMFgTx0r9/uaKGFNoxydJkiCKIokxBFEi4pscaOcULX4qFYqioLOzE3v37sWhhx6qOxOj51VO4ltqZ4MmJDU3N2P16tU5nY818efhro/wsy0vpW1dnQmDoCIMHqYsC+pmNSYno9oUhMDnPzOvlFhrBQB8SiQVaKd/Eh+5Z9KOhoPTWOVsQE1NDX489BgEIfo7oGK5fQLNlknwHMAY4FPN8Clm5OuGWWsZwvZQS8xjYTUEC6rAcRymdTFGRUB5ABxy6YrK4JWH4DQ0YSL8OvxYBq/Sl7CVqHrwxL5vQeC2wSMflHnUDPGTpIr4x9jdOH/BN/TtP5y8FlD+ARPHMBXejAbrxTGvCchdMAvNELjC7zOySVOS1WEALK0zJh0cx8Fms8Fms+mLln6/X4/L+voixzlanIl2lBdjkagcFDpPn8+XlZN+rkBiTAWj2WqTFWSKbjFd7B+epAbx+vgd6PK/XNRxk6FChsAZ9rtVyoNc5qLB5IzJjmyLAYuiiOnp6aIUAy42lS7GpCvulkwc83q9cLvdGBkZwa5du2AymWLEGYsleUBaCIqizIl2hYXmPPt8keB3vuQ8E0SloNWdShc/aQtcxcbn86G9vR08z2Pjxo0xBSaj47ZywnFc1vtUVRU7d+7E0NBQUiEp2/39z7bX8Of+jgIya1Q0VvlgNMgIywYo4GAoSNYBwFQ4DCLsxvxiQIVx8ElmeCUzRDX57ZNfCcLLRDzqeR3ATKHdoWCkrsobE50YDu/RHSg2QcRa5z7YDZF6NTLjMSlbIbHCbs8ajdMIsRH0iAv0x0JqENoVe1r2gYeKjdVdsAu51270ygMQle3wsxZMy70Jz3MQ0BVciCrhaQDAhJQ5VksXP3nCI7hv7/VQ9sfTU1I/OiavhJ0f0r9j09KrMWKMqIxgj++HWGT7d1SbTgYAKGrkuivwuS+CZJOmFFaHAABynmJMPBzHweFwwOFwYPHixWCMwev1wuPxYGJiAj09PTGLZpVeBFmjUAePJsbMF0iMqUCibbWaEhv/49JuQostxkyI3Xhh9BeYloeKNmYmzLwFASUXVb4wyi3GHMg1YwohVTHgzs5ODA0NYc+ePRVXDHguiDHZzo/nebhcLrhcLixbtgyKouhttIeGhtDZ2QmLxRIjzhSjjfmBZLMF5k/OM0FUAqncxNGUKk1JK3K7ZMkSrFq1KuE8ponMleqMCQaDaGtrA2MsQUjKFsYY7grswXavN++W0zynYEm9By6rCF844mJWVB4Q8j9uBk6GyxSEIUc3DGOAXzbBJ5vhlzPXbfHLITwkvY8QkxAtxgwHp8AYw2/7nth/XBgWWz1otbn1ejV+xQSvYkma/pQrPMewzDyGPWKDXvQ3uoCvVx7DCdW7YBHyWwidDHdjSt6OySRCDA8TJqSPoc23G6fUAJIqwKfwEFURZj5198BU8VOXvw1PjP4aKhQIMGC3709wB2+FnY8VkXzSB1CYHwJnh6J6MeT/FUJKF6bCm3Uxxh3+JwxcNWrMZ+b8nrOJTSQ1kqols+KIMfFwHIeqqipUVVWhtbVVd5R7PB6MjIxgaioi+m3fvr2ki2aFUoyaMbORRl8qSIypMFRVhSzLaQMJ7XFt+2KxbfppvOO+W1eey4WZM6GYJdkyw2DgBMisNCtj8ahQ4Qv44LAlv+kiZ0x2aEXPenp6sHr1alit1oKLARebbKvtzxaFzE8QBNTW1qK2thZAcueS3W6PaddoNOaeDlgKt18pKEYwMZ9ynglitolucpCu01mx05RkWcaOHTswOjqaschtqVOkkpFNzZixsTFs2bIFCxYsyLvttqjI+MyLD6FbzV+IsRrDaG1ww7w/LUkTJhQ1v+sWU1U4jGE4cuyUFFIM8Epm+GQzVDazbx4K1DQ1XOpN1dirjkOVYxcmhoJT+N+ef0Lh/DBxEtZW7YPLGBFHFMZhSrZCZMVLn+fAwHPAKste7Aw1AwBENVKLxifvg5H7CwQ+X0e6isHQExCTlDAQOCsGxSOx1ecBIEBlHKYVCwAOfiWQUYyJ/9696Xkamycf09/TUY4wfKEbYU7ydWCQ4A2/CZfpZAwGbsGkFHH3T4Vf14WesdDjsAkH5SXGZBM/SUpkITs6TUmWnobBeF7O+8uGaEf5smXLMDg4iOHhYZjN5pIumhWKoih5xYcagUBg3rS1BkiMqRiibbXaSSPdjWR0fmChiIoPm8dvw57AGwWPlQ8mvvwnBhNnKJsYAwBvvvsWnGZHTP0To9E4b1TdcsNxXFGKAReb+eSMyUQy55ImznR3dyMQCMDpdOrHP9t2jXPFGVOoaDTfcp4JYrZI1uQg3e+qmGlKXq8XbW1tMJlM2LRpU8ZV6HzqtxRKOjeOqqro6upCX18fDjnkECxalF8B1NGgD+e/+AA84fybMtTa/WiumUT06Z+xyOcos9yuCYwxcGAQeJa1ECOpfCQNSTYjrAhJv0PVpiDc4dRuRlF0QWWTUFnsayclPzZPdmKhdQor7eMw8JHPI6gaMCVbwQpoWZ0Mbe/R7hFRDUEyj+ClqdshcPkuuqqwchLEJAWZjZwTXcHDsDMwpc9CVA2YliOxV0AJoNZYk3Lk6Phpq/cj7A68jl2B9wEAVk7EEbZ+VPPpv19T4Vfhld7DVPg1aC22ZTaBgLIdjEkIKV2Q1DEwpoDjcrt+ZxM/ac4YlU1DZUFw4CCGfgpeOAY8vyDta4uFxWLBihUrAJRu0axQFEUpyLHj8/nmlauYxJgKIN5Wm029hGJ1BHCLO/Di2M2YlMqXlhSPkSv/19DECwiUcXHq6GOPhuKT4Xa70dvbi46ODlRVVUXmYjLNmRvQSiBZIFtIMeBiz62Sb65L+T0zmUxobGxEY2MjgPzbNRZai6VcFMMZM59yngliNsgmLSmeYsROjDEMDg6is7MTS5cuxYoVK7I6b1VSmlIoFEJ7ezvC4TCOO+64vG9utntG8PlX/4SQmp/TgoOK5top1CVpW62JGrIqZH99ZSoY46FAgJFLPyeVcfDJJnglM0KKEZqMwRiSunuqjKGUYsxKayteHprEgiouxk0DAC1VPpiNA6gzBfbvF5hWrAiqpVmM5PZ3UzJFvX/GRjG17A1wyPf7p8LKhSEnuXU0cbXo8K/CntB0zONhZsC0EhFj/Ep6D7z2+b7h3oz3pv+FEIvclyw0eLDWOgQjl3ne7vAzsAprITNPzONT4c0Q97tWFDYFv7wFDuP6jONFk038FFZnOkrJ6gg45QOATUKRXwFv+rec9pcP8fFT/KKZJEl6XBa9aKbFZS6XCwZD6e/JCl3Mmm/xE4kxs0y0rVbrkpQthdhdGWPo8T2MnVN3wcEJqLcdga7A7rzGKhTDLIgxRr68qQEqr8acEEVRhNvtRl9fH3w+HzZv3ozq6mo9DaQSVswrOX0q07HJthiw5pwpVjHgShdjyplGlW+7xrkiTFLOM0HMLumaHKRDEyfyPV/LsoyOjg54PB4ceeSRqKury/q1s5WmFL/PiYkJtLe3o66uDhs2bMj7BuyFod34z3eehrrfhaCqAEICYowe+2ukzPxbe5zBYQmhxulHnSO540HVxBFw8Ig2WHgJVr0dNQemj8uBYyrAcVCiit8ylvj5MgYEFSO8khn+FC2qVcaBT1Iw2J6iFbaNN2PLuKi/NtoZ87FGH+qt78LERwRDURUwJdugFNkNM4OqH2cjF9mnkw+gxjCYd/pYOiHGwi/Ae9NLsDfsT3gu4oyJOCACWYgxY2wUL7gfhlkIQYCCtZZhNJs8aV8Xjd2wHj75o4THPeKzCEelDk2FX0sqxmz1voKVtqNgFRIFt6zSlNShqH+PgA8/AgBQ5JdhLIMYkyl+MhqNMYtmoijqcdnOnTshiiKqqqpiFs1KkUpd6KJbIBAgZwxROLnaapORr9U2rEyizfNjjIXe3D8XGQLKWycmGoEr/42XMYk9UVWAUmk08R2VzGYzFi5cCL/fD1mW0dLSotc/6e3tTUixqcQCXLNFPgF0qmLAbrc75gJUaDHgSq9kP1vzy6Vdo8FgKOhGqVwUQ4yZTznPBFEuopscANm5YaIppBvl1NQU2tvbYbVasXHjRpjNqWtgpNr3bDpjGGPo6elBT08P1qxZg5aWlrzPs1tG9uE/3npKF14YAxA0RHSX6MsnA3R1YL++YTaG0dLgQa0zgMlAujoiM3OTVQEhFbALEpyWmXolqhoRV0RmAOLEFzVKaAkrArz705AUlv5zjzhbEuNrI6/AADlBlFhgWIKd4vj+13JQVQ48FJzcshutdg+4/S2rJ2UzQiz/ltXZYIasjy9wKqoFH1pMkwUIMSylEGPlW/D61AJMSMnFNJ9i1t0/mZwx+9CNt9hTMAthLDBasNq8BTY+lPUsTfwS+OVtSZ8T1YGYv6eld9Ec9bdXnsATozdhXBpElaEerdZDE8bIFD8pzAuVzTiDZPk9GNSOyHPy22BMBMfldr7IlVwXs8xmM5qamvSuadGLZsPDw5BlOcbR7HQ6i7JYRs7iWEiMmQXysdUmIx+r7YT4Edrc1yKkjMU9U776KfHws3DDZUiiuqgSD76Aav3pkFJ0VNJS0rTWdVqKTXzXGqvVGpNiUw4b4XxGKwa8YEEkhzcYDBalGHClCwiV4jqJb9eoqqpe82ffvn3w+/14/fXX9e97bW0tLBZLRR1bRVEKSnWbbznPBFEOoltWA9mldcej3QTkckPAGENfXx92796NFStWYNmyZXmdj2azgG84HMaWLVvg9/txzDHHwOVyFTTuDze/CBUzugsLCjNiyH6XSjw8r2BR3SQWVE/rtWEsxtSpRPG1Vzw+K4xMhdMiQlWBsCogqJhSdiBijMNk2AKvZEFYFZCtCKIyLun1nAODwyhiUpqJwRaZG/DGvvGZ16oc6i1enNDUA1uUk2ZIdEFReZgMpf38LfzMPg2cWgQhRkwqxNiE5XjRXQ2vkljIV2NcckI75umcMW9N/h/etTwBHgrq+Wmst24HkEvamxkqGBgy1wfiOSe6xAU4GJHf9aueR/Ch91+6y2okvCelGJMuftKK90YQYFBejvo7CEV+GwbjSVm+n/woNMazWq2wWq1YtGgRGGMIBALweDyYnJzEwMBATGxcU1OTt7u3UDFmvsVPdEdXZlRVRTgcztsNE00uF3XGVHR578Xu6XvAkggvjOVWab6Y8LNwf2VMcrKSJQEGS2kukuE0Hari04F4ntdPdMuXL4ckSZicnITb7UZ3dzeCwaBuI6ytra2Ils7lpBSCR7piwL29vfpnkqkYcKWLMZXa7Ynneb1do8FgwNjYGFpbW3VxZteuXTCbzTEdAXJdkS42lPNMEOUjuslBofFTrt0ow+EwOjo6MD09jaOOOgo1NamLkGaz79kQY7xeL3bs2AGXy4WNGzcWXLTz7cEB7PJPADYOAIMaFIC0HY9UNFT70FzngTFOjDAbFIgKD0Pc6ZQxFiOyGDgFvpAVVoMMSeHhV0wJtVniYeAwKTqhJEk5SofKtKorse+BA2A3hjEpRc7dHDhMB+xQ2JS+zeE1Q1jmmIiJbackM8bCTlQbSt871BwlxnBgBQgxSCPErMaz4xYEM9w7TMkz7s9UzpgnR/8Xnf53wUHBWvNeLLG4c56nzXAIfHJbxu0ErgY7xTUYCu/DvlA3/jZ2MwJqbJ2bkXBiu24gc/wkRdWLaTYeDKgfxj4v/gaMjYITjoNBaMk413xQVbVoi7Ucx8Fut8Nut6OlpSXB0dzb2wuO42LiMpvNltV5uRjxk9bZcz5AYkyZ0NKStG5JhQoxQPZpSiFlAm3u6zAhvp9yG59/EiVLX80Al+NFshgYkrxZVS7dAUjnjMlUm8VoNKKhoUFvl6nZCN1uN4aGhnSlWqs3k+3JkEhOIcWAK12MqfQ0KmBmxUQ7ttpjWs2fgYEBbN++HTabLSYIKHdHgEJXdgKBAIkxBJEFxXITa2hummxEEY/Hg/b2djidTmzcuLHgwu/lLuCrOWK6u7tx0EEHYenSpUW5Blzz2osRpYIBapAHlNTxU5XNj8WNHtjMqeIgQJINMAgRJ4Qo8zAb1ITIMBgywmYUUV/lg1fOPnXbIpjhVyLpLvunnAUcGOMBbmZrA1RwHGAVZt7HCutSvDw0CQCoMftxWstOVBljnSKSyqMvWA+Ag5Ii/amYmLn48aP9S9mTSoix8ofiH+McpCw6kkaLadHOGMYYZBbG/cM3YFwagh0ijnT0wi7kvjAcYo1QsxBiDFw9todWYK80iUXmg/HwyHVJtxsN70n6eKb4Kby/XsxC4zrw6rsJzyvKVvhECzzyLRB4F6rNF6LO8uWM886FQh276UjlaHa73RgbG0NXVxcMBkNMXJbK0UzxUywkxpSBYgcSGtmssIyF3kGb+3qE1fQFsAxGdRYzlcovxghJ7DiKVLqivvE1YzTy+R7E2wi1k+HExAS6u7thNBpjXByz7SAoNuUWPHIpBhwKhSo6haxS0pTSkWyOgiCgrq5OL5SpucW01ZmOjg79M9DaNZb6cyi0AJ1WwJcgiNRENzkoVuwEZF7MYoyht7cX3d3dWLVqFVpbW4sWt5WrOL4kSejo6IAoili2bBmWLVtWlHFf6OnGgDg9o2zIfNLsH4tJxOJGD6rtmdtdazftIUmAqvKAQY2p/2LgZNitCha4vMi13oqNN+liTJVgw1SG2iVApL5L/Mdk3F+A18xHRCOHYMVHowEADEc2DOCw2mEIHEsYpztQrxfrzbVFd+4wNBqnZv7kOAhQcy4WHBFiEmPisG8FnhcBxuX+HY52xrzleR3veh+FqPqwzDiJgywDeTl4QqoBRt6XcTsDtwBbQksgMzuqDQ4Mip0pt52URyGqAZj52Jpu2ThjFhjXw6C+k/CczLXAg2qE9otGqurDROiPqDVfBq6ITUzKGeNFO5qXLl2ql1iYnJzEvn379IXLZI7mYtSMcTqdxXors07l3jXMA6JttdoNZDFvItPVjFGZjF3Tv0e390FkI3bwBmXWxJhAYDrzRkVGSFY9fxbEGKCwrkXRLo7W1lYoiqLXm9EcBHa7PcbFUYrK6OVktrs8pSsG7Ha7oSgKAoFAwcWAS0GlpilFk00wEe8W0z4Dj8eD3bt3IxQKJbRrLPb3nnKeCaJ0FKPJQTrSxU+iKGLr1q1Fq60STbnSlKanp9HW1gabzYbq6uqUqbX58OO3XgE4wAIDQuHEHtCCIKN5gQcN1d6s09ANfOSYqCoPfv+N/v7+THCaRJh4db9gk/t3wMzP3OpUm+yYCmYhxoBLKOKrzdG4//+1XAv62BA+tawTdebkYw6JLgTVmQUxiRkApK6xUhgqVpjHYmrGAIARCpQcbvcsnAg5iXhj4o/B8+FAfI3krNHEmN5ABzZP/RE2LoRjbSNwGSbzGk9lQIiZYEL6Ir8GfhG2BFphFBZgVNwNlrG1N8NoeA8WW9bG7i9Dao2Dk8HFCTGMGeHnN2BS7gDDaNz8vQjIH8FuPDrm8THxQ2z33Ysm83FY7fhchrnGMpsLbtElFpYtWxZzPzI4OBjjaJZlOa8GNBrzLc2bxJgSEV/tv9hCDJD6oh6UR/CR+xp4wluzHkthAQCz07FHUcJlT5ESktnmpNK5LdKlKRUTQRD0dKUVK1ZAkiRdKNi1axdEUdQro9fW1hatMnq5qaRUm+hiwFqA73Q6Cy4GXArmQppSPrnEyQoya3WWtm/fDlmWY7plFeN7XwybbS4tcQniQKFUbuJoUsVPExMT2LJlC2pqaopSWyXb/RYLxhgGBwfR2dmJ5cuXY/ny5fjwww+Ltojx1M5OjMh+QAbCRgXR4ggHFY31U1hUPwVDjs0QzAYZCgNsRgmyyu0fj6HOEgADn7JAbzYYo8QYu5BNCkeklGt88WBNhBE4hsXmGmwd78fBjWNwy3aYBAVOQ6zIotWJiUZmPFQVKHbYJUDCcssELLysi1n6c7yKjPrDfixceL+LJva989xx+L9xb0FzDCgBvDP5T7zs+TMWGT1YaxmGISGlKnvcih21hvSuKyO/BLvD6+Fjo/CLO7MeeySFGJMqbvAGfwpeejTmMYk7CG6mIix/mPQ1AOCXXk0QY6blfnikTkxJ3TjIfjG4HDrOVpL7Ofp+BJhxlbvdkZpAH3zwQd6OZnLGEBmJr/Zfqh9GMpvtSHAz2j0/haTm5jZRWAgcrHo18XJitZuAzC7WopLsE1GlEtaMKZEzJhNGoxGNjY1obGwEMNM1yO12Y2Ag0uovumON1Wqt+Jv12XbGpIMxBqPRWJRiwKWgki7UqSjGHLVUvoULF8Z0BNAcY8XoCECtGQmi+Gi19UrhhokmXhRhjKGrqwt79uzB6tWrsXjx4pLsu5RijCzL2L59O8bHx3HkkUfqYm8x9/mLd1+LNLkRgJmqLgzVVX60LHDDasqlA84MAs/gD5lRZRHBcwwK46EmEQXyIVqMMXCZz9lsfyvueDEmWjjwhYNYUT8BFQJ8ioA9QSMOsQ/rIkt0nZhYOMiMhylbdSSBxPovVj6EBYZpWPanT/FxcbyQ5b7MnJhEiOEgYyNemphK9bKsUVgPNnvacZhlCM2myYLG8soWVAvpbxwYtwrbxNUYEnflPP6IuCfhsVSxiS90JzjpIeheLmaFjz8CU3IbMqlgPmkzGvGd2MeUSGyuQkJQHYdNaMx63oXGJaVEc5W7XC4MDg7i2GOP1ePjrq4uBINB3dGslQZI9V78fj9sNlvS5+YiJMYUkWJW+8+GaJutyiTsmPoN9vj+nOdoDCbeAlEtsyoCQJ2FTk4+b6LCrygCWKLjtiiEy+SMyUR81yCv16sX39q9ezdMJhNqa2shiiIsltlxSmVDpQpG8fVsCikGXI75VSKKohS13kuyjgDxAlk+HQGK0Q1gPq3sEEQhRLuJi9XkIB2CIOiLWaFQCFu2bIEoijj22GNL+rsslRjj8/nQ1tYGo9GIjRs3xly/s2kUkA0Pb90CtxSMaAH770mtFhGLmybgsqdPFckGm1GEwrRUnuJ99sZoZwEXkXjSfQLakYrv1KSlKYUVHoJ5PGaOMhMwHHahxTKVUCcmnvzFGBUuIYQpZeYm1AgZ9QYfwqoB2N/WOb4phsBl3peZCyeIXxx4+NWNeH1yMo+5xs67yTyNeoMPR9j6YecLi/lVmMDzLGUKHGPAPmU1ekQbwqw7r32MhHuSjJsYP/nFP4GJt+lzEbnD4FY9kNO4YaIJq70IKwMwCYv1x3zyTFcmvzyUkxgzVxbcgMj9iM1m0xeLRVHU47LOzk6Ew2G9c6yWbq7V3Mo3fvrf//1f3HTTTdi3bx+OOOII3HHHHTjmmGOSbitJEn7xi1/g/vvvx9DQEFavXo0bb7wRZ599dsx2Q0ND+O///m8888wzCAQCWLlyJe69914cddRRWc+LxJgiUQ5bbTzaRd0vD+KjiWswJaUuSJUNRs4MsdwWFQAyK1X+bGpMRkG7bs3AAEXmYDAW33kxW86YdHAcF1N8K7pjzfj4ONxuN6anp3WhIJ1KXU4q3RmT7nefSzHg2tpauFyuogoTc+VCXco5JhPIvF4vPB5Pyo4AydxLxSjgS84Ygoj85qNrCJQzfhobG8OWLVvQ0NCAI488suSFv0vRTWl4eBjbtm3DkiVLsGrVqoTzUjH2yRjDrz96O/KHABgEGS1NbtS7fEVYwGJQGQcFAphaAjdS1AQVpsAqWPSCvsnh9s+Ki7mmC5yKsMJjRKxCMrFoPOxEo8mL0XBVTJ2YeGQmIGIvyg0LJ0MBDwsvIqSaYUIYS8xuiMwYU2w3fmbxTpl4TFx4f9PumVfyMMItH4t3p9M3/8gEDwULLVNYaR7FKvNIQgpVPnDcYlj55O2nfYoZnWIrvCoPZKglkw6PvA9T8hhchgb9sfjYJBh+FmroevAcoDAHpvk18Mlbct6XT9qMWuHzM3/LA/q//cpeNGB91mPNhRgvVTF2s9mMpqYmNDU1gTGGUCikizPDw8PYvXs3HnzwQWzatAlmsznn5iR//vOf8e1vfxt33XUXPvaxj+G2227DWWedhZ07d+qCUDTXXHMNHnroIdx9991Ys2YNnn32WVxwwQV48803sX595DPxeDzYtGkTTjnlFDzzzDNoaGjA7t279U6g2UJiTBEoVbX/TPA8jynuTfSMPACZ+Qsez8SbZqWIr6QGgSRV20uJw24DwpMJj6uSABjzs9mmo1w1YwohumONqqpQVRU1NTVwu93o7OyEJEkJ9WZma/6VdNyiydV5kq4Y8M6dOyGKYkytk0KLAc+VAr7lFP14nofL5YLL5dJFyenpaXg8nhj3UnQ6n9FoLHiegUCAnDHEAU2pmxykg+M4DA0Nwe12Y+3atWhubi7LfovpjFEUBZ2dnfoqb7IbCm2fhS5i/OGjDzAlhcCZFDTVT2Fh/RQEvvAba8YAiQkJLpRiwsc4WBTYBVN6MSbqbTHG6WITY8CIWLXfQZLsZRw6fQugZIhna40LIWNPttPXMfEyVAgwcwokJqHV4oZfNcEqyAgpM7dzXJzgkU4AMXFSQmFkgbNiWDwS7b7ChBgzH0azyY119kHUGzJ3PMoGm7AOPiVR8FAYh95wAwal2oLqC0XTHfgIR1adqf8dLXSI0ruQgt+EwDG4FSOCnAVqHkIMAIjKzEK6zEIIquP6335lKKex5pIYkw6O4xI6xy5evBhjY2N47bXXMDg4iDPOOAOnnHIKTjnlFJx66qk47LDD0o57yy234Ktf/Souv/xyAMBdd92Ff/zjH7jnnnvwve99L2H7Bx98ED/84Q9x7rnnAgD+/d//HS+88AJuvvlmPPTQQwCAG2+8EYsXL8a9996rvy6frnUkxhSAVu1fCyTKKcQoTMSE9QFMG18pWmdoI1e69Ih0yCwEoNwrxIkHjbHS1Y0JV6AzJhOCIOhFURljMfVm+vr6wHGcLhJo9WbKQaUeL6DwNKBkhWjdbnfRigHPhQv1bM9REARdeAFi3Uv9/f16RwAgsipSX1+fV5FPn883r3KeCSIXytHkIBXBYBA+nw+CIOC4444ra1ezYrW2DgQCaGtrA8dxOO6449KeSwpNU5oWQ7hz67uorvdi8QI3zMbCV+0Yi9SFibR6Lu3nHt2GWVJlWPj0sW70zbzKOPBgMHESwsycIbmIZVXnxszZIKf4ONIV99XSq1RwqDEEMaVYZ1KSonYZn6YU/7eGkZP0LTQMnB09wSPQGSisRoxDCGKNdR8Otg7DyqeOf3PBwC1AQElMO5qQ7dglNiHEinsP0xOMFWO0+E6SdyEUuBwyU7FXNUa+Lyz/rrBhtV//dyRFaebz8st7cxqr3ItZ+ZBPXRuO49Da2oqrr74a//mf/4mGhgY89dRT6OjowAsvvIBrr70WJ598Mp5++umkrw+Hw/jggw/w/e9/X3+M53mcfvrpeOutt5K+Jlm5BqvVitdff13/+6mnnsJZZ52FCy+8EK+++iqam5tx5ZVX4qtf/WpO74/EmDzR0pLef/99rFy5ElVVVWULJLxSLz5yXwOvMb9cyFQYitjrPhcYVJh4M8Jq+dKVkts2OShyaW4C54IzJh0cx8Fms8Fms6GlpUVP7XC73RgZGcGuXbv02ieaQFPsLhTx86lEil2TJb7GT6HFgOfKhbqSBKN495IkSRgbG0NnZyf6+vqwY8eOmDba2aTzFZLzTBBzHVVVEQwG8d577+Goo44q6bUinpGREXR0dMBoNGLJkiVlby9fDGfMyMgItm7dikWLFmHNmjUZz5eF7FOUZVzw7O+xeMUQnNbCYzTGGFRwkNTi1oXJFolJsPDZf+aMcTBxEmosQSgs3XldBQ8GNQuXt18JpewgGlBMcCStqaLqNWgMUOEyxjp7uBT/jv6bMQaJGWDiFRghg0Os8GTgbNjuPwx7QvkLCwDQYJzC0Y49WGYaB8cBXsUEM1+YgKcyHsEgD8E6kwUgqgJ2h5swJlcVNHYqBkM7EFZDMPGRG3JVVQF+DD7/FzClMoRgRDG+w5Iyk5YUXS8GAHw5OmPi4yfG1Jy6MZWDYjQ/AIBjjz0WZ511Fv7rv/5Lj8tSMT4+DkVR9IVOjQULFqCzM3mJj7POOgu33HILTjzxRKxYsQIvvvginnjiiZjGOT09Pbjzzjvx7W9/Gz/4wQ/w3nvv4aqrroLJZMJll12W9XsiMSYPoqv9+3w+yLJctpvDAf//YdvkzVBY8Wu7ZFNlvlSYyyzGJC3fxgC1VGLMHHTGpCM6tWPZsmUx7oHe3l50dHTA6XTG1Jsp1g12JR+vUhbILUYx4LmSplTJczQajaipqQHHcfjYxz6mp5Z5PJ6Y1LL4onPxBAIBqhlDHFBobmJZlqGqKqampsp2PldVFZ2dnRgeHsYhhxyCkZGRsuw3Hp7nIUn5OQVUVcWuXbswMDCAQw89FAsXLszqdfk6Y7Z5+vHf7XejaZG3aI0NRqcdcDnKWydQZTM3T6IahtOY/voSfaiMvIwaSyCtEMNDgZFTIbLsRMVJ2YtqE2Dhq2DkLPAqo/pzIdUIR0JBQ0BReTCBg6ICC8yJZQlSuV+inxOZAWHVABsvguNUsChFSIAJbd5DMRwuJJ1IxSrrKI51dMMlzIhFIdVYsBjjMB4JHxcpissYMCxXo0dsjKmVU2wUyOgLbcUq2/7W09w0ZNN/Y1RVwYq4X5mNQWVB8JxV76SkEVByd8Zo8UZQmcSUPIwm89oMryovhS4K+nyR72h0/GQ0GrFo0aKC5xbN7bffjq9+9atYs2YNOI7DihUrcPnll+Oee+7Rt1FVFUcddRR+/vOfAwDWr1+Pjo4O3HXXXSTGlIp4Wy3P8zEdjUqJrAbRMXkThgL/LNk+hFl0G5g5MxL7G5WQZDm0pRRj4pwxiqpCKGNaW6mJdw+Ioqin12zfvh2yLKO6uloXCvJpJRxNpR43VVXLWjMq12LA5ZxfvlS6GAPEFqDLVHROlmW91pLT6URVVRUMBgP8fn9eq/K5dAOI5k9/+hMuueQSfOpTn8KTTz6pP/6lL30J999/f8y2Z511Fv71r3/lPDeCSEV8kwOtUG454ie/34/29nYAwMaNG2Gz2TA2Nhazwlku8nWphEIhtLW1QZZlbNy4MSchN9cCvm7Rh19ufwQD8g5UO4snlk0HTRj3l1+MUdjMew8qIgym7G4ELUIYC2y+tEKMhQ9D4FT4FXPc45Eiu8kIqSJsfA1CSj0cRg5eRMQYWQUkNXZfjDE4hRAUjofKGGoNgaRjRteFiYgv8dd5hoBiAcAgcGpM3RsBwHvewzAuJR87G3jIOK6qB4dZh2CI694UVE1wFVBM18Kvhk9uAxAp0LtTbMK0Wp4U3+7Ah1hlOxqy4oF1yTXwI4hSOLrCSj8shtUJzhiZBRFSPLAIqQvCBpQxhBQPaoyrYuKnTv+zqDetKPpcC6VQ93MgEIDNZstpjPr6egiCkCDCj4yMoKmpKelrGhoa8OSTTyIUCmFiYgKLFi3C9773PSxfvlzfZuHChVi7NlbsOvjgg/H444/n8I5IjMma6JbVwEx+c3R7xFIxHd6ND93XwC/3lXQ//CxYRjWMfPlsygDAWPLPTClTzZiAKMFpNe+fS+U6PfLFbDZj4cKFWLhwIRhjCAQCer0ZLb0mut5Mtm20tWNVqYLCbLaOzqYYsMFggCRJmJycLLgYcKmYK6lUyeaYrOic9t33eDx45JFHcMcdd2DdunVYsGABBgcHcdBBB2X9OeTaDUBjz549+M53voMTTjgh6fNnn312TAG6XLsUEEQ6VFVFOBzWbxS0cyTP8yWPn/bu3Ytt27ahubkZq1ev1n9r5VpIiycfMWZ8fBzt7e1obGzE2rVrcz4/5nKce3wjuHrLrbCYQxCKfHkYmnJBLUG3pEzILLKAauB4SCycMdJljINZCKPFMZmmGK8KhyEMHgw+2Yz4G/QaQxB7w6nPowG5FvvCI6gztUTNk4cSVchYUQETp8LEyfAxKywIwyIkbzCRtn01x4ExFQFmgoWTYoQYAxS8O304xuX8BTIDJJxd04GlZnfyueXVxjsCjyqEmQeqqqI33IABua5oBXqzYZv/NRjV57DI0APBIKNUqXWSOggLVsd0UtLwK8NpxRiP1INO32M4pfZGAPvPbUxGp/9ZHGfMrXZJOSg0Tcnn8+VcL9FkMmHDhg148cUXcf755wOIXJdefPFFfOMb30j7WovFgubmZkiShMcffxwXXXSR/tymTZuwc+fOmO137dqF1tbW7N8QSIzJSHS1//hAAkDJxZg+3+PYPvlrqCj9SgI/i/e35a9Xk+ziwEFVyuOMCYQiYkyligrFhOM42O122O12LF68OGl6jdVqjal9Uur2oqViNsWYeJIVA966dSskScLWrVsLLgZcKuaKMyabYCL+u7927VqceOKJ+Oc//4mtW7fiU5/6FOx2O0455RScdtppOOOMM9JW4s+1G4A2189//vO44YYb8Nprr2FycjJhG83dQxDFJFOTg1LGT4qiYMeOHRgZGcHhhx+eIFYWs6tRLuTiUmGMoaurC3v27MHBBx+MlpaWzC9Ksc9sF33+p/MBSIoKbXlEUQGOKzw+nApYoHA85BLFWOnwixHHh5U3QUI4uTM6CgMno8nuSynEmHgJFl4GxwEB2Zi0u5LDEALCKlIVh9kXjtS3YFGuG4UJUMGBsUh3oCqDCJ5jCMMEAxTUG1OnEKUVY6C10+YgMgNUlWFUcmFKscOEGozL+X+4zcYJnOrqhMuQ2vlSk8LNE43CIt+x+JmYhKXwyx140bcWVl4sqxBj40Qcb++GgZOKlqaXCq2Ib7IaMX5lGHU4JOVrJ6UejEvb0B/cDCBybusNvoWg6oGSpkTCbFGMmjH5pHh/+9vfxmWXXYajjjoKxxxzDG677Tb4/X49nvriF7+I5uZm/OIXvwAAvPPOOxgaGsK6deswNDSE66+/Hqqq4uqrr9bH/Na3voWNGzfi5z//OS666CK8++67+P3vf4/f//73Oc1tbt7xlIl4W22ybkmlCiYk1Yctnp9jX/Cloo+dCm42+lrvp9z1aliS98oYwEolxiRxxszsd/45Y9KRLL1GS+vo7u5GMBiMqTcTXXODnDH5Y7VaYTKZ0NDQgEWLFhVcDLhUVFoB32Tk694xGAw4/vjjsWTJEvzmN7+B3+/Hhx9+iJdeegmPPPIItmzZgt/85jdJX5tPNwAA+PGPf4zGxkZ8+ctfxmuvvZZ0m1deeQWNjY2oqanBqaeeip/+9Keoq6vL+f0RhMZsxk8+nw9tbW0wGAzYuHFj0nNZOVw5ychWBBJFEVu2bEEwGMSxxx5bULHvbPf5r70fwIshgJtxdIiSEQKvwGwsTLganq4CxwOM8VAUoJzmRy3+4iQGGIFAKHXNRSMno9aRqkaMCrsQhsAxcBwgKgIklvw2ysTLMHIKpAwtu1UW3Xabh8Cp4KHCaYyNEasMobSCgIFjUFRA4PerZ9EwBq9igwEywIDOYDOsBhk8xyCxVQDyaQaiYpOzG0fYBtK2zgYAmyBhQrLCaUhWmBhQGSDCCDOTIERN3WY4El6pDa94V8OvWuDgQyhEWnAJfkwp2dzEMxxhHsJi8zhUVp54U1L6EVTGIbNE4cod7sdCs5Qyg2BK7gUAbPHdC6NzI2QchR3+fwAAFJb8mOeKwmQIRVo0L5YYk+vn8m//9m8YGxvDtddei3379mHdunX417/+pS9Y9vf3x8SeoVAI11xzDXp6euBwOHDuuefiwQcf1O9dAODoo4/G3/72N3z/+9/Hj3/8Yyxbtgy33XYbPv/5z+c0NxJjUqC5YaJrAySjFBf0yfB2fDhxDYLKcFHHzUz5V4k0yl2vRk2apsRBKWCFIB3xzhhRkiErasEtJ+cDBoMBDQ0NaGhoABA5AWppHUNDQ7qDQ6t7UslUshgDIMbdV2gx4FLPsZIpRjBhs9lgsVhw/PHH4/jjj8e1116b9lyQTzeA119/HX/84x/R1taWctyzzz4bn/70p7Fs2TJ0d3fjBz/4Ac455xy89dZbFZ8uRlQm0W6YdC2rix0/McYwNDSEHTt2oLW1FStXrkx5LhEEAeFwcW5UciGb1tZutxvt7e2oqanB+vXrC3aKZhNnKKqKe/b8FSYTYBBmPpOIk4WhkPjQ7bdC1aw1jIOi8BCE8sWbMq8CCuC02DCh+CGmSMkxcDJqUxTrNXESLIKs6xwK4xBSU6fXGzkVZl6GpKRPwZei6tlYeQkLbD4Y+Nhjw4HBLmT+rkoQYEJcGhNj6A/XYiLsQG+oDgwCllgnAMjwKyYoanZ1YqJbbjuFAM6p7kCjMftKjz7FklSM0YSYyLuM9HcCACPfgoDciTd8KzGlFqfQfbPBk1GMqeH9ONbeC55ToDLAwKlpUtWKR1gdSKgXAwAWvglPjG7HwyNX4/TaM3FW/TkJ20xKewAAQXUMocaX8aKnB6PhXQAiIkoxGBF3YYF5FQSu8JIShaai5+uMAYBvfOMbKdOSXnnllZi/TzrpJGzfvj3jmJ/4xCfwiU98Iq/5aJAYE0d8tf90QgxQ3Lxjxhh6fY+ic+q3YPEn1LKQep/RJ+JSUO56NamcMVBKc9KNrxmjKCrCUvm6cM0lLBYLFi1apNfc8Pv9cLvdmJiYQHd3ZAVn165dqKurQ21tbUXVt6j0bkWp5pdPMeBSpZLNFTGmkDmmynku5vnA6/Xi0ksvxd13363XEUrGxRdfrP/7sMMOw+GHH44VK1bglVdewWmnnVa0+RDzn/gmB+mEGKC48ZMsy9i+fTvGx8exfv36tN95YPbSlNLtlzGG3t5edHd346CDDsKSJUuKck7IJjXqW+/fC5MpkmpiMcr7XRYAzzOoamHn431ep56tw1hE4DGVcfEvpIbBQYBZMAIKYLAYgf0aRFgWYDIoMHAy6i2B/ek80aioMojguJmiuIwBAdmEdPVDeI7BxMvIZDgPqzJUxsCBQ40xmGBqUVRggSm7DkcKEyBEdVViDPjAuxTbAotjthNVA5wQ4VfMmJbdMHMGyBnuOfyKCU4+jIOse3GSc2fO3ZFYinQtVRdhAAU8DFAAGAEY8ZZvIcaVmbbVKrcAYJM57TcaqyAhkqiWXFA43DyAVvO4nnYWSRorT4weVvuhxHVSMnB2bAu0wKtGPv+BUH/C62QmwqcMw8hVwcS1YlTogm+/EAMASpLOXPkwJvXAKrhQY2wueKxixU/zCRJjosjGVhtPsWy2YWUK7Z4fYzT0RsFj5QtL86NVVR4cp5Ysb7Lct16p1GJVKc0bDCuxx1ZWVISkyBwOdGdMOjiOg8PhgMPhwJIlSxAOh/H666/DbDbrK6B2u10XCaqrq2e13sxccMZkM79sigFXVVXpzpliFQNmjIExVvGOjNnIec61G0B3dzf27NmD8847T39MuyEzGAzYuXMnVqxI7LSwfPly1NfXo6uri8QYImvimxxkcz4oVvw0PT2NtrY2WCwWbNy4MauC8JUmxoTDYWzduhU+nw/HHHNMUV2gmdw4j3dvwT62DZr3keOAkGiE3SLBbJQRDCfevDKWmA2TjHGvDSy64AzjIKsC0i3+FQOVMfD7J8jAYOFNMPKR2EBUZ5wxvqAZdQ4v6q2JQoyBk+EwiPuFhJn3EFSS14mJhuMYTFzm73ZAmYRFUGAWkm/rEEQIGdKANMIKj3HVgVpjpPV1Z3BhghADRMQYAPDLZihMQb25HvvC+wDGodHciNFw7DVGVYGQxOHTDR1YbcmvJXyTSQYHIWEhVIk6jpooYzMcjtemJjAs1cR8x4x8I8LKZF77F6BABQeXEMCYEv/bYjjRthuLDFMIQ9ArdBq4yHenHMjqCIJSdJMWHmPyERgUZ4oi+5REUW5KGoDLeDhGw72YZLsS9MFMNWNUpoDPokzEeHgPqg2LiibGFLKImm8nykqGxJj9aLbabNww0RQjmODkDyEH7kUVPBiNsumVG5Ymt5DjAFkRYDSUKse6vO9ZRZKq6AxQS5Sm5Bf9ePvtt1FbW4va2lpIsgxRUir6xr0S0QL8ZcuW6Z2BtLonu3fvRigU0kWC2tpaOJ3OsrosKl2Myde5k6wYsJZKNjg4WLRiwLncyM0ms5HznGs3gDVr1mDr1q0xj11zzTXwer24/fbbsXhxYpAOAIODg5iYmMDChQuzf0PEAUumJgfpKDR+YoxhYGAAO3fuxLJly7BixYqyxm75kEyMmZqawkcffQSn04mNGzfCaCxud8l0aUrv7RvCnb2Poakmdk6KykOUBJiNCoJJXiqrHASOpXVMMwaM+J0xq22MAYpcerGdsdhqsBbeCAMXmUhAiS42q6LR6k8QYhxCCAKvJjg60tWJ0TBCAsDBGOUeWWCchJmT0R+u3z8/BpcxBKdxLGVxZKYyVJkzt4T2yyZMK1YInKoXyx0I1eLd6eVJtxcVIySVR3j/e7YKkRvbFktL0mYaNQYfLl3yJmoMqWvtZMLCT8EiHIVp+aOYx6ML8qrgYOLW4O3pUewJL0gQ+4JK/vuvE3wAODgFEWP7P5ZDzHvRG67ByfbdcAn7j7M6870RwCAyF8D5895v9qgIKFFdebhjscUX250qWoyR1TDenvoNeoPvQWKpvyPpxJiwGsQO3zM4ourTGWc3IfWhUV6ZcbtsKDR+CgQCJMbMN6Jttcmq/WdCEASIYp6djpgKPnQ3+NCdcEGBSwDchla45fH8xisQlaV+HxzHIEuGEoox5Q2KIieo2BoYDCjYjpsKzshjxYoVcLvd2LVrF7YNeDDWWI3FDa6Y3HoiPfEBpdFoRGNjo94pI5lIEF2U1mazlfQ4V/rnWKwUIKvViubmZjQ3N4MxVrRiwNGuxEpmtnKec+kGYLFYcOihh8a8XktD0x73+Xy44YYb8JnPfAZNTU3o7u7G1VdfjZUrV+Kss87K+/0RBwb5uImjKUQQkSQJHR0dmJycxIYNG1BbW5vT6yuhmxJjDP39/di1axdWrlyJpUuXluT6keq97vZM4GubH8XBy6cTnhN4FWHZALNR2Z+iE4us8gCvgk+zkDbmtSfanhkHqQwdlVTGx7RUtggm3SkTUsPgYQDHyVjTMBZTE4SHgipjKMENA2SuE6PhMERiaWOUM6bWEMDRjl48MFYHHioaLT44UhS0BSJulHTdkxSVg1u2Q2IGWARZd9YInIpxyYGXJ9ckzF8jrBrgV2bacSt6XGUEx0V/Ngzr7X34mKMXhgzdmrIhrO6EwFVBYdP7R48IMDNw2BXisFtMFGIAwKdMw5bnZbd6v0hl4WaO+UrTGA4yjcDCRwkW3EwHLB5WVBkPwmScgFR8eKj8sdgbjNR+swrr8S93Yptwn+IDYwxt3gexxfsUlCzum6IL+HrlERg4M6xCNQBgl/8ldAdfzyjGKEyGRxrEtJyfKyqe2awZU6kc0GKMqqqQZTnvQEJ7TV4XdHUcgv/74OW3Yx628rPTwQQAFJZadeY4BrmkqxnlFmNkxIsxYBxYqQr4MimmSK344W4YISEY9MPv9+P111/Xb15ra2uzslkfyKT6nSYTCdxuN8bGxtDV1QWj0agLBLW1tUUvSjsXxJhizy9VMWCPx5NzMeADxRmTb85zrt0AMiEIArZs2YL7778fk5OTWLRoEc4880z85Cc/qahaTETlkW2Tg3TkGz9NTk6ivb0ddrsdmzZtyus8PttpSrIso6OjAx6PJy8xKReSOWOGfdP49D8fxdLF40mdGRaTDL8YOa7JnlcYD6iAMUV6DWPAWMCRIMYwhhLHktr8uJjKICbeoE+FgcEpCLCapmIcMVZehFlQktY3UVUgoKSvE6OPI0Ru7qMFDKcQhE2QcJyzG3vlahj49G5wKx+GkU/8fnplM7yKBUZehcAzCHHpXpOyFe9NLU1ZowWIpAZNSTP3GpPSNBy8A32hIay0RRyTFi6Ms2o6sNjkyfh+s0VlXjiEDZiS2wDsr9MYdzz3SmFwXPJro8TCMHF2hFnuThUrHxElTPvdSjW8DzZOTPg4DWAQAXAwQ+C84Epc24jnquFRV2FvsAcAYBOW4wVP8oVxBz+GR/Z+DmIaJ0w86v6SDGPh3fi/sWtxkO0UbKr5Ghhj2O5/BtPyXvjkcTgMqWtsuaUBqFAwrRRHjClG/ETOmHlAtK02U7X/TOSzssNJb0Hwfx8cm0h4zsKXp2NJMmQWBJBcDNLSlEqFWlDDutyJqMW2hMdZiZwxclyNGoPRCJfDiUZbHfr7+3HQQQfFdLKxWq26MDPbdVAqiVzq60SLBK2trVAUBVNTU3C73RgYGMD27dtjitJWV1cXXKukFGJHMSlHgeHoYsDLli2DLMv6cc9UDDjXNIfZQlGUglIJCrHZ5tINIJ777rsv5m+r1Ypnn302r3kQBya5NjlIR67xE2MMe/bsQVdXV8FOktlMU1IUBW+++SasVis2btxYcuEzvoCvJxTEeU8/AqtjGi578hs7gWewmcL755x4Qxppycwj1ULayLQzqRADAFIJY8nIflhELIqam4kz6EVueCiwmdyQ9gsxHFS4jCGwqGKy8YiqKWOdGA3rfqeFECXG2IXIDfYhtiGM+dLXA1JVhmrzzOKorHJwyw4ojIdZUGBJIYBxTIUBCsIs87XJp8x85yakSay2L4Xb3wsOHBabJnB69XbY+eJ3Gwur3cD+cgxqkmPtMgSxV65J+XqL4EJYzl2MMXAqVERahy82uLHR1pNUV+PBoDIOTsMyQN2CUi4UG/iD0Cty8KuRwr1mvg5vT1clpB05hQAWWzwQOAYxx4oOCsLoC76LFyb+BwwMnf7nsdZxNrzyGKblvQCAgdCHONhxZsoxJsJ7AKBozphipHlrbvj5wgF3hxdvqy1EiAFyvKAzBXzof8GH/phSbWWh8ooS0agsBA52sBRzU0poLVXS1KspBTKTEDFJRheWA1S1NDeBDAySKusF5BRF3V8zxgiO4+ByueByufSbV61YqlYHxeVyxdRBqfSb1VKTz/sXBEE/hkDyorSFHue54Iwpt+vEYDCgrq4OdXV1AGaOu8fjSSgGbLFYKvr4aSiKUpB7bT7abIn5T6FpSfHkEj9pBW69Xi+OPvpoPeUuX2bLGTM+Pg5RFLFixQqsXLmyLOe76AK+QVnCx596CH45hEMXJKZCRGMQ2P7/Jx4nhfFQUyyOqCowEbQlSVECAA5SlDOmFNdMxjjET83IC2BMjaQhmUJ63RczL8EqhNM7SRin11fJBhMfWXzTxBgTJ6HGGEmTEfQ24Sm6CzHoBXinZTN8ihVGXoGBZ/s7DaWAqVhp2Ye+UPouYjPMHHMGhq5APwAVK8wvYZV5V8o6NoWisElYhdUIKl0xxXs1HHx614cRuV83DZB04aeW9+Mge+pSEBwHmLkWGDkrVABchgK4+RISj0AfxvT7LQFm7A6ugEeeSRk0c2EstY7vd/Pk94EYOQeen7hR/5tBxduT98Vsk0mMGd/fOntaHs1rDvEU2k0pEAjMu/jpgBJjom21HMcV5aYk62BC3QfB/9/g5Q/TbsbLbFY/FTNvRkhNnq5UWjEmz7o7BWDkjJCiHCsMACtRNyUgkqpk3P/hKipDWJIBGBPcHgaDISalSauD4na70d8faW2nCQYHWkpTMTtPRRelZYzF1JvRjnN83ZNMQeNcEGNme37JigFrotjAwAAURUF7e3vBxYBLSTFWduabzZaY3+Tb5CAd2cZPbrcb7e3tqK6uxqZNm4pS4LbcYoyiKNi+fTtGRkYgCAJWrVpVtn1raUqKquLjf38YE6EgmhonYTFm19HIwKtQWWy6kqJyeg2WePZOVyXVGhiLbC8pM0FuSDTCailuZ6XIjXfs3HiOg8xCqDKFIDMDoltWpxNiVBbpOpQLWuFenmNwGQKoN/lh5rR274CVCyPIksdtBibDE7aB8TzMvAKLkPnYMMaw1DQBlyGUsbhwKuz8NM6rbUej0ZvX63PByDsRVJDgQvIpZmwLtqR9Lc+lzh5QVSQtKN1giBTvXSRMoYoPIZOwYePrwLMgVKTvMpsPHOyYlA7GXgzGPD4WOBS9UkSIESBjqWUCNkN4/1zzO9fWGw/CVu/TCY8Pie0xfw+LW6EwCQKX/LyqiTEyExFQJmHbX3MmX4oRPzmdzoLmUGkcEGJMMW218WiW03Rw4VchBK4BxyYzjlfnqkJXOQp3p0IWUvaZVkqUwgMAslp+McbEC5CUqAsdA1iJnDEAIKlhQIikgcm6Mybz/kwms14HRVVVeL3eAz6lqRR1T2w2G2w2G1paWsAY04/zyMgIdu3aFVP3pLa2NukNQaWLMeVIU8oVq9UKq9WKRYsWwe12Y/v27aipqSm4GHApoZxn4kAhuskBULgbJhqe5xEOp77ZYez/Z++9wyS7CjPv37mhcofq7unp6ckzGs2Mcg4jgj8jJIyxCd4PcEZri12MvNiyjcFgHJZdre01CAM2Xj6DCUvwEoQXbIEQyEhIKE+e7smpZ7qnc1e86Zzvj+qqrpy6qrtnPO/z6IGpdG/dvnXvOe95g+L48eMcP36cK6+8kg0bNrRs27quLxkZE4/H2b17N4ZhcOONN/LSS9UX5VqN7Dj1Ld/+CiOJOUzTYbB3tu73CwGOq+EzFo6XVOX13VLCdDqIqHKbcT0td6+MpwIE/LGm/64h3SLpmeQPXKUSJbHCQrnMeMcBgSlcwmUqq4uhlCLp+italyohq4gxhGSVPzOgzw/zDWo2Ka+UjJESbM9ACo0OrV5FhmLAmGGVL0OiOLLx+9LVobP8P51DuTyVdsOTGXuMp7RcUO+EE2F3ciMu1fe/mlVs1g0S9ZUuJnfpcTYZUwREmRbVMtCERMosWdJ8g1MxDG0jZ+wIs7KQiJHuTexzEoBko3+KTjPFYkgYgH7zasbsobqquTv1TZxNn2RjsDxBPGkvVG7PuRcI6d0cTRwhJdNcEbqCoN7YmOxygG8pLvlZW6tltcWoekNXDlrqo+jWF+r+PN8yz+N0WYWMaaMyxlFJYGkDI01hAIUkkGzrd1y4ubqezEl8a6k9UpZNJJS5cWuaVpelKRqN0tvbe8lZmlqpjKkGIQSdnZ10dnayadMmPM9jZmamIPeko6OjIPdE1/UVTcZks7JW6v5BZh8Nw2DDhg2LDgNuJxZr90omkwwMDLRwjy7jMlqP/MpqWLytuxjVlDGWZbF3715SqRS33347nZ2dLdsu1LeQ1gqcP3+e/fv3s379eq688krS6fSS26OEEPzP80cYSmfaedavmUSvESBbDM/ToICM0Sg3zxuZ7apIxGSVMSDwPB3DkKQsH66rYZrV96e86kEh0Qjrznw7UHbfCrNfBJIpeRhduPT5kiQ8X1U1TBYzdqhqdXc5KMV8w5QoaKHKD/MNak5JFIlUim4jxeH06vnJeH3o1hKsDyyE7DZipzJwubdnP9sDrckCqReOHMGWgwiR2e/TVg+H0mvrIr1cWfk366CTdE1CxsJY24fDtf4xjDKNYJWg5Amkylj4VJVik0aga7cwlL6AW5QVGtKv4dHZBGt8M5n2LLE4EgZgte8azlsHa75OoNGhX88LsbMMBM6UJWPm3AvYKpn37zFsqfHpkU+TkmkGfAP80ZY/amj/LiuLS3FJkzFSSmzbbmsoZMXBhHcGPfFeNG9/Q5+Xz54vByKhDhJ2+RUTr42ha56yESqAauCCuViYxXdZJdqqjLHlwg3CkxLXq2/inky7REqzhoHqlqYzZzKhYPktTStBVdAKLDWhoOt6Qe6JZVk5EuzQoUM4jkNXVxee5+XCWVca6ZElslaaMiYfxSsmiwkDbidaMZgIhSr8qC/jMpYZ+SUH7R4/lSMmJiYm2Lt3L729vdx4441t+U2326YkpWRoaIhz585x3XXX5WyZWctQq4h7x/UwjerXor/e80KOiImEk/R2JKu+vhwygbgLkGVyWaSEWStQQMYolcvNLSBvXE+bJ2NMbNfANKsrQRyp4S8KEvZrDq4ykAg0ZE414Sktt28CSbcvhScV/cH4/DS39nGfc/wNEzFCSfr9c2U/vqBmW5RajwwklqfjM9ySY10JIdJcESzM8ajXprTanOENPXvp0lun/KgXrtLYkwpzVXCa4fQaTtqr6n5vWpbPlMn+lBPST0A6aBp0aknujRxqiIgBkHmEiWqiuSkfAh+WuJXjqWMlzwW19bw0N8fV4an538girwdKI+xu4LyqTcT4tQ4suZHd8YxK57x1tuzrJubDe7M4nd7D87M/IDX/d0jKxq4lWadKs+NQpdTlzJiLBdk/drYtqZ3tHOXIGGF/Fz35ZwjVuPfSWOJWoWJk1CLl0U6bEoBP+LGov7Jt0dvTigYw7bYpFSljXC9z96il9kimLKA+Fri42nlubo6pqSlGR0c5fPgwgUAgR8xEo9GLztK0UpQnfr+fgYEBBgYGcjeHbN7M0NAQhw8fXnHWmouFjKm2f42EAUejUTo7O9vyfS97ni/jUkW71cT5KB4/SSk5evQop06dYufOnaxdu7Zt286SMe24pySTSfbs2YNSil27dhUQr9nrUSu2q5TixIUprhysPJn9txMn+N7pk2TcH5INA6UtnvVAKlHy72Ilw9np7hJVjPIEaAqh5StjMkobT4LtmjiOAcFaZIyOv8gYZWoST2asKyHDJu5mFMSe1DLNSErS4bPxlMZAYC6XeeN6IhdOXA5p10DmWWjqgV84rA7OzTcplb5Ry2OifEUWJE/CKl+cA7FBNA3cOrgDE4cdoXMlj9t12JSuDp7hNd2NkxSLhSCK0rZwKjXCFYFRXkpuZNyt3ixVjISMY5T5u8h5y5kCUtLHNt8YrwodbehvWA6KJBCAonOvgGSsAF0MMOquYdItJWKUinDWnqU/0BpCWFM+AnI1MeN0zdd2Ges5mTKYzmtHOmedKfvabF5MBoLjyTnO2wvXkLTX2JwtS4Av1uZ9qY2fLq6ZWB1YyoEEFA0mlIWW/Et0+5+a/jyjxWFRjUIXlX8gbhstPABBI4jlLh0ZoxcNLhTtzozJV8YoLMet69xMpZsLtyvX0pS12hw7doxUKpWbuPb09NDZ2bkiiI5qWCqbUiMQQhAOhwmHwxw9epRbbrkF13ULcn2yJFjWWtOKAMpGkW81WKlodMWkWhjw2bNnkVLS3d3d8jDgxZIxl+LKzmVc/MgvOViKivn88VMqlWLv3r04jsMdd9zR9sF29ve72PyCYly4cIF9+/YxMDDAjh07Sj47e31rRbOd7XmcGJuuSMYkLJv/8sNHYX7S3dcbIxxozYKfVKKAoHG9jJqkhIxRAuUJdE0WKGlcT8uQMIBlm9TK5vCUVmBVEkhsmRcELM1MU5KRYiodREPh11yCms2qQKG6Qc5XK5eD6wlS0qy7TUgpRY+ZpMeXRAgwStTtEgNZMHH35b1GKUWHbuHITCZP2jNKFEfF0JTLVaGRsvvo1LAp3dVxmF0dx3DV4u0w9UDTNoEYIC1jJL2TIA8S1k1eSGwmJhtfpEp4MbYEr2DSOVrwuJt3Lu70n2dX+HjLvp0mwsi8xfUJN0JIswiJyr8lQ7uOI+kYljpf8LgnIakCOCiEaA0RY8gwwg0SN0dqvrbHvIbdsQncovrsC/Z5POWhCx1HOnzh3CdZ699EWh3KvabLuI6fzI4WvM9Wdu599SB7vb+cGVOIS4aMyZfVZlcclqoyUEqJco9jJv8A4R1e3Oe1yJ/YLPQqx6ydmTGQaTdaSlipVFE+TqnstqXby6vvzlZbQx3KmHRrCDrDMOjr66OvL1N9WNxiAwuWJtd1l4UwqAcrmUxQSqHrOuFwuCwJdvz4cZLJJJ2dnQXWmqVQq1wKyphayA8DVkqRSCRyiqVWhgG3IoDuUvM8X8bFi3aWHFRD1qaUJTBWr17Nzp07W0qOVEI+KdKK7UkpOXLkCKdPn+bqq69mcHCw5nYXC9eVnB2fqfj8r//zN0kLF0No6JrLulXTFV9bC8VnQ4aMWbhWn52Ols2KUUqgJOgmkE/e5NVbGzIKzJW+uXh7iJzCRKriMNeMKqLbl2nL6TSTrAvNEjRKF7OqCUKm7Aj+Mu8pBwOX1cEYwbzGI62A5BEIFCGtcAznE4XFER2GxVBsNaYpcZ3yWTwL++6xM3geUyt//thVbEqv6TrInR3HAYi7QYJl7FKLhScFMzJESpl0GkGkOwYsKDBmvSAvJTZhqcrjyw4tTUxWbgmddtIIpaPySK3sufjayEGuCo5WemtTMAhhkyFjZrwgI243m80JKOtk0JHaHewvY0uS82qaZhuvyiGk9YGmEReV67pzr9Vv4IW58oSNp1wu2OdZ41/HnthzjFgniBjjTDpHAOgxtvH0bPlsoZSXImLUN57JF0o0g6wS/VIbP10SZExx2v9SETGQGUys7X0eM/bHiBakbmssZ5USFasKAWSbbUqGtrST/0gwzJiVxw632aZky4UbsisldpuVMbVQPHHNbw+amZnBMAyklCvK0rQSlTFZZPet+G9aTIJZlpXL9Tlw4ACu6+YUM+2scr4YlDGtWC3OQghBJBIhEom0PAx4sZ7nS3Fl5zIuTiy1mrgYWUtPNQKjHWglKZJOp9mzZw+O43DnnXdWnSi0cruO5zE6Vd4O//cvvMC+2AUQ4CnJ+oFpTL35bYoiBiNDxmT3QxB3feXJGCmQUgBekTJGz2Wj6LI0P8vxtJL9daWGoXlIpTDKbEvXYCTVydaOC6wJxiraSPQKRMZEOoSrDPxUH3MpBR1Gmn5/HK3ouGhFagcdRUgrLIow5i0vUsIqXwwpwRM6Jm71OAAl2Rq4QFAvJAGUghEryuHUAEmvXBGG4vXRvdwUXrCiOMogWON71ouEZzLuRIn6OnDVeXRNEsFCqsLvPeZ0sje5Aa8kPDkTeJzFJnOGfVblgPtpd5IrgjsZdxYyOSXw/3a/yKBZf0tYvdBEAFQm6+a00wsI3DIB0LroYdLbwqhVgYgha22SVGxKaQBd+npSMkZaVicylYJJq4c5VV05c946wxr/OoYTz7Mp6OWImA59NS/FrIocYUqmiNQZpZAlv5u9x1iWheu6l21KKw3Faf9LuuqrkgTs/8qNW7/dso8UKg41qt3aCa0KJd/OAF8AvQUXp0bg95kFZUrSEwWe5lbDyqvv9vKqrWu3KbU/R6i4PWhoaAjbthFClLU0dXR0LJvCYqWSCZXImGL4/X7WrFnDmjVryqo3dF0vUG8EApVXiBpBtklppR4/aL1lIB+tCgPO5kxc9jxfxsWObLbeUqphskgmkwwPD+N5Hq94xSuWnJzMft/FNiplw4b7+vq4+eabay5aZK/BrVLGXJguXcA7OjnJw3t+kpvv+YNpVnU3nmGYj2ICQ7GgjBmfXYfQyit4lQKVVVXnja8cT0c6mX+PphJ0K1EgWYmn/UTDqbzPEfNEhYdUGlqZsguBx5pgjA6zuprYp3klFcoJ28SRBv7iLMF5jCdDeFKj259kY3iGDtMq+7riX5AmJIGijJislSmo2RhCcTLZQ2BejePmhQ8XQCk2+CbpNhaOSdLzcSS1miPJ1SQqKkkkb+7ZzdVF+TKL/aVPOUHG3Q4MTRI1knT7Yihi6BU++ITVx3B6TZktK4LCJqUWSKRBM86+8oc3h3PWGOb8aWXg8ovde4noNd7UJHSR2beYDORyktwiO5ih7eC4JUnK0iDcQiImQ8aVI3MaQY+xlRn3HK6q/p079Q3sj8dqVoZDJjcmGLdIypdyNiZTBDmd7iJVhfBJyfqFCItZyIKMqhi4rIxZKcjakk6ePImUkvXr1y/tJMM7jBH/fYQ80dKPFaQx6MZdpiBfQeUBQrszY/RKfYhtglF0vigpCgYLrYZVpIxJO5mbby0yxrLbo4ypBk3TCAaDbNuWqbpLp9MroqXpYlTGVEM59cbs7CzT09OMjIxw6NAhQqFQgXqjWYVSNsx8JaOVyphaaDYMuBWe50tRZnsZFw+yauLh4WG6u7vp6+tb0vHT6Ogo+/fvp6+vD8uylk0ltphGJaUUx44d48SJE+zYsYN169bVfQxb1eRkex7Ts4VtJkopfv3b30TmqqsVG9dO1p2BUgnFZEy2TSkqdrDfnq5cZy3F/EIXBSSD42q4bmbc4EhJRESJM5V7PmYVkTEIPCXwpMCvFxIxSkFYTzMYmkOvI5Q2qDvMuQvko+MKZt0Qpi5Z5Ysw7RYSXDPpAOdjXQQNh65AmnCVSX+JUgZFoChbRBcKKaHbn/l+c16Q8LzaJRM+XAxFvzFLvy+GUnDO7uZwcoAzVk/Vim6Bx1v7XmBbYLzkuWKCqBZcKRh1uohLPxHdImJYrNZrE3xKCQ6kBjnr9JZ9PiBsgppNKk/Rs8qo3dCTkglMIejQk9wbOYjZovyVchBkVPsxb4HwyidjhHYnB1InUWX+csVEDGSatRYzqu/QNjHpnEYV96MXIWpcz0ux86h6pvoK5iYP8UT4a3kPClx1JaN29erzlNcYGbPYhSwhxCXXRnlRkjH5stpEIoHjOEs6kNCsf0JL/iWC9rCwIS3MnJxpy2fXRuUfd7vblOoNgGoVtCKGXnltzowpUMaoum1K6fTyNmwBBAIBBgcHy1qalrqlaaUqO5ohY4qRzTSJRqNs2bIFx3FyeTNHjx4lnU4X5M000ha0lERHs1jsqsliUG8YcFbRctmmdBkXI6SUuK6L53nMzs7i8/mW7JrqeR5DQ0OcP3+ea665hkgkwoULF2q/sU1olhSxbZs9e/aQSqW4/fbb6ezsbOj99Shiq2Hf7Em+dvZxwqqT6e5RjsRG2BIeQNd0fve7jzKRt1LdHY3TGVp8MYKpy4IGGaUEIT3Aj89PIaowPWo+KLZ4sSvt+HDzhpt+1UVcZMgYqSBp+Uoaa6QSaEIV2Y8kA4HYfFZMfdA1has0zHniZNzqwDQy54EtC8dbaUfnzGw3EdNmTeccQghmnCA9/vITUFFkuRFCFWbEABqSXjNTNT5hhQn78po2lYZWtCjYpSVZZcbYG1/HkdQAca+2WtbA5e2rnmOTf6rs8xHNIiWNqiRd0jM573ThKo1uM0XEtIg0MO+Rys+LiUEmvcoq0Ihmo2tebuph4NJVQWWVD4VirZnkFaH9iyYaa0ETmVDl/BwbV2loRJjjGs6mShfllcqYr8rtmyEkVpM//9XmNZy3q1dXmyKAENt5MVbatlUOIS3AFUE/096Bgse11BYOyOpEDFSuGy+HVpUfrNR5QLO46MiY4rR/wzCwrPaQIiVQcfTEn6I5323rZoJ6cBnJmMp8bbsDfNt9QS1GcVix8tqrjCnOjKk3wNd2PTxPousrYyJdbGmq1NIUjUbp7e1tqaXpUlPG1IJpmqxatYpVqzJtGfkEwcjICFLKgryZUChUcftZm9JKRjttSo2iUhjwxEQmKO/HP/5xU2HA6XQaz/Mu25QuY0lRruQgmwu2FIjH4+zZswdN03J1z6lUqm310vWguFq7HkxPT7N79266u7u58847mwq6X6wy5n8d/zfO2YcIGi5dt8KfHf4rlAJPGqQ7NLYGDWzHIJ02iHSksRwd0/AWNcYSIqMgMQ2F5+n0+7uZTKWRQlS1vGSt37Joscstsr1rXig3I0nbZkYJIxcqqBUQ0N2CHBmfsFkXmq0YZlsNSmko5TJjBzGNhR3LVzADJFwfnf40/ZFEjgSacsIVyZhiaKiC9iTIUDWZemuNc1Z3ARmTUcYsqJqSrklcdvPM7NaqKph8+ITNr6x6lkFf5fwUXSjmvGCB7Qlg2g1y3upC1yR9vji9vuZyLFPS5MXEZuKyeg5bSLfm260y6NHT+MtY0Ipxtf8cr2hBdXU9EGiklVlg9XEIcMYdIOadLHl9NSIG5nOFGh3KKo1+386yREz+9TNIHxNulDG7PiJmjb+PLmOSaa/w9d3aNp6Rc9RjaEt6tZVMWSx2jBePxy+TMcuJbNp/diCR9fvqup4L7m0nhHsAPfH7iDJ+wFYjoJUL4FoaKFVZhdHuzBhtiTNjii+USrZbGVPcplSfMsaTkrTlEg7VFy661CgOqM23NOUTBlnlzGLlhSv1IrwUAbnFBEE8HmdqaorJyUmOHTuGaZoFBIHfv3AtuVhsSiuxxSvfThaNRnn55Ze59tprmwoDTiYzA5fLNqXLWCpUKjlYqvHTyMgIBw8eZMOGDWzbti13HWpXvXS9aIQUUUpx8uRJjhw5wpVXXsnGjRubvtYvhoyxPJfd0+cImf6CpiAhwNBdIjpAZqwxnghzIdnBhVQHSikMITE0ialJTM3D1DP/+XQPn+Gha9UHQK7UwRX4xCCOVCQcF73WLGK+FEFJLZe3UQ62a+ZmJEkncw/wpIahe3ToYaZRhHzz31dJenzJksrqRpBwTHoCLm7RNCgt7YJw4LBpz5MleaoeuXCPyq/bBkrmrgIwi8kYASYec64vlxWThas0dCUZMGcQSvKD2E5kAzmSIWHxK/3P0D+vvKmGlPTRnVc8Mpxaja55DAYXF4I76wV4IbGlrtaggOYUVJRH9VRNy9ErQke5Jlgf2dAKCChpdxJiQ46IkUoQ9/x0GumaRAxUz+Ys+3pl0uPbwqh9qOrrfNY6jkqJxUxdn7szvI6UHCLhFRKQHfoAL8czrWT1IGbVn0fVisyYS1FVfFGQMdXS/g3DWHQAWy1o6c+jpR5GLFGOS0As36RbVpEgttumtNRTbFF0QczIaNu3PSsvaMtrQBmjpCJtOyuWjClGJUvThQsXOHLkSG7Smp24NjL5/vemjKkGIQQdHR10dHSwcePGnO1genqaM2fOcPDgQcLhcO44XyxkzEpRxlRCVmbbbBjwYjzPn/zkJ/mrv/orRkdHuf766/n4xz/ObbfdVvN9X/nKV/jFX/xF3vjGN/LII4/kHldK8Sd/8id8+tOfZmZmhrvuuou/+7u/y2VFXcbFj3w1sRCi4BrQ7vGT67ocOnSICxcucMMNN+QUfllkf+uLla43i3pJEcdx2LdvH3Nzc9x22210d3cvyXbL4bMnn8SWHtLxsapK+6bjaUwkFyYtQgg8dDypY1XYtKYkehFZ48sSNoaHkGE8wow401h2N5pe+36cvWVLr/r4Km4pmJ/vJu3MWGd78CpOzKV4bibGlv4JNKEwcFkbniWgN0ciplyDsXQnhi6ZsCIUjzwlEhMTB4c5y4+pyzJjU8GsHaDDSHMs2ce2SOVaYSEUhlb6G/NpHidSffjNwj+GJzW69CRRPc7/nbghQ8QUhRtXQoee5FdX/YSeOjJXYKHVCSDu+bGFwYi1ij6ztA2oXow5nexOrkfVSSD5hIvMCxzq1i2Mit9V8XMd+1jrm2YpZwwCVULG2GqB7Jp1g6SlSYeesevUUqAVzz2qwSRM2Ozngn2k2gfSadzA7lT1tqQsNATXd6xhwtlb8pxPhDiV6qzZ0JSPo6eP0nliwT7f3d1dcVy/2Gt9loxZqYuyzWLFkzG10v6bkZnWDTmLnvwgmvNEez6/AgLa8v1Ziqvo8tFum1JxdWK7UVYZ08YLfIFNyau/2lrK5QnxbQWKLU2e5+UsTSdOnODAgQN0dHTkyJl6MlBW6kU4KxVdrv3TdT13HLdu3YrjODlL0+HDh7EsCyEEJ06cWPZGrEq4WHJtigcT9YQBHzp0iJMnT3L11VfT0dHR8Hny1a9+lQcffJBPfepT3H777Tz88MPce++9DA8P09/fX/F9J0+e5Pd///d55StfWfLcX/7lX/I3f/M3fO5zn2Pz5s388R//Mffeey8HDx5sWYvXZSwPsmpi13Wrjp8cpz2LTLFYjN27d+Pz+bjrrrvKnk/Z33q7F9QqIT+QuxJmZ2fZvXs34XCYXbt2VVS8NbrdZsmYfx55GcioVNKuUaKsyGIs0dHweEYKDak0HI9MhkfRqXF73yDDycxEPW471BqqKgnZSbP0tKptlbOWy7W+K5hN65zxkoDN6TnBoZk4nYEUQcOmU08xUKWyuup3U4LRVAcJzw9C4FQ5/H7dz0RSx6d7Fbc16YS5YHdgSSNPHSMpJgkEmcDWYujKBU3M13UvPO8qDUuafHfqGiyVOddCWidJVV2t0mPE+ZVVP6FTrz+/Y8A3S8zzowSEdZvxdAc+3eNwsp8rQ41nObnyVbycbIwoMTUPRy38Bjs0C70MWaHj8tbul+jS61dstApSpUnIQseCLWOYaAghmXFDBERmfF/PuTn/i6BWvXVQi6IRZMo5VfE1fq0DS8bZHTtLPcelQw+zOagzkVcLvrBfAltdwZjT2N9+1bpVbDW2Mj09zbFjx0gmk7mogmg0SldXV8uI98vKmCVGsay2Uu1iu2S2wn0ZPf5ehBpt+WfXgm+JW4Xy4akUlX7Q7SZj2uoRKoMSZUyNlZvFosCmJFX9yhgUlrX4c/zCdJz+6PJaI3RdL5i0ptPpHGGwb9++EktTMBgs+N2vdGXMSiKKTNOkv78/N1EfGRnh5MmTxOPxgkas7PEuPtbLgeUM8K0X9QwmKoUBf//73+fLX/4yiUSCN7zhDdx999285jWv4dprr635vT/ykY9w//33c9999wHwqU99iu985zt85jOf4X3ve1/Fff3lX/5l/uzP/ownn3ySmZmZ3HNKKR5++GE++MEP8sY3vhGAz3/+86xevZpHHnmEt7/97fUekstYYaimJs6Hruuk04sPdy3e9tmzZxkaGmLTpk1s3bq14rmdtUotFxmj63pFUkQpxZkzZxgeHmbLli1s2bKlZdfHZsmYoblzTNoJsmO0uF1qc4FMTfOc1Xq7uztvOfJcA6HX3v988kXJyscuaBj4g/C90wk2hLqJu5mxkil0QLIhOsX60CyRGpXVlTBjB5m0Qkih1zWPv5ACv1H9nEx6PrIflpYGIc0tSyIIVFlbypi1Cr/uMZ4M0xNI5ixirtTwxAZiXuYeraHjFx1VyZh+c5Zf7nuWsN7Y8fFrHofTXewIjnHcXpX7PuNeJ1fSwIRcmZybu51DXoqK3dYVoKPQ82xJHZo9T1EskBVhLc1bu17EX0ZhtBSwlFFCbCokptaBq2bR8Rjwz9VNEgqRIei8KmRMh74GR9rEZOU5aKexjtMpg6g5i4asaWdbH+gnqI0y7c5U2Ob1PDvX+JzXUlZBtqFlWUxNTTE9Pc3BgwdxXZeuri56enpIJpOLGuNlM2MuNaxIMiYrq83PYKh0E2z5jVxJtPQ/oKX/FrGo8rHm4VvG+ZAnk0D5E73tNqU2VtOVR9ENsspgoRXIb1NyG82MaYEyZujM+LKTMcUIBAKsWbOGNWvWFGSgjI+Pl7U0wcpXxqxUGIaB3+/n2muvLWsf8/l8Bce6FSvAjeJiUMY0Y6UKBoO85S1v4S1veQvf//73ec973sPdd9/N448/zoc+9CFCoRAPP/wwv/RLv1T2/bZt8+KLL/L+978/95imadx9990888wzFbf753/+5/T39/Mbv/EbPPnkkwXPnThxgtHRUe6+++7cY11dXdx+++0888wzl8mYixRSSmzbrqiGyUerF7Nc12X//v1MT09z00035Uj3amhVzXMzqLRt13U5cOAAk5OTdX+PRtBsm9LfHv0B+WxCzPHTR3G1NYzGO2iHemDGzoxhYikfoka+TGZf8veh/P4EDYONPWGOxjNWn+OJhfYfTShuGjzDps7puiqri5H2dEZTnTiYdR2OXN5HlerqBSx8YMrzETLcsgqYSnprbT5HZtoK0eGz0LXM79BTGpIFC6tfBNGpfC9e65vk7X3PE9Qa/x3HPT9n3R7WylnOONHc40HD5Wy6m3WBmZqfoYsuTtu7OKnHcZpomBVCFZBYYT3Tjtuh2cRkgE4txc917sEQHlIptCUeYwltOy8ly9u+fFoE251lfWC6YbWWgazYXRs1NhFzJ7BVZbtZj3E1u+NTmGKOHgGG8LBV5XHJ1eH1xL0DpGT586TH3MGPZ5oTHxS3Kfn9/oJxfTKZzC26Tk5OApmFomYWAhOJxCWZt7fiyBilVN0DCWix51lOoCf+CM2tPLhdChSHfS0lJDYanWUvE+0O8FVqaQdkxbYo2cYmJShUxriezFmPamfGgO0s/pwYOjPBq67bvOjPaRfKZaAUW5qCwWCuvamRWuelwEonY/KJjkr2senp6YLMkyw5ky8zbfc+XiyZMc0ilUrR1dXFgw8+yIMPPojjODz33HM5FU05TExM4HleyWtWr17N0NBQ2fc89dRT/MM//AO7d+8u+/zo6GjuM4o/M/vcZVxckFJiWVZByUE1tHL8NDs7y549ewgGg+zatasgPLwallMZU46Mydqr/H4/d911V93fY7HbrQVPSl6cLrQruFLHlQIjjxiZTgexvPaEoJ9LzSKEjhT1GaBqDemCusG6Tn+OiMlHyLTo6f4R3f7GQ3o9JRjLsyTVgywR0wyJlQ301cosKJrCLbsLYSMFXjfTVpA14TkC84u/rtSwPC8bn4MpQiDKT9U2+8f5D70vNKUYsaXOM4kteELnhdQm7KKw3bN2T00yxtTWsTuxg5hME/MaD/3V5+cZWt5YPDRfA96lpXGkxlu69oDwcNARKHzKW8Jcvo08nzRxVLnmLEXam0FQ9ylWAL1Co1KfeSWT9im8ChmlGgZB7RpeiGXyYTrn1VCGkNhlPs8QOleGQsx5eyruS6c+yAuztQOfKyEpK5NGQgjC4TDhcJh169YxNDSElJJQKFSyEJhVale75marrS81rDgyJquCqWcgAS28kctZjPhvIbzqadVLAaMiX7o08GkB0rL0Bthum5Ja6u+tiranAJVpVGrHtd7ySgN868uMUVjO4lYvp2Mpxqabv9guB4otTZZlcebMGc6ePVuXpWmpsdLJmGoBvsXH2rbtnMz00KFDOI5DV1dX7ng3k3lSDy4GZUwr2gDyV3ZM0+Suu+5qxa7lEIvF+NVf/VU+/elP51rOLuPSR3bcVG92VSvGT0opTp06xZEjR9i6dSubN29u6Nqw3Dal/G1nW582btzIFVdc0bZrUTNkzFfPPItbZuHG8XSMrKpCCsYT7Vs1TnkOwgsg6qySrpYRE9B1VgXgRHqm5LlN3RNc33+uqcrqaSvIpB1GCa0uXsXAxVFG00QMgDXfBpQlFQSSLi1Ft56kUytfge3XXBypEXf8OHmqc09q2HkLsgYBKKN42B44z5t6X67ZPFQOUsHTiS1YIkMiFRMxAK6ovuBgalfxr5MddJoBZrzTDe8DQGQ+/yXfxhWYP5cjms1rosOQdywUAolooFuqeQixmt3pKClVrilIYiCRJJqeK/iFiy6S2DKEEiFslWS1eTWj9lBetXkhglqUmLeGo4mFoN5skLVR5jzo0iNsCCpm3MqZMz4R5ngqjFX2e9aHlFdfzTtkxnjBYJBNmzblFgKz5Qfliie6u7tz5QeQsSk1q4xppADBcRweeughPve5zzEyMsL27dv5i7/4C173uteVff3/+B//g/e///285z3v4eGHH25431YcGQON3aiyN9NFT4S0LtzOf0I4L6DHfwtB/SdXq2HQnC+2VfBp/rJkDIDnCfQ6EvSbgSomR9oMVSwpVYAA6Qp0s/XfcTY5ywsvvEBPTw/ptJUjWGopY6SS2Pbijs3JsWlS1tK0gbULWcvSt545xIO/8oYCS9PRo0dLbDZLXZG80skYKWXd++fz+RgYGGBgYAClFKlUKldXfurUKYQQueOcJcJatY8XAxmzGGVMM57nvr4+dF1nbGys4PGxsTEGBgZKXn/s2DFOnjzJz/3cz+Uey95TDcNgeHg4976xsTHWrFlT8Jk33HBDQ/t3GSsHjRIhi7Ep2bbN/v37mZub45ZbbslZSRvBSlDGeJ7HoUOHcud+cetTu7bbCP7PmefLPm5LneC8quJCIoKn2nf9lB4gyrULVUAFMsavafQHBOeKFAem7nLr4CkGw41PDNOuwWi6EwejAqcyP8Arws7wOfbEN5R9rl44UscvHPqNOfqMOB1auqatyidcZqwgIHDkwv3EVRrpPCuJJvwoWXi/uS50hp+N7m3KugXwfGITcarfs4O6w7QTJGqWzoWEdgePjNvMeBohs/l7YVjLht4ufA+/kEipuC54BiFKr00OGlqb1TFCdHHQ2sCcnCrzrMREFqh5mtsGmEhMPQ4kCeqvZNoThPWtJN1TSFE4Xu82NnM4KYl74wWPm/PHSC8iYzYGVqPUWWbcyr8lgUZabmW8wcDeYhTblKqhePyUXzwB5IonpqenOXLkCOl0ms7OTp544gmuueYaYrFYU/eZRgsQPvjBD/LFL36RT3/60+zYsYPvfve7vPnNb+bpp5/mxhtvLHjt888/z9///d9z3XXXNbxfWaxIMqYR5Cc057NnzUKZt+CFPoSRfH/tF7cJOq0N1GsUvgrV2kIoHNdA19szqZdLnNFTQsaQDafT0M3WDw41v8baVWuZnJxkZnYOTyqGhg8jpSSVSlWc0EqlsOzFHfNTYzMtyZ1ZbvzpV57k5PgMv1fD0rR//346OztzhEFXV1fbJ/krnYxptto6W8McCoVYt24dUspc3szo6CiHDx9eVF15Pi6VAN9qSCaTDa/s+Hw+br75Zh5//HHe9KY3ARly5fHHH+eBBx4oef2OHTvYt29fwWMf/OAHicVifOxjH2P9+vWYpsnAwACPP/54jnyZm5vj2Wef5V3veldT3+0ylh+N5JEshgiZnp5mz549dHZ2LqplqJ5Go3ZB0zQsy+InP/kJmqaxa9eulhHLtbbbCBkzkpzmXLp81aw9bx9PuwbT6fbuu5QC3ah/EloutNcnBAMhnZEia8NAxwy3rD5bsR2q4j7NtyTFPX/V++9q/xxjVlfh/ikY9M+yJ16eqKmxZTb6J9keGmOdf5oOLd2QSsIvXKbTmWyYAjJGaii1MN4TysDLI7VujRzntV2HmiYDDqYGGFedNV8nBJyy+4iaZ/IfJaVezWPj8y1MCPwiVPEzaiE4T8bkK2NMJGNSEarYCiXw0DDa1LYhCHLMuYoJrxxBITHxalZXN4qIcR3PxhbqxE3RxSrfAGHNwFUT+EUPL8dGkeVapuZJmHwy5prIeubc/cx5Bh1VpsUR/TqeayKwtxiNKGNqjZ+KiyeyC4HPP/88H/vYx0ilUqxfv57BwUHuvvvuusoPoPEChC984Qt84AMf4PWvfz0A73rXu/j+97/PX//1X/PFL34x97p4PM4v//Iv8+lPf5oPf/jDdR+HYlz0ZEyWgGkVGQOg/G8gkXiWsHikJZ/XKLSy/sSlg1Ghr1AIhevplPQdtghSLbVyo/wAULpaxecWA1vauVCrzqdOw1gCnz+AUjP85Cc/IRgMFkxosxesVmTGnBybIbVIQme58V+//ENePjtONFA60S9nacoqOUZGRnKWpqySIxQKtZw4WelkTKtUJ5qm0dXVRVdXF5s3b85l+ExPT+eIsPy68kaIsIshM2ax+9hsNeODDz7Ir//6r3PLLbdw22238fDDD5NIJHKDi1/7tV9j7dq1PPTQQwQCAa655pqC93d3dwMUPP47v/M7fPjDH2bbtm25auvBwcEc4XMZlzaayYxRSnHixAmOHTvGtm3b2Lhx46Kue9UajdqNdDrN5OQkGzZsYPv27S0jgtO2w59/7jGSpuT2ret47XXb6Ota+M0LIXLf+cDEBa7uq1xND/DJo9+v+Fx2It+u0N4spEtDRAyU2pRMIVjX4eO0u2CZ1oTHjQMjbOqcatjykW9Jqv5eRcSwmbA8vHmTSzYjxtAkq8w4405tgsInHHaGzrM1OM6AOYtvEe0+Ps2ZV8Ys/A2lAoWGqyTafN+OVHqGjBHwis7DvLrjcNPWmPNOJ8ecVXW/P5lX5+wpgwvuq3h+bnqeiJmvpV9E1qJfKx2TTkqF1eB57CnRtEqoEAZn5a2MOCNlnpP4qFx13iwixk6ejxUqcBxlc846DUqjx7yKMet0WSLGwM0RQ7qQaFLjuq4+Jp1MPkxaBumoEKrcY+5sOrC3GI0oYxodPwWDQdauXctXvvIVpJT80i/9EkIIfvjDH/Knf/qnBAIBXvOa1/AP//APFcdVzRQgWJZFIBAoeCwYDPLUU08VPPbud7+bn/3Zn+Xuu+++9MiYRm7sWY+067otCVpTSnHs2DGmxjZz11WL/rimoLG82R6GKL+qrWkK123fRMlTS2vPkuUyYwDptGdl3lZObsLuepmN9fUPMDVxgVe84hW5tPHDhw9jWRbd3d309PRg2XYu7LdZnBqbvqiVMZ/4vz/he3uPzof51L7pFqe5x+NxpqenmZiY4NixY5immSMLenp6WmJpasQGtBxo1/4ZhkFfX18umyS/1vDAgQO4rps7l6PRKJFIpOJ+XCw2pcUQ/816nt/2trcxPj7Ohz70IUZHR7nhhht49NFHcwG8p0+fbvjYvfe97yWRSPDOd76TmZkZXvGKV/Doo4+WDEIu49JEo8oYy7LYu3cvyWSS2267ja6urtpvavE+tAJSSoaHh5mcnKS3t5edO3e27LOHjo/yrv/vW0ihmAlLfnjuNP/jRz/Gr3TWhMLsXL2KDUGNV2zPjFf/bt/zfOL/+dmKn6eU4qmJoxWftz2d2bSfpFOqTMoKpFpx2ZdK5FpvRG5qWP16kx/gawKbuoOcsBaCXnuCCW4bPNVwZXXGktRRd0tSSLeRaPT64lywu3JETFCzkQgGfLMVyZheM8ZVoXNs9E/RYyQWbU+BDOky7YSJOZlzwJlXN7l52TGG8GGrFJ7UcKXi7p4D3NFxoultTrtBXk6tb2gM4Nddno5fgaE8IsY6jianiecRMQBp2fy43TefB5O1Kb2+42BdfUzFf4EZGaRXrxwiWx8E4+oVHLfOlHkmo4hp9fAprG/m5XgKWaaFS8dHSN/K0eQJVpvd2F7pkQkZC8feFD426R6TzkKgvyWNshmYncZanp8tr7RrBhKJJS38Wu05+GKUxVkl45vf/Gbe/e5358oPnn76aUKhygqtZgoQ7r33Xj7ykY/wqle9iq1bt/L444/zjW98o+Be9ZWvfIWXXnqJ558vbyFtBCuSjGkUrbqZp9Np9u7dSzqd5qbr7wT50RbsXRNQcajh52wnjAo3WCHaS8bINiluKm+vkJzIDlw8tz2TQYXCUQ4+4cObXxWznEzekWEYrFq1KudVTyaTOWXHhQvjePYcBwcXvJWNSsJPjs0QMC/On/v//uFuvvr0/twdRTY4FspvadqwYUPZ5qBmlRz5+PeijKmFcrWG2XP5xIkTaJpWkDeTP/G/WMiYxdqUmp3EPvDAA2VtSQBPPPFE1ff+4z/+Y8ljQgj+/M//nD//8z9van8uY+WhGZtSPdeuyclJ9u7dSzQaZdeuXS3L5FpqMiaVSrF7926klKxfv76lqpwvPfoCH33yWTyfwJdv0RECS0hOpmOcPJXJcPj7Q0cIfPMJ5ta5/Nk/Pc4rd2zkrp2b8Bfdp//l/B4sWfn4OFJnLN5R9rmMMkWVTMaUyvxX76XWK1LF+AwPKUVB8GwxpCeQ86UPBrC1t4OjqawCQLGzb5SdvRcaIjfqtSQVo8vIqM0jhs2YtdCatNY/Awii5kJGokCyNXiBK4NjrPXNENZbs0ioiQ6OJLrZE+vl+cmNxL2FMX72OHp5Qb2m8GOrFI5S/HTv17kh0jwRk5Qmz6c2IWuE8hZDiExA7Jy9ibHULAnPhyyaH8S9ZN3nUTHyrTU/HT5Cvzld1/syXV4L501c+gkLOxf+2wzmeDVD6VIiRsPDQLaciAlqaziQNLDLOCH8Igys4VQ6E4wc0ENlxfqBPGXROnM9I+JIwfO2NFAIRN6x8msdHE0EsVXrFv3v7rm7LiIGWluAkC0/aHUBAsDHPvYx7r//fnbs2IEQgq1bt3Lffffxmc98BoAzZ87wnve8h8cee6wli1cX5+ysCK2oZ5yYmGDv3r309fVx0003YegpmGnN/jUKgcT0/Dh6PRxx66FVuOpkMmPaR8a40oIlyUnPQKryF27ZJjIGwJIWPs2H62VuQk6F8zY/o+P7L07RFTbw+/25tPFGyIO5ZJrpWIpo5OJb7f7Oc8N86nsvFKx+LXZdqpylKatKOnDgAJ7n5ZQcjViaVjoZ02xmzGKQX2uYnfjMzc0xNTXFuXPnGB4eJhgM5lLzl2MfG0UryJi1a9e2cI8u4zKaQz02b6UUR48e5eTJk+zYsYN169a19Dq3lGTM+Pg4e/fuZfXq1ezcuZOTJ08Sjy9+UuJ5kgc/8QhPTp0HM3NsbKHAU6BXOFZCkPB7SKH42tFDfOPoMOKfoUMz2dDZyfXrBvipq7fwhYmna2xd4HgaosxlU8ms0rzwrlknV5f3+sLPMDSJ0sCxK08h7KQJCEwB21Z1cTivvrojkGZNx1xDREz9lqRSBHQH0OYXdCTZcWafmSHGgprDzR0n2BKYoN+ca6qhqBxc1YfSdnDKMhmzu/jKifJjMFdmlTELX0zHB0ju7fsyV4VONr0PjtJ4NrkZm+bI0ym7kwu2hYYvZ/HKx6wbI2oKaEIxlPn7Czboc3To9VeYJ6QPX16mjK0M4sqfqwdvFGnxSvYkl46I8WlRjlvdJGRpuG5I9JCSESad87nHjAo5nr68gOPixWxHakg0JAv6NYFOwtvIpFtaJd8sXh19NT/f//N1v345MvcaLUAAWLVqFY888kjOyjo4OMj73vc+tmzZAsCLL77IhQsXuOmmm3Lv8TyPH/3oR3ziE5/AsqyGvuclQcYs5mYupeTo0aOcOnWKnTt3snbt2vmbVwcKA7HEobJZBLUgTl2Cvdaj0lSo3coYV1lA82FgjcJTLvnfVs3P+GUbv6MlbToAb17ekVXGVINSoBl+tm7dytatW3O1w/nkQX7Fc7Fc7+ToDACpi8ym9OODp/iLR54skSF7jY4ka8Dv9xc0ByUSCaamppicnCyxNEWj0YqqpJVOxqwEG5WmaXR3d9Pd3c2WLVtwXTeXnH/8+HEA9u7dS29vLz09PXR2dq44cqYVbUrVJLWXcRlLhVoFCOl0mj179mDbNnfccQcdHeUVGItBM81CjSJ/nHfVVVflyNBW5NWcuzDDb/zN1xgz7EI/gADNA1nlUiF9mdcpP2CB0mA2kGb/nMP+g5P878P7CNw0TbNZMEoK0BZ3v/Sc0tBeTaveJuNYOtLTMQRs7+9mKLbQACOQrO6Mk3B99Phr5yOmPYPzyU5cUaklqTY0kaFgFII+X4Jxu4M15iza/LHRNMUrOytbweqFUjDlhTmR7mPO62FOCSBjn/GsK4DyzTbZzJh8ZYyO4O19z3NFYLzse+qBVIIXkhtJqOYW4pKuyZTTwdbgFo4kj5V9jac8QlonSTVb9vkqewfAGj3eEBGT2WbhmMBWOnHpp6/BzwGYkjdzwDpX9KjCwGtRDk0hDBFizNnAlDtZ8lynvoZJWxHzCp/LL/M2MPBpPfT5okT0vdhqBiglY7J160otEGUh/VpeaEFgbxZ3dd/FL6z+hYbes5jxU3Z83mjmXqMFCPkIBAKsXbsWx3H4+te/zlvf+lYAXvOa15SUJNx3333s2LGDP/zDP2z4O65IMqbRCUOz9YzZgYbjOOUHGqITVLlqs/aj09/BnD2zLNuOzc1SjkTX5tuU2gWFh4ZW1j/ZDmRsSnkT62xmTJuVMZBZSQOw3QyJWG0iL5XCyTu/i2uH8yuejxw5gt/vz01mo9Eo+45lLr6W4654wiCLfSdH+aMvfr+sCkY26lNqAEIIIpEIkUgkZ2manZ3NVTpXszSt9GOrlFpx4bj59jzbtnnqqacYHBxkZmaGffv2IaUsyJsJh8PLfoxbEeDbTGbMZVxGPWgmc6/cYlZWRbJq1SpuvvnmlhUkFKPdyhjLstizZw+WZZWM8xZLBH336YP8ybefwK7gGhZFQ4xiKDNzL5N+RU4IPWDByQxZ61ufZDGhvFKJeS6m9J5Zz11UVSgZ0jVVkYyREpyUiUCyc3Ufh+YKV6N7I0lMXZLyqlutm7UkFcMv7AJrzZbgOG/pfYmg5nDYymRIiPn9bob3d5XGlBvmvNPFiNONrUq/V4ce5f9eqFwx7BQpY/yazc/3Psp6/+IUDM8mNjGpmiNQpRKM231sCGyqSMRk4dc7SbqNkTEhzWaVlqRXn2tY7WwWVV5byijIJ6oXjryO3ak5DLEwdhN46Ki2EDEaBjG5k3P2WMlzPcYmzqZjZcNw8/ekxxzgnD3OVT6LGXcm93hx5qat5smY+R9wj3l1ywJ7AW7vup23rn5rw+9b7PgpHo83tSjQSAECwLPPPsvIyAg33HADIyMj/Omf/ilSSt773vcC0NHRUVKSEA6H6e3tLXm8HqxIMqZRNHMzzw40+vv7ueqqq8qfHMtIxgTq9N+1A36fXvZOLQTzbUrtgyZ1pLY0ZIyrbArJmKwypn2TPXs+7MyVWTKm9ndVChy3/Pmdn4eSrXjOWm6OHTtGKpXin//tTO5z0rZL0N8ar3+7cHJsmvd8+l/KpsdD65Ux1aDrCzk9QIkqyXXdnCqp3pyG5cJKUMZUQ3ZSNDg4yNq1a8uqlAzDKMibaUVoe6NYrOe5GZntZVxGu2AYRsFilpSSI0eOcPr06QIVSbug6zq23Z7w/mzOTU9PT8Z+XkQoNUvGKKX483/4Lt86fQx8la+ptdwuOTLGl/lf5fcQ4YV7vdZT33GpdFlfrDLG8wS6Xq5OV6GVHadJpGUCir6QVkLE6JpLbzijXnCqSIYWY0nKh1LQ5VtQ36wyZukxEqSUQTBPSSAEJJWPCPUd77Q0GHc7OG93c97tKgi0LQfpbcKjsmojPzMmpKf50JX/wnr/TF37Ugm7E2ubJmIAzqe66DHWcSx5suZrzTrqrS1Pw68vnDNXmuMMGLNNLb36hFfgALSUga0am5tY9haed1JIBK7SMYVExyWoOTiqHVNjgSdu4niqWIUDq4wrOZYaw60QneDkPR7UQ9zRFWLGLQyedYvImKwyRiLoMtbz/GyjyqXKuKnjJn5x4BcbHk8qpZZtMavRAoR0Os0HP/hBjh8/TiQS4fWvfz1f+MIXcq2UrUJ2EfeSIGMayYxpZKChRHcbiwKrwy+W708TDPqzysoCtNumBGTyVJYoyNerlBnjLYEyJkvGOJl9qK6MkbhOfbcsXdcLmm32HDjNWGphVePfnnyKdQOrcsqZ5ZjMVsP4bIL7P/EITpVlDreNyphaKFYl5ZMF09OZ8LlDhw7VtDQtB1Z6OG52/3IrVEUqJSllTqV09uxZDh06RDgczhEz3d3dbVu9z8dibUrNVltfxmW0A/mLWclkkj179iCl5M4771wS0rAdyhilFMePH+f48eNs376d9evLt8g0s+2ZuSS/8dF/4oRKkuuVrYBai+vZ+WNu7tftIozMMoTWbdUdoacJhSwzWlVKlNRLLzxZvZlQqcqaHE3Lbi0/kQKUo6HjEYgopgvCfTP389Wd8Zz6pNyWXSkYS0VISD+iXAhOA8g2JgX0zBirS4/TYyQQAsbdTgLCLSB64spfkYxRCuZkgAtuJyN2lBmv/uu3KXz86Hz1yIFsZowPhw9v/7+s9lVW0dSDo6lVnPF6miayZp0ArlrFOWsUVZdupfZ9N+4G8M83Hg3oc9wUPt20Bl4IcJSOLjxcJXLKp5Q0CNYR4iu0zeyVQaRIoZSJh8BUDh16uiSguFXwabfycqKUiOk3r2I4cbrqcc6qZZRS9PvGmXZLG4BcCi1/9jwZY4gwhxM+bNW4hascrotcx68O/ipaE7/P7LW22XFothCi2fFTIwUIr371qzl48GBDn1+rRKEcsvelFUnGNGNTqueGmp+iX9dAQ+sum2C9FPBry2knKE+GaJpsa4AvgN8IkHRbc9GoBU85ZG7XheebaicZM89eu96CMqbWEVVS5exMjeIbjx8o+PfmK67ElBYjIyO5yWyWmOnq6lpWG0s8ZfHrH/066Rq/ZakyZJa+zMRCMVkwMjLCyMgIpmmWWJqyAbXLSYas9HDcWooTTdOIRqNEo1G2bt2K4zi5vJkjR46QTqfp7OzMKZk6Ojra8n1b4XluR/bGZVwGND9+GhsbY9++faxZs4YdO3Ys2b1A07SWkjG2bbNv3z7i8XjN+u1GlTHP7jvJ7335X0n66lwQqPWnyF6esoc64mYyZHSJubZ2nkqtzSgpKob11voGnluaFZN9pyAzIfYbHta8rVtKwBUEgg5xuzCjxDQkhubSGVggJYQAV4IxfwxcKUi4fnoCaXpIM5LsQjVJyGSJGBBoQhHW0gyYczlyQhMw4kYL3pOWhYphTwmmvDCjThcjdpR0GftRPejStjHjVF9gVPNkwH1rXqLHWFxF83m7iyFnoGkVrCM1Jq0BPGy8OidAXiXCbx6uFFhe5vj2aAl+pmv/osMILGUSwMvZcSDTqlSLjBFiDS+nOknn2oQEGh5dehpN0BaFc8i4medjpURMn3ktQ4njNd+f8BIopVjtm2PavVD2NRkyJnMhkUrgzDO9Ye0ajrjVbWb14qrwVbxj7TvQG2zlyiJ7nW/23pJKpZBSXjLjp9nZWT7/+c/zlre8ZWWSMY2insyYpgYaorNFe9g4qihf2w6lyt84hKDtyhhTW9pT0lA67ryWeKHaun0H35q3KeUH+IZYuAF4nkTXCwcgUircJsmYF0+cL0hk9gVCbB5Yy5YtW3AcJ2e5OXToEI7j0N3dTW9vL47jtKy6tB7YjsuvfuRrxOqUq1uOR8i/sogFIQQ+n48rrrgCKLQ0HTx4MGdpyio5ljr/5GJRxtQL0zTp7++nv78fyNyop6ammJ6e5syZMyilcsc6Go3W3YpVz35ezoy5jEsFmqZx8uRJZmZmuOaaayq2S7QLrQjRzWJmZobdu3fT2dlZV/12I2TMJ//pR3x27z5kA4MzVeVyVmDHFiANiRbINLeoiIUIedSbF6MZCtdSiLzmpmx9dTnWpdZ0U6nK1idNLLTLmLqH5WaOsZM0CAVstHK2Jl2yuiNR9JmCmBMk6k/liBifnmfREhKvCZWClMwftszGfDis9U2X+T6FDzgYpKXBhBvhvNPNOacLtch2T4HgxQmDSgucWfSac7wxuodOvTQvpBHMuEFeSq+v/MerAaVAymtBzOHI+q2Daa/690t5JhKNIGne2LW3TrVNdThSAz1jUcoirvysqmIHEyLKAWstMZlRMUsEAkmfEc9lxLR6RBYxruPZWKFdTyidbmMnh+sgYgBSXoJBf4xOs/L5oZBoKoAUDpbUyX6Tabc1FtArQ1fyG2t/A2MRrg3P8xBCND0OTSQyf9tLZfw0PT3NX//1X/PYY49dGmRMNZuSlJLh4WFGRka4+uqrWbNmTd2fq0R3i/awcSynuUFWaHFaCpuS0STj2iwChkncK/y+7VTG2PM2JTcvwDffbTs9m6Svp/BCo5TCcRu/ee0bOke8yNedthdumvGkw+rVq1m9enWJ5WZqaiq3Ytrb20s0Gm0bOaOU4h0f/QYTifpXAi3HJbTCsm+KrWaVLE3Z5qBs/kn2v3Zbmi6GzJjFkEXBYJC1a9fm8mZisRjT09OMj49z9OjRXCtWlqBp5ngrpVpiU7pUBhOXcXEjkUiQTCZxXZddu3YtS8tXK2xKSilOnTrF4cOH2bZtG5s2barrWlePKidtO/znv/4ae61pMBrMSNDJSDnL2JmyTUpZuAGJ38zcr32DdsMTauFokEeEZNZ3qtiUqBxaK12BVlYVA2Ze7ocx//8dWwOlofskeklGjaTTnyZoli6Yzjl+Os00cdePXy/8O+hC4jVcwZ2ZYGe/kl/YrA9M13KTATBmdfBSbCM+zW3ZfbLX2Mz345XHNQJJh9/itauH8IvFTZrT0uAnic2oRdxDo8Yt7EnNkJaNNbkmvFTlGlbA8kxCwuat0d2oFtWGO/NEWbEyphIEYY4425n0FtqpFNCrxzHy9km0gCjKImJs5/nYTMFjBj4C+haOpU7U9RkCuLO7mxn3bM3XGsqHLRzsPJWXvkhCEWBLcAvvXPdOTG1xY+5WLGTpuk4g0Fw72ErDunXr+NznPseXvvSllUnGNCOzdcrIAJPJJLt37wbgzjvvbNxnJirLW9sNUyyTPwqQqjz72u42pcw2lpaM8WkGZMmYbJtSWzNj5pUxXmlmDMDUTJLeaKFiQipw6wj6LcZXH3255LGUldne9348xMmz07zzbXcCpZab4eFhbNvGMAxOnDhRYLnp7e1tqQXkP33iW5yZmWvoPc3attqJark/5fJPZmZmciqOgwcPEolEClqaWm0TWOk2pcXeqPMhhKCzs5POzs5csHU2b+b06dO5452fN1PPtrOr6EtdzXgZl1Ev6h0/nTt3jgMHDuDz+di0adOy1a0vloxxHIf9+/czMzPDrbfeSjQarf2mvG1XU8YMnxzjP/+vR5j1Nbl/QiBcRTmHiyq2OnUsKE6McBPbk4UZMGq+mafs3FIJMtO8Mi1LFRqUsjDyFnh0TSI9UI6O8Es0kfkvH37TpTtYfkyZ9kxiZYgYAENI7AbmxVlrUpb70vG4M3ocn1ZHhIFncjg1AGikPEm3r/6FoWo4PdcFZZQapubSFUgT9tl0G0k2hyYZcaJs8ZfWHdcDTwmeil+BuwhleVjbxP5YjITXuE1q1p2j0yzPH2bUNvCewScQLZzXZOut85UxCemvoOoyOe3dxHlnwSqklKJLS5ecH0JkogEWS8iFtA3sjjsF7bB+EUapAU6nz9T1GQLY1R1lxt1f1+vDMoowFJ7qIPvb1hb5PTYGNvKf1/1nfNriFwsXW36QSCQIhUIrehzbCAzD4NWvfjWvfvWrVyYZ0yh0XSeVKrx4jo6Osn//fgYHB9mxY0dzfzytuzU72AT0JQqxLQdPpSlHcwuh2t6mZLSAxW1oewXkz3wknbcUNqXMBdpyvIIa8VTaIZG0iYQXGP6MMqbxm9hLp0ZL/oxp28FxPT76jad5083bK75XCEEgEGDbtm2Z96XTOcvN2bMZhj47ke3t7W2aqX7vZx7l0PnG6xvTduNV9u1GI9XWmqbliJetW7di23auBSvfMpZ9TSssTZe6MqYayrViZY/38PAwlmXR1dVVkDdT7lgtNoAumUyilLpkPM+XcfHB8zwOHTrE2NgY119/PefOnWuZTagZLIaMmZubY/fu3QSDQe66666G1W7VbEpffvQFPvLks3iL9IxrXvnoQZW/yKwURmdmzCeQ8w1IDSpjiswfWUWMavBzKmfFZKDnkzFC4XoazLdC6VrpRLgvkiijlgGUojeQKEvEAJiapN5gkfyMmMz39bij+zhhvbbaxFOCffG1ZAdLEq26T6tO+N0OfjARz/scScTn0OFP49e93MPXR86iCZiTwaa39ZP4ZtJiEZNlFeRYMsScN1P1ZT/TeZx/ndtS8rijXEJ6JylZuqjmSPjtNU9iaq1tTMta2Ky8FiUPjbQyCYr8+ZPGBbWLk/aCskQpCAoHfx1EXTPQ3C72WRpW3ncOiR6SMsyUU1+9dIaI6WXG3Vv3dpO+E0SNnZyQNpCe/5zmz+N1/nW8a/27COitUaIsVlUcj8cvWVXxJUPGZG/mnucxNDTE+fPnufbaa3O1Vc1ALWNmjFHBKrQUcGUSKD3hhVBtD/BdLIvbKMy8oOTscEG1lYwptCllyZisMsayXWIJq4CMkVLllDT1YveBsyTLDIBStstD//ADUsiatZv5CAQCDA4OMjg4mLOATE5OMjo6yuHDhwkGgzlipl6VwX//6hM8c7S29LIcrBoZUYtBI6RKK94HGUtTvmUsmUzmyK9WWZpWembMYldNGkHx8c7Pmzl9+jRASd5Mdh9hcWQMXDqe58u4uBCLxdizZw+GYbBr1y6CwSBjY2M1M/faiWbqpZVSnD17lqGhITZv3szWrVubuvaWsyl5nuT3PvEtfjR5DszFjwUq3WflPOGh+1zMyELWimnkQk8a245WqHPJKmOq5ZEWP6UUiBpV2Fre80JklDLufHOSEBKRVyHlNyw6/KWTcKUUq/xxwmblRcd61eGel/nu2WOmFNzSdYpus7a6RSk4kurHajKctxocdyNK2GjCozuQJuKzS0ipXiPG+mAmv0QXElcKjAaryF9KrGe6zHi9XnhKMJZex4w7U/V1Px05xZu6jvOT+BqmyxBHHfqaMmSM4hejewnqrVEaFX5yBnZRDXVc+ghqC+fVLK9iuEiJ4hcOoSpE3WJ+9aboZEStI60tVEmbdjfjHlj6VF2f0QwRoys/lrWVp9zCCutmx6RrfGv4rfW/RUhvnWLychNlKWZmZnj22WdXJhnT6MmTzYxJJBLs3r0bTdNa439exswYjdZfvOqFwkEXRkn181JkxjRTl7YYmGW2J2X79sEuCvAtttvYtks8UZRhoxbIm3rxlTIWJQDpSn44dAo0UE3k0EChBWTz5s25VpusysC27ZzKoLe3t6yq4+++8yz/uvto03c9y2mfTWk2YdEdaXwloFXKEyEE4XCYcDjM+vXrCyqdF2NpuhhsSsuxf0IIQqEQoVCIdevWIaXM5c2MjY1x+PBh/H4/PT09BIPBgvrtRhGPxy8pz/NlXBxQSuUa9DZu3MgVV1yR+61Vy9xbCjSqjHFdl4MHDzIxMcFNN91Eb2/vorYtpcwR6ecuzPAbH/8aY7pds7a6XlQiY4Tp4euy0X35L5BN3xNFEYkjs1kxZTJjcnf+onrrWqoYKFTGAJiGh2tnphKFl0XJ6q54icBEKUWPmaTTV33B0ad7tXJvmbMyJErE5+Q++4aukbqrocedCON2aRyBVGJRf37d8/HsbILVkRRBwy0rslEKru9YIAl0oTjvdrHeN1P3dvYn1jDidTct4pFKMJJaS9yr3mB6XXCMt0WPIoRgZ3CKpxNrS15jydIxyBs799NhxEsebwX0MpkxkMmNyYb4psSr2JssJGICwkdIq75PGZVZ4wdVFwEm3C2Muwtq715jM+NyFKuBMOo7u/uYcffU/fouYysHZmFWzdZ+cR3o9/Xz7g3vJmK0dtFosWRMttZ6JSu860F2rPu9732P//bf/hvxeHxlkjGNQtd1kskkTz/9NOvXr+fKK69syaBeLWNmjNaiTvhm4RMBUqrwgqVpS0DGtPXTS+HLrxCfH4O0VxlTVG09TyosKGO8EjJGqiaUMWcuUM7x9W8/HM41PKgGP7MS8lttsiqDbAjwiRMnSlQd33xmiC89tW9Ryw+W076V3KnZRFNkzGKUMdVQXOncrKXpYrApLWe1ehaaptHV1UVXVxebNm3C8zxmZmaYmprKWTqef/75nHKmkXyfS83zfBkrD8W/cdd1OXDgAJOTk9x444309fUVPF8pc2+p0AgZE4/H2b17N6ZpsmvXrkWTmtnfoVKK7z1ziD/59hPYbW5PUD4PtSFNIFI6SdebsCdlIXwS5S0QxfUoYwr2SxWqXoqhCUnQdNCK9DSm4aEpG+lqmUYaIXjVYD8n504T9hXep5VSRM0k0UDtxUZNKIQSKFG6T1LCrB3IhAnnfcGtoXE2B+vLXUlJg+Fk+eYwT2loiyhf3j/XS0+4OiE04J9hwF/4mikvzHpm6trG6XSUE15f0/d0qeBMqo9EjXHgenOWd/Xtz52rm32zZcmYs+kxugwDKTJ/89dFhogajWUBNoL1gc0gJwpsSpBpVAJwtF28kBjJPS7QMegjKGrXPDezTCnQSaprOGsv2JBWmds5mjzP1aGzzLjrqee3fVskymydRIyOiV+7jmdmRlsWO9xr9vLu9e+m02i9M2SxY7x4PH5JKGOyv9lYLMa6dev4j//xP65cMkYIUVffu+d5nD17llQqxU033ZSrOW0JljEzRqg4y+ki82l+UrKQjFmKzJjF+BubQQEZM49c8F0bYEmrQOVSrPDI2JQKw+6UUg0pY557+STpMvWS2wZ6eG5oBLp1UArHaj2hka8yyKo6shPZ06dP8+XHfsI3Dk4suj/QamOA79Rcii2lY42aaBcZU4xqlqYTJ04U5KNEo1H8/szgZKXblFbq/um6Tm9vL729vfT19XHw4EHWr19fQIbl581EIpGK58HlJqXLWEpkM1UCgQB33XVX7lqQD13XSacXV6m7GBSrUyohGzi8YcMGtm3b1pJrha7rKKX483/4Lv985jgsMh+mHHKLH0jUOhu916KSAFjXF0HGaIAlchl0C+MYUTECJX+UUKqKUZi6S9B08Rlehhwp8xl+04N5dY9A4UnB/vgxoqHScUy3kaKnDiImiw2hKKdShdYOxxMkHN9Cq9P8Pg36p9kRri+PI5MTs45Ky3+O0jGbJGMm02GSno6/igVLKcUNkdIQV00o9qcH6dETDJqVVQ7TbpC91lpEk/IdpeBsqoeEV70dp1tL8d7VL5LXmM5as/xCsSUteswNTLjHeXXoGAO+5sKI64eLqwRe0apjQvqR4maeTVzIPRbS1nM2BVuDh+pSEZWPtq71pps5mlwICO43r2Y4cYqAliaiW/iFU9MOd70ZJkF9Yb2d+npOpTo4Z9c65+v/JlEjygPrHyBq1h+C3ghaEeB7KYyfsve5X/iFX+Atb3kLQoiVS8bUg+wqCYDf728tEQPL2qYksDAI4dLa0Kt64SsTBiaWoE1JlFkFaSfMMlfmdpIxtrJz4b0AtuuSGSzN25Zsl2IBpVKqoTal//P9Ulbd0DXkmSRifjtCgmW1X5qeH1T7k6EzfPPQ3kUTMbDQQtUOzMSaswg2YgNqFXHTiKVpOa0I9WApM2Oahed5GIZRUFmeJcOmp6c5efJkTsmUJcOCwQV//WI8z5/85Cf5q7/6K0ZHR7n++uv5+Mc/zm233Vb2td/4xjf47//9v3P06FEcx2Hbtm383u/9Hr/6q7+ae8073vEOPve5zxW879577+XRRx9tav8uY+VAKcXp06c5fPgwW7ZsYcuWLRWvN7quL3tmDCz8toohpeTQoUO5876V47x4wuLjT5xkNFi+froVUBrIXhttMF3VAiQWocTIwdXAlJlA23x7Ug2OZ0EVk1G/+E0PU/fqOiT5E1elMtYXn+7h0wvtV32+BF3+xrIQO/TCcWja0bGkgVG02NRjxrmu42xdE22l4FhqFZasPDG2pUGoyRKNkfgAUlYft28ITNHrK20t0ufHvzMyRNi16DJKSdKUNHkuubl8J3kdUApG0t3EveqqMh8u/2X1y/iL1FIh3eK/D/5w4W+ezeohs5g66ur4tfYr+yV2iUUJMiG++6wQihkEBho389zsOV7RNVS3natRMsav38ZL8QwRo5RglXkNQ4njAGwMTCIEhHQby618zt3Z1c+cVz5eoHDfdCL6dTw7O46kti2pHlEDQKfRybs3vJteX/O2z1poRYDvpaCMyS48fvvb3+bgwYO8/e1vv3jJmLNnz+b8z6tWreLll2ufxA1DhFD4EMtEiIS0EHM1LurtglGmIk9bgsyYujW1LYKRP9qYH7zINitjvBJlzMKxtmwXq6gpSCoKCJxqUEqxd2S8xKJ03cAqjv74DHTOB+1JSKeXTpp+6PQF3vf577VMStmuzBilFDPx5laJGyFYEmmHSLD1evhiS5PjODnVjOM47N69m+7ubnp7e4lGo1VVHEuNlaqMyUfxYKIcGTY3N8f09DTnz59neHiYQCDA0NAQpmmi6zqhUKjhY/7Vr36VBx98kE996lPcfvvtPPzww9x7770MDw+XnZz29PTwgQ98gB07duDz+fj2t7/NfffdR39/P/fee2/uda973ev47Gc/m/t3OeXEZVxccF2X3bt3MzMzw80335xrEKuElZAZA5QN8U0mk7kFtzvvvLOl9dvP7zvF737pX0gG2zfmkBEXuSWNEax9fI1FqGKyyK5lFY9hFAJR4e4r8AibLn6fg66VV79Uw86uVRycy2ZkCHQknQWki2R1IEbYaHy8EciTZMQsHwhKQnA7jSS3dJ7MERm1MOlEGLO7q74m06gkqShhqgBXwrQNAb3K+5Ti+o7axQXnnG66jELVg6s0nk5swRHNT91GrS7m3Oq/Ix2XP1zzLOvM0rFQp+7gF+X/lmOugU9LL7aIqi4olS5LxgD4tE5C6OyLrWXKPs9reobRGlnobeC1YeMmnoudm3+bTtTYweFkhojRkHQbGdItpNlMV/iMeokYw40ykogyoV+o+dosVB2j7oge4d3r302/r8WChiK0KjPmYkd2rPulL32JSCTC6tWrVy4ZU8mmlB/edsMNN7Bq1Sri8Xj7BhOiC9R4ez67BoJ6iDk5syzbNkSpfHEp2pQaqvhpAfJv4Dmmv61kjJ0L74Vs9olR0KbkFBENqoE2padfPIFV9CfqCQc4/cJ5IO/wSkXaWhoy5syFWd7999/GaxkV077MmGTaafq4NELGxBLptpAxxTBNM2dpunDhAldddRXpdJrp6emcpSmr4ujp6VnWyfhKyYyphlqDCU3T6O7upru7m82bN+O6LjMzMzz++ON85Stf4ezZs3R1dfHHf/zH3H333dx55511NWN95CMf4f777+e+++4D4FOf+hTf+c53+MxnPsP73ve+ktf/1E/9VMG/3/Oe9/C5z32Op556qoCM8fv9DAyUz024jIsTWcLv6quvruvcWky1dCuQr4zJx9jYGPv27WNwcJAdO3a0lKj95D/9iM/u3Yf0t+NeL1E6eFvTaN1OgcWjLJTCnBOIsIBFEkPZeuviMUzhUFoSNDMKGNPwytdON4CYt6AklQq6AuncZyqlGAjGmyJiAHQkUsKc7S9RwwAENJtX9x7N1GDXgbQ0OFQhJ6YYqgnT/IVEJhek2t5sDV2gs4zipRjTbmlj0ZOxraRE8/foMauDaafWZFbyewMvsL4MEQMQEhJnXkjmyIwuxtAEk55OTLm1z/cWQapkSV5MFnF3kO9MTOAX47yudwijwXlFvV8hYlzDc7EMMWLgJ6Bv5ljqZO75tf6p3BzDr5X/DdzRtZo576Wae9RlXMcLiWlsvTF1maqhuAtpId69/t2s8a9p6HObwWLHeJeKTSmL0dFRXv/61+P3+5c8L3VRiMViPPPMM6TTaXbt2sWqVauAhcFEvXKshrCMVqWgtnwTI73MisBStCnRCqluAyhYLcoG+LaxTalSZkw+GRNPFrcpKTyvvnP7a2UsSqs9H/Z8PkyWe1oqZczkXILf+MQ3cVRr/66JdHuq3+NJq+HmqiwaImNSS694U0rlFBzXXXcdr3zlK7n66qsJBoOMjIzw4x//mGeffZYjR44wOTm55BO0i0EZ0+hgwjAM+vr6+OAHP8j+/fv5kz/5E9atW8fJkyd5+9vfTjQa5fWvfz379u2r+Bm2bfPiiy9y99135x7TNI27776bZ555puY+KKV4/PHHGR4e5lWvelXBc0888QT9/f1s376dd73rXUxOttvnfxnthqZpOUVUPVhum5IQooAQklIyNDTE3r17ufrqq7nqqqtadl1I2w73/Y8v8w8H9yONxc4aS+/JSndR25KoG2PoUaeqQkDYEJiCiPQwTW/RRAyAmCcsipUxGh4hn0U0lGBVJEE0nCLgcxdNxABM27Hc5NXQPHzGwpimx5cgbDR/r0vJFLN2oCwRo+PxU72HCWj1nbtSCfbH11JvTYQq00JVC6cT/qrv1ZTk2shI2eeK0WlazDoLc4Bn5jaTEKUETb2YsCJM2h01XqX47f4XucJX2WakCUjPkyCj0iCmNGY8jSkp61YntQJSxUuUMQKdtHwF3xqfpN8I8dreg/i09oxjIvoVvBiPoQC/iKCxltNFFdpRY8GK5i9znt7RtZpYDSImqPVgy+t5emYCWzX+XarNiwNagN9a/1usDTQRktgEWmFTuhTImOw8Ye3atbz44ouk0+mVq4zJh1KKs2fPMjQ0xKZNm7jiiisKJj1Zn3Elz/Gitq11LbVYI4dAGXXKUkEvM4pYCjJGNXGxWQyEKN1ei3mDAqTd9HxOTAa24xacy7btEouXtimVk3AXQynFgdHJAouSGXMZOTmR/yIgS8a0dwCeSNv82ke/TqoNA/3DR4/xXNDOKTq6u7tbMmCPp2ycJkmIxpQx7SGTqqG4TSnf0rRly5aCivKhoaGGgmlbAc/z6p5ALhcWm2sjhOCKK67gC1/4Qub3euAAjz32GNFo5cC8iYkJPM9j9erVBY+vXr2aoaGhiu+bnZ1l7dq1WJaFruv87d/+La997Wtzz7/uda/jLW95C5s3b+bYsWP80R/9ET/zMz/DM888s+IVSpfROiy3TQkWFtTS6TS7d+/GdV3uvPPOlg68h0+O8a7/9QgzvtZ8Vz3i4MUXrlde1EHfmkAzJV6Zml+YzxWTAp/yMCMezBeWyJgPWPx+CVMCGkpC0G8RCVmYhotpyLZZR1zl0W12MJFOEg1ZZDPwomaKqL/5YGhb6jw/ncAsey2S/FTfETqM+u6jSsHx1CpSsv4FTldp+BpYHLTtKJbMWM1kBW3FleFRQnp9i2CaUBxOr+ZW8zR744NMilpESmVM2SEu2LXbcX6zbzfXBmo3INlKI4THhGeC5pLCw1ziiZIiiaUW7pu6CDBq3cJzc3NoeNza9RLBCmqUWqj1Uwnp69iblLjKI6z1EndDTLtjJa8L5G3fV0TG3N41QMx7sep2uo1reHkuQVI2786YS8yVbVb1CR//ed1/ZkNwQ9Of3SgWO35KJpNs3LixhXu0PMiOr971rnfxK7/yK/z+7//+yiVjsoN+13XZv38/09PT3HTTTfT2loYLZb9YO8iY5VTG+MvktiwVyv1cNE3hSR0pm84OqwnF0q7QKZW/vcJKyHYg5ab5yU+eXfj3vCUmp4yx3JJqa6VAytorDk/85Ah2/kVXKvzTHvm3luz9UkjVVmWM43r86ke+xpzVHgXImrXr2LhxI5OTkxw8eBDXdQvsNs3kcgDEklZDYcn5aIiMSS5te0n2/Kp2IyxXUZ7Nm8kG0+ZXlLfa0nQxKGMWu7KTH+ArhOCaa67hmmuuadXuFaCjo4Pdu3cTj8d5/PHHefDBB9myZUvOwvT2t78999prr72W6667jq1bt/LEE0/wmte8pi37dBkrD8ttU4LMdWl6eppjx47R39/PVVdd1VJC8CvffZGP/OgnuK1qS9IkRo+NF/chAx5yaxo94qJrEiFUKa0iFYYAn89FK1J5KFsgu1tx/CX+gIepS8zIQviu42mLJGIyBE81RM0gk+kEmagUSdRsrDWpGAnXx0iyK5PdUgLFXT3H6anQ6lMOU26Y8zVyYorhKr0iGWO7ek4BlMXwzGqYH7+WU8YYuFwdOVfyeDUklY8TqV5Oyt6m86VnnSCjVu15zC/27Oe20FTN1wEoNGY8gY3OsB1mq3+i9pvaAFtlFqx9ootDyasZSswBknt79hPSmz//qsX3+rVejqQipGScLn0t47ZL3Cs9biZugT0q///f1jlAvAoR4xcd2GobP54pJXgahekzSnheA4Nf6fkVNgc3L/rzG0Erxk+tzA1bbuzatYs//MM/5GMf+9jKJWNgoZYxGAyya9euioN/TdMQQuC6buszD0R3az+vAfgaDA9rJcqFvWWbjlxPb5v0T6qlJWNkXmL+UmTGeJrHjp1XwWMnAYjFkyilOHnyJKtXryZtlyNjVEHOTCV884eFlXiBSQet+Lvk25Sc9pAxSinue/gbjMdL2wJaBVdSUO+cSCSYmppiYmKCY8eO4fP5CoiDeknajDJmKciYpVXGZJVV9ZId+RXl69atywXTTk1NMTIywqFDhwiHwwXKpMVOnv69kDGNrvb39fWh6zpjY4UDs7Gxsap5L5qmccUVVwBwww03cOjQIR566KGSPJkstmzZQl9fH0ePHr1MxlzkqJS5Vw7LTcZkbLgehw8f5qqrrmLdunUt/fy//Ox3+cqJo2C27r6uR1y0gIu7JYXWa5PNa9XybD9KKnQlME0XM1D5niKnDFjV3D1HF5nWI1P30CtUT8sm7Db56PMnmLCqqzImLYtoMAVIVvnjdJjNL8JMWSEuWBEq6RNu7DrDoL92i0wWltQ5mGg8D6NSo5InRQkZoymDCdvLLVKW++ntDJ/H3+C42dQku9PrMKu0cFVDzPUzku6mltbjjd1D/FS4/ol/0vMzOz+YTMvlmUYKArj0EtS6eGZmAyN2HFD8dPQQPebixp6VjpYpIozYg8x40xh0cs5KYcnyY7keX7zg92jMq/Bv7RwgISsTMd3GdvbFPOa8xRMxAEIXBWSMjs7d7t3MHZrjSZ4sWMTMb35sB5Zj/LSSoZTiP/2n/8S99967csmYM2fOcODAgZq1jFm0S2qrllEZ41vimud8lIsgy6aRu66Bz2wXGbN0DT8l25svM2jGK1z39pAYgQVps9ANhBA5n/z582NMzTicOXOG3t5egsFgXTYlpRSHLkzl5IhaWmLGFcWjs8UoY+olG37rb/8vp6bqHyw1g/wAXyEEkUiESCTChg0b8DyPmZkZpqamOH78OAcOHKCzszN3w+ns7Kz4PRIpG9ddAptScmkzY7LnT7M2o/xg2mJL0/DwMJZl0d3dvShL08UQ4CulXJT6MpFIlFV3VoPP5+Pmm2/m8ccf501velNuPx5//HEeeOCBuj9HSollVSYBz549y+TkJGvWtD/I7zJWDrJjp0auX62CZVns3bsXz/O48sorW07EzMwl+N6Rw+Br3XVFoRBrU4g+G90uur8KBQoMJfEF3LoUxJUallXp7Tv/XUR8FmaZLJWCV8nFkzG9/iRzThC7wqRbSojLObpNj1X+RNNEjFQwlu5k1qk8IdweHmVrsH7Lhqu0hnJiCvYHrexvwvU03CIb2lRyK1oB0VL4Hr+w2REubEaqBaXgSKofWxmsMWpbh4qRcH2cTfWU7Esx7u48xs90jDSkntKAGelDF6ptC7PVoGs7OWz5kaKHxyajzLoZFcyurqOs8TV+rIqR/R0XQBpMs40xZxxPCtLSxZKVv3tXkTJHR3FL5wBJ+SJSiZJ2J1MEEexsiRomH14eE6Ohcd/a+7iu4zqUUrkFtrGxMQ4fPkwgEKCnpydnXzfN1kZlXCZjCpG9tmzatGnlkjGhUKiuWsYs2rW6YzkBlksUtdQezEKUbjt7sW5no5KXR45UH4y0Bq4qMzlpMweWdhe2absZD+XmzZsJhUL88xPjjFyYYHJyMqfwSCVTeJ7Edd2KE8HHnhrGyfuzBCfdsgNrkZcZY9seUiq0OvWvp0an2bSm+u/x/f/4XfaP1F+71yysKoSJruv09vbS29vLtm3bSKfTObvNmTOZgLV81UwgEMi9N5W2mw7wLc5kqYalVsbUY1NqBO2wNF0sypjF5NokEgk2bGjco/3ggw/y67/+69xyyy3cdtttPPzwwyQSiVy70q/92q+xdu1aHnroIQAeeughbrnlFrZu3YplWfzLv/wLX/jCF/i7v/s7IBOE92d/9mf8wi/8AgMDAxw7doz3vve9XHHFFQVtS5dx6aOtNu8qmJqaYs+ePUSjUTo7O9uSF/U/Pvcd4iFBNfezRKLVOVmXnS5yc5pQh4We3+AjQUNlJlgCzGCdobIzOqqr9H6jlMKQCq9CRXKn30ITkloT7fm7fV37UgmmJon6koyly2eOxC0fqyNxuswUHWZz9zVXapxNdZH2Kp8D64OTXNMAaaAUHEn2k5SB2i+uAIFG8YDQlTquVzgG3jdNAd9TPIS8Onyu7sanLI4k+/E0g7TT+IQ45ZmcSfWgavztd4VP8wtdJxuyQE24AY46Jr75RqiwbrU1uiAfgiAJcSuHkqcI6Vv45wsGlsqQfzdFTrI50CK7VAmBqTGT3MaIPo6UkPJ8COFhCD9uhZzLcFHrkRAg5SEAYl6ALmOBrOkytnA4bjJRJndmsXCkM/8NNH5t8Ne4ruO6+f0RdHV10dXVVdD8mF3ETKVSdHR0FCxiLnZ8ttgFt0ul2jof3/jGN3jyySdXLhnT19fXUMJ/OxoBzp49S2x8kus2tfRj64bZgkC35lF6LHM2pTaSMVItrKo4toHP317bkiPTQOFEsZ3KGIB0nqwxv01JCIHrKdKWy7XXXgcoZmZmUN85iet6PPnkk3R1ddHb21uiPvjWEwsWJd+sS8WMuDybEmQalUKh+gbBu4fPVyVj/uL//Iinhk+3n0GjsWrrQCDA4OAgg4ODBasB586dY3h4mFAolLvhxFNL06Y0l1jazJjFKmOqoZalaWhoqOAYV7I0LTbcbSnQysyYRvC2t72N8fFxPvShDzE6OsoNN9zAo48+mgv1PX36dMGxSyQS/NZv/RZnz54lGAyyY8cOvvjFL/K2t70NyNwv9+7dy+c+9zlmZmYYHBzknnvu4b/+1/+6rPXml9EaNPI7X2oyRinFiRMnOHr0KNu3b2fDhg288MILLV9MS6ZsvjtzHhky0GOVX+d1gFbleQBlSrwtabSuTFW1ps1bgjzw6RIz6M5Pthpby1EpHTpKv7c530zj5c3wdeHhKR2f5hIwXFyp1dyWVGLRi0u6kHSa6bJkjOXodPhtus0UfYHmrCEpz2Ak2Y1boaYYoM+McWvXqYZIgwtOJxNO7dDaaojoq4jLwsUl19NxvYW/S0htwBNeCe2RJSjCWppt4cYWqGadAJNeBKGBX3fxJFTg5UqQdAzOpHsr5O0s4IbgeX6l50hDVdSHUt2MKSdHxEAmC2VWBolqi8loqQ5Pgs+4isOWyYx3Cp92K18bs3K1zTtDI+wInW/Z9sLGKmx3Ovdv6d3AiH4BpSAp/bkhbkAEiKty2UWqbMtXUOsg5aZIeH469RSGMPFr1/LMzBiK9izQecpFIPilNb/ETZ03VXxdtvmxr68PILeIOT09zb59+5BS5hQzzeYyLmaMl40j6OhoPsh6peGzn/0sf/zHf8yVV165csmYRtFKZYzruhw8eJCJiQluu+7qlnxmM9Db9OOsB6qMV3YpyBhX2WS9No7VfjLGVimKyZi2K2O8hRuZ60ny42AsO/N94wmLrs4gvb29+P0BIMkdd9zB5ORkTn2g6/r85DbK8MQ0GALhKXwzhaG9+ciJreY3mrbqJ2MOn7wAlP89fO+Zwzz+3JGW+vKrIUtiNYri1YDiBqHhI+dJOJnJbU9PD+FwuO4bTiNkTHwZbEpCiCWxIdRjacq2NPX29uZIxYtFGbPYlZ1mZbYPPPBARVvSE088UfDvD3/4w3z4wx+u+FnBYJDvfve7Te3HZVxaaGvmXhEcx2Hv3r3EYjFuu+02uru7gfYomz/yxX/B7syoCgxHoszy1xY3ItEToMnS5xUSb6ON1m8tTIY9hedoUmsyDgABAABJREFUoCki4YXreE7JW+f4QbngVQjuDZkunhQFI0BDU0jPozOQRojMeKzWwpEqm/7XGHShCBql4zApwXJ1NnZMsSpYf5huPmbtAKPpzqoKjg49xV09xxqqTU54Pg4n+5vap3yE9K4SMsbxtAJlzHPjHQhRPFbPNCppKK6NjDS071LBcGoAMc88GZoi5fUS0SZrvnfCCmFJX81l3B3+ce7vO0i97e6uEjyb7EHpibLvmfWCRI32kDGCEHMMcjyZAAQeu/jmhXju+U2BC9wQOd3SNcBsTINUClO7ngN25hxIeWbBmerT/CBLz/2wli6xIQEEtCAJz4dCENE3cDYd4ZzdejVMPlzl8raBt3Fb120Nva94ETMejxfkMpqmmVtgi0ajdSkbL9uUFiCl5L/9t//GH/zBH/Ce97xn5ZIxjU4aWpUZE4/H2b17N6ZpsmvXLoL6PojXfl87YKdmlmfDgFKlk8UsGdNOm1LGNpQxhrmOjpSiIBSv1fCUgyH0jNRwfjNLqYwBcD2Vs5HYduYcjs2TMZDxqHtSEQwGWbduXU59MDs7y+TkJN989EXc+TtkYMJBq7b/xcqYlAOVW3VzmJpLcmGq/A9hYjrBR7/wI1R7s78KYLVIBVdst3n25L+Rnphlenqa48ePF9xwenp6qnpoV3KAr1Jq2YiO/GMMGUIiu+Jy6tSpXMW2bdstVze2GpcHE5dxKWIp6q1nZ2fZvXs3kUiEXbt2FQzedV2vmYvWCBzX45FzJ6E7sw3hKVSlS7cAu0dR7HDw+mzYkEZJDc/RMHUPn09iBjyEqxBCFVQYz0fO1Q05aUBfmbGNVITDFqm0r6QFpdNvoc+PhzShqJXrnxnLFO5VI5YSg4zix0BS3KqUtE22dE3S3wQRoxRcsCJM29VVgj7N4tW9RxrKJfGU4GB8Dc3kxBRDo/SkcT0dd564i+hRLlg25W4JnhT0mgk2BRuzzhxOrEZqhR844F9PXFYnY86nu/CUhiag20wx45Q/tht90/zWqn11r5tNuX72WCFMvfLfOSUrj4uS0kdIa27xSdeuZtgyCHEcjdXMuLfz5MxCJsyAb5o7Oo833TRVCVKlSXiZ/T5vR4Ep0p6BVIXNZH6t/Peu1PRlCIOElyG8z1m9nLPba+lfH9jIm/v/XzYGNy3qc4QQdHR00NHRwcaNG3O5jNkx3IEDB+jo6MipZrq7u8uONy+PnxaQSCQYHx/n/vvvZ3Z2duWSMY2iFSsr586d48CBA2zYsIFt27ahaRrKW74AX1NbPmWM7ZRKTrOkSLFftpXICHN1JB5SariO3nZ1TEj3M+e2r/mnGJZXeGNy86L385UxWSiVaWYAmJ5NEu0K5Saw0WiU//m/XwZAT3kYyepBOwJAqgUyxqrv2B46cQGnjBpFKcXv/s9/xvMkUi3dZN9uUhlTDUIIXAk+X4Drr78ez/OYnZ1lamoqd8MpDgLOv+E0ZFNahjalpQ7nrIRiS1MsFmNycpLx8XEOHjzIyZMnCyxNS5ljUQuL9Tw3a1O6jMtoBI3+1tvZqKSU4vTp0xw+fJitW7eyefPmkv1r9fb/7qvfJ9WdF5Rfg7XwQgvPe0EXtTWNFvLQNY+AkISCNrZtIOc9HbquIGeSyCA/466evLvMZ5Xul4lE0xSG4eaRMRJD8/DnN/iI2veccgtLrtTw1ZlfEjIyCmkhIOpL5cgTTyo2dUwxGK7h7yoDTwnOJbtyk9JK0PD46d4jBCt6rsvjWKqftGqNwquc1SdjU8rcA8bi69ArVCh7SuP6jjMNEQXTTpBpFS45d8adSYIVbjtSwtl0FEMjty2BIKwHSHiFdujVRozf7d9NoM4FzsPpLs55LmaNmmhdVFZFzMhgw2SMIERc3MJQ8hQCSY/pcDp5G3tiC0RMjxHj1d2HG1Id1YOAtpEjqU2s9/8bZ60oaenDkQJX6iV/F0OUH5t0GuVt6Gnp4M0r/80KRE4rENE7eP2qn+e2zjvaMu7Lz2UEsG07lxl48OBBXNctKHTIjnkWM36SUl5SmTHZTJ6JiQkikcilRcY0u6rqeR5DQ0OMjo5y/fXX51ZwgWWttg6YNiytmyEHx02Sq+aZx4Iypr2njeZpSN1DSYFrLwUZYzLnwmKD7upFcRVevjImS8bE8sgYKReeP35qgpuvWwgAdRyPI9MzoEGgQmhvMYRaGJxWalSyHQ/TXPj7Hzs7gVMUmrvnyDn+5ss/ZmwiNr+fhStn7UQjmTGNIG25ucyYrA0sGyJuWVbuhpP10OarZhqzKf37UcZUg6ZpOdvYyMgI11xzDa7rMjU1xZEjR0in0zlLU09PDx0dHctKKi1mZedS9DxfxqWBdmTuQcbyvX//fqanp6sWMrSSjFFK8aXDh6BnYYKYz0koqXIWkMwDgA7SkCgU6ooU4c40QZ+LqS+QFn6/Q8L2oWmgZcmMMvPAWpcn1wU9paG6yxMiwXkCxDQ8VDpzTxEogqZT5rPrCfAtVMM4no7PqJOMMRfGB905MkaxNhijP9S4ZNzydM6munFq1iFLXt17hLDe2AD4gt3BmN26BVRPlR4nV2ZsSqbw8dyEjVbhdtBvxlgXmKl7W1IJjqRWF56b84h5s3QZndiqsCnIlYKzqR58euF+KgRbQv3si53OPRbVk/zB6hcJ1UHEKQVDdpBxlcaoI1RmwN8NnCl53FEaCdkYMaZpVzNs6cx5pwDo0PzoKPbEZsmOL8NaitdED2G0sOTEFFGmvev47vQ0imkGfYLTVi8h3cbyfGV/14a2cB4bQmdDYD1j1jiRkoAfQUS/gZfiU3nba/2itobGK6Kv5p7e1xPUl06q7vP5GBgYYGBgIDfOyVrTjx8/jmEYRKNRlFJNX+eTySRKqUtm/GSaJm94wxs4cOAA99xzz6VDxjQrs00mk+zevRshRMaWVNyzvoxkDCTIWHaWvuJa93lgF14stCXIjAHwG35c5WTIGMeANmfnaG7me6klOsyWLFLG5K3aWfNKlXh8gVl3HC+3b6dHZgrImO/88ACeLvBNO+huvTUDCzal2bnycspEysF0F250Z87P4OT9+ztPD/E/v/IjDCtvOLiEp2mzmTG1kLZdvApyeb/fz5o1a1izZg1KKWKxWEEtYJYk8Pl8NRUdS21TuljyWHw+H9FolFWrVgEUtDSdPn0aIUROClvchLVU+7iY47iYzJjLuIx2oR3KmFgsxssvv0wgEGDXrl1V82g0TWvZ9j//rSeJ9RSu1Ku8S7FuS2SgaAwjJNrWNOFoimDIRpT5iWcCewVKSHRDZu7JFXa5kmXJcTQcqYMBQsoSe4tSEPTPt59kYmlQAiI+J2dPagyiZBLplsnGqYRAniolo5JRrAnOsSrQuDUp7vi4kI7gqNrTjjujJ+jzJfAasIwnPZPDydUN71c1WLKUoHQ8HUdqRLgCRCWySHFb9ERD2xoqY0/KhyFCBWRM2jMYTXeVEDFZeHlNoWEtzR8OPE+nXvs3ZikYdkzmlEKrc+HDUzNlT/iU9GFXCWbOh/T8xLmFI+mzucdC+nrm7FFWG5mq6FkvjA+He3sPtKxSW2CixK08Pp0mqRZCeyecDmJeiD6fznTRaaAh6DHDRHWXhDmLX3OJ6IINAcWWwBwpuaAk8mu9zHobORArzIbRW7yo1Jfu5z/ueCer/QMt/dxGIYQgEokQiURYv359LlJhYiJj13vuuecIh8M1Cx2KkUhkrjmXyvgpGo3y0Y9+lDNnzqDr+solY5ZCZjs6Osr+/ftZu3Yt27dvLz/IFj4UQQTtSwqvBIEkpIVJyqUPrZEqDRQOaJYiwBfAr/tJuHGkEjh2e7cFZJaqlnCx3S7K43HyM2PmFR/5Aa/O/GO27XJubLbgvd/58SGEI/HP1a65zEJIlSNO9u09gFATuYam7IUxZbmk7YXf0+hEDHdeGfP333qWL/9gD6ZVtMUlJGPSbVLGWI6LrGXEJ3N96uzspLOzk02bNuG6Li+88AIAhw8fzoXUlmu+sh23bcqeSlhJNqVyUEqVJYyCwSBr165l7dq1OUvT1NQU58+fZ3h4mGAwuKSWpn8vnud3vOMdzMzM8Mgjjyz3rlxGE1iuzL0szp49y6FDh9i0aRNXXHFFzf3RdR3bbo0M+B9eehl6Cy0Anl+QFQTkW5ZEh0P3uhihSBpDr33dNzSJK8V8gC4oR5VVMuQjq0rxPIHt6RmFig4gsB0NQ/dyqhWhJEaeakVTCk3zCJrlFayZMVk1m9J8pa7KhMkCeF79ZIw/rxHGp3kMBmebak2asEJMWBG6zDR2jarm6zvP5BQlmWDf2n8XTwkOJgZRLVbmprziQc6CTenx86Ji9s72jlEG/PVbuCbtMLMqVFVVZYhQ7v/POX5mnEhFIiaz75nJqx+H9w08T49ee8wx4RocdXXcBgfEjpoGuoGZgseTyoeDXtO259jbGLY1LGOBiAnr2/nOeJg39mbqoHuMBDEvwOv69hHUGrOuVYJgBy/Fuxlz4kWPe5xOR7mjayMaMaScwac5+DSXTkNHI42aZ2K3LfxZiHuFuT4d+nXsScRIydKQ3nIBv82gx+zlno7XkzyYXnYiphyykQrBYJAzZ87wile8IlehXVzoUE39nEwmMU3zkml7PHr0KD/4wQ945zvfCbByyZhG0cjNXErJ8PBwThY/MFDjBBZdoJaejAEI65FlIWMUHobwzbcbZZAjY9qYGQNgzHsplRRt3xZAT0cn5+J5A4w2kwp2sU1JZsN73ZwCJt+mlM1HicXSXJiI5T3ucmx2jsCEi2hgBUnkcU+bt2xj69ZVTE5OMjQ0hOM4dHd3MzkVwzDNnPVmaiaJJgR//P99jycPnMKwl5S/KkGrAnyL0WwWjWEYGIbB2rVr6e/vz4XU5jdfRaNRent70X0hlALPk+j19lUuEsU2pXjaJhKor0VrKZAlI6sRHfmWps2bN+O6bk4Ku1SWpsWQMVLKy5kxl7Ei0SpljOd5HDx4kAsXLnDDDTfkFG5Ltf1Hvvcck72lk30vqKHNizk8n8TcEiO4Jokv4DbUwuL3O7iphcmAUqLsfVDlcSSOo2OaHmnbRCsifDwJnjQyGTG6JFA09tCFR2fAqriPtamKzBvzSQ2vgWw3M8/SoomMVakRSAXnU13E3IyC0Vem7jcfV4TH2BZaCDWt1rKUj+OpVaQatMPUg7iXIFg0S3I9DU12MGHblOf+Ja/sPVb3NjwlOJpeVZPU00Tm+41bYdLSj1HDbhTzZtCBP1zzHKuN6nMjqeD5ZC+2Fm/+nik2gJopeCilTEBgo+MvIyMThJl2ruOYPFcwGxXOTr4x4cPFpWs+r6bLSHJv+Dwd+uJVxTHPz3nrCma9JH5thM0BB7/mZv4TLobw5n9z3wdgbZEAt9YUwRSdpNWV/CRWuW5blbHANQKf8PHTPa/lp3ruJhVPsUfbs6jPazeyqmKfz1dQmpGvfs4vdChWP8fj8abqtAE++clP8ld/9Ve5KJKPf/zj3HZb+XYpx3F46KGH+NznPsfIyAjbt2/nL/7iL3jd616Xe81DDz3EN77xDYaGhggGg+zatYu/+Iu/YPv27TX3Jbvo+Oyzz/K7v/u7vPOd70QpdWmRMfXczFOpFLt370Ypxa5duwiFQjXfg9YF3mgL9rJxhLSlleHnw6f5cb1SMqadbUqQ8V7CPBnjtJ+M8RVProRAelT0Ai8Wtipk9bOZMfnWm2KbEkAsbjE5vUAafeuxfWhpidngvUnzFm4ljiNZtWoVq1atQilFMplkcnKS3YdGSaRmeOaZZ+jt7SU2l8ZRkjMHk2jOQgBwPlqco1YV7QjwhYwyxlxEJkj2RlEcUpsNAj5z5gxnRjO+4cNHjzLQv4qurq6mrS+24+Iza1/Gi1Un56ZjXLmmt6lttgPZJpVGjoNhGLlzF0otTUBB9WKJBbWJfVRKNU3GJJOZ3+7F7nn+t3/7N/7gD/6APXv20NPTw6//+q/z4Q9/OKdK+trXvsaf/dmfcfToUUKhEDfeeCPf+ta3CIfDPPHEE7z3ve/lwIEDmKbJ1VdfzZe+9CU2bty4zN/q3zdaQYYkEgl2796NruvlLd9t3j7AJ596Fno1ShqEfALNTBHYmiTYZTXd0CgEGHn2iExAbvXPsm0DV+olRAwIDE3hSg0pBbbU6AgWBn+GAtXtSZpQFa08+bbr/CBf2YBNqTiTw5VawfevBkdqnE12Y+U17VQjENYGprm+42wB8aSoHYY8bkcYtbvr2qdGkZIpIph4LIzZXKkT0fvQ9ZGy77m+a4ReX/02rqHEAKqOwabA5FyqC4VWV2itqyzeO7CfdWb5MNksYp7JC6kOTCNBeWqxPngiQvEpnpSZBR9bGfhF4XmjadcylBbE5LnC/bZv5LtzHqAIaDa++ff1+2L0mM0XbZy3OzGEwtAkhpCsCRxnTdOfVhkd+k4OJF1iXmUiBsBbRCDo9R038XOr3kzUzFShJmRiUYrdpUC58F4hRNlCh3z189NPP83x48fZvn073d3dDW/3q1/9Kg8++CCf+tSnuP3223n44Ye59957GR4eLsyHnccHP/hBvvjFL/LpT3+aHTt28N3vfpc3v/nNPP3009x4441AZgz07ne/m1tvvRXXdfmjP/oj7rnnHg4ePFhzsS07xr3xxhv55V/+ZV588UVuvvnmlUvGtMOmdOHCBfbt28fAwAA7duyo++RVonvZVABBsXySLLMoIX2pMmP0eTJGLhEZUy6fzHM0tCoS0MWgkk3JshZu+PltSu58VksslmZubmFl6tFnhvFPujSqUcm/J+YH+AohCIfDhMNhzBdPYUqT7du3c/Lseay0izQAKdCdCltc0syY9ihjHMer2yddjEoBvvnNV1u3bqXz1Bh87zTJVJoDBw7geR7d3d05S1MwGKz7+jd8ZoJrt9SWphbblEZnLn4yphj5lialFHNzcy21NGX3sdlBz6XgeR4ZGeH1r38973jHO/j85z/P0NAQ999/P4FAgD/90z/l/Pnz/OIv/iJ/+Zd/yZvf/GZisRhPPvkkSilc1+VNb3oT999/P1/+8pexbZvnnntuRdvnLlY0Y1NaTIDv6Ogo+/btY/369Vx55ZUN/45bkRnzxE8OcC6qIQyF8gAlEIZHaCBOuCeFz2wNgR8ILBynehYghK7KEDEZaJoiv5Ip4Fv4bEdqNfdZEwpXlr/vFLQ75T0uG1DRFlspMqqa2scx6ZqMpLrw8vJCREEReCF6zBi3dZ8oaB6qJ8Mv5ZkMtzgnphgBLUJCLmSJeF6AUW22LEGk4bGr93jdnz3tBJlVwZqNS0rBcHwchFbnaE9xX89+Nvuqq+qPpzs45YFpLL5N1FYO+fSrozSc+SmmXXAeRIhxE8PJU0W7LEh6d/LDuYV93uK/kDvOq825su1W9SDm+ojJEGt8s7Vf3CQEPjRxPT+Jnav9YsCR1UmycljjX8ub+/8DW0PbCh5fbJbdUqCefSynfnYch+PHj/OZz3yG8+fP88pXvpLXvva13HPPPdx66601x3Ef+chHuP/++7nvvvsA+NSnPsV3vvMdPvOZz/C+972v5PVf+MIX+MAHPsDrX/96AN71rnfx/e9/n7/+67/mi1/8IgCPPvpowXv+8R//kf7+fl588UVe9apXVd2f7DwhG27/wAMP8Du/8zsrl4xpFNU8z1JKjhw5wunTp7n66qsZHBxs7MNFZwv2sDkE2iXPqAPFZIzQlqZNSZ+/4ColcOz2n6K6UBQrBqWjQ6A9ZIxTRMaknMx28tUe+TYlJ8+mlJzPkrEsh7GzM+iy8YlMNrAYKrcppdIu8ZTLbBr+6IvPZpQwCox0ZepnKadUjieRUqEVjWIc18OTioCvufPGdgs9+42g3jalbA7yFVdcSWfYTzweZ2pqivHxcY4cOYLf788RM9FotOLNJp6yOTM+WxcZU2xTOj+99NbHasheu1s1MRdC1LQ0FdeU19p2dh+bHfQkEgkMw7ioPc9/+7d/y/r16/nEJz6BEIIdO3Zw7tw5/vAP/5APfehDnD9/Htd1ectb3pJTu1x77bUATE1NMTs7yxve8Aa2bt0KwM6dO5ftu1zGAppVpkgpGRoa4ty5c1x33XWsXt3cxLgVypi//td/gx6BEB6+VWnCPSkCIbuhauGGkUcYlNtMyjIqEjELyLYQKvxGhoyRKkOa1ArtrXbJyiddVMH/L2xXqgSDUgtXqI7b6rQdZCzdQfERMYQsazvq1JNc23keoyyzJSqGIXtKcCi5BkV7x8j+IjImIvqIGRNlaYFboqcr1hoXw1OCI+n+mos/SoFUGor6J+9v7z7ETn/1e/yz8T4sPY7ZosOX8s4TzDsoKbkwf8iGNmvadQylFTFZSMToIsCRie0conCf1wem8l4jGyZjlIJpN8SY2405n/dkNKmKq4aY62fO28iMVx8RA2Cp+gmwoBbidX0/y67uV6KVSRe/WAoaGl3IMgyDe+65h3vuuYdvfetbPPTQQ7zjHe/gscce42/+5m/wPI///b//Nz/7sz9b9v22bfPiiy/y/ve/P/eYpmncfffdPPPMM2XfY1lWSTFEMBjkqaeeqrifs7MZkq9SW2A+svOE48ePMzExgVKK3/7t317ZZIwQIpclUAuVqhnT6TR79uzBcRzuvPPOplYl1XLWWy/jD8woIoKWShmTvdgslU1JIEtUHZ7bnuMuECSnDPI7y6cTdkYZYy+cv1llTDrt4MnMEGZmNok9/5pv/etexJxHMxSIyLMppa0KZIzlcvLCHPd/5BE8V2ZWPObbHSpiiUu/LNcl6CvMB/ibLz/FG151Fds31pdVUAzX8/CazCmql4xJzRNgrpdRq3R0dNDR0cHGjRvxPC9HGhw7doxUKpUjDXp7ewtyUE6cn6r49ytGvjLGcT3GY423YbQT2cFEu1QS1SxNZ85kKjnzfcrlLBae5yGEaHrQE4/HCYfDF7US5NChQ9x5550F3+Guu+4iHo9z9uxZrr/+el7zmtdw7bXXcu+993LPPffwH/7Df8gd23e84x3ce++9vPa1r+Xuu+/mrW99K2vWtEMsfhmNQNd1HKexUMymLN9Vti8rtNjVgxf3HeNkv03HugShzjRCV22ZdNULAdi2hirTZlT8yoxVCfyGmyOOHKnX3Z6kVPlt5BMfquhxKbWFeu6ynwm9BUG9GUrEr9sIglhlVvWlhLF0hFm3vExf1zJkjC68nGLGFDa3RM/MUy6N4USqj4TXXhv/NeEzGMLHVPb7iyBxRVnVtClcbu+pv0HpSLIfKapPwZTKqpHqv2e8sWuYm4IzFZ9PeAbPpbowjXhL445dNQNiAFQm0iGpFsgYS/mI86pSNQzgE10cSlzN0DwRI5BsCE4z6wTo1hfOQa3BcyQlTQ4m1uIKjd55e1NS+ujUWtlkqTNiRZh2wwjR2JgqE7BcfWFGILijaxc/s+rnCOuV566XKhmTj3g8Tk9PD7/5m7/Jb/7mb+J5Hi+//DIbNmyo+J6JiQk8zytZJFi9ejVDQ0Nl33PvvffykY98hFe96lVs3bqVxx9/nG984xtVxR6/8zu/w1133cU111xT83tk/0633XYbf/u3f4vP58OyrJVNxjSCcisrExMT7N27l76+vownq9mmDa178TvYJHy0R51RD4yi02Op2pT0bOicErhL0KYk8ApWjgBkG8iYkBZGHdjOD39ceDOYSmQGwdlaa8iQMdMzSb75z7tzj41PxZDzRMq/fntf0/5erYJNKR8HR6Y5NDEHIlOD7c23P6wkWI5XQMZ88wf7efTpw9x29YbmyRhX1tWmVA71NhalrAUyphi6rtPX10dfX1/mtUWkQbbaube3l+HTF3C8+vY1/2Y9lUgteZtTLZTzE7cTxZamWCzG5ORkrqY8EAgU5M1klZeLbVK61MN7dV3nscce4+mnn+Z73/seH//4x/nABz7As88+y+bNm/nsZz/Lf/kv/4VHH32Ur371q3zwgx/kscce44477ljuXf93DV3XSaXqD2dt1vJdbfvNKGPSns3nTjzJV88/yZrrnPnmIPBkRmnSbogKPiWpMoRKPfMjTVP4dZsuf4bgcKXWUNNKflNS8eN5e1rwnKtEmcF/hnBQSjEYnGMwvFAWYAoPRxkoFP2+1ZxNl06qTyV6qlqgsvkzft0l6eqA5NbuM/g0D6dMqPACmVSayzPhhDlvRytuqxXQcen3xYE9xLwepr0IYbGNmD5blsS4o/c4Ib0+QvOCFWFahqsSdc0QMfd0HOeu0GTF509bEY66Ap/RpsUYMZgjYzLhvRko7RqG06VEVVAb4CezGzlrLShiVvvm0DTYEpqg31w4Bw0hser4WThK43iqnxNWPyYu64ML6hpbmUBryJiwvoGjqQAzXqKhEPAsJB5BLUCqgl1pc3Arb+7/f1kbWFf7s/4dkDHF4ydd17nllltasWsF+NjHPsb999/Pjh07EEKwdetW7rvvPj7zmc+Uff273/1u9u/fX1U5UwzP8xgcHCxw6VwyZEy+TUkpxbFjxzhx4gQ7d+5k3braJ3NViK4W7GFzMEVrfM7NIJvdkkUuwLfNDUfZC1smM6b9p6jCLRm3NULG1AqYA1gj1rPvWz1cGCu9EWSVMdla6zV9HazuCPMf3/l5PE9CR+Z4j09l2P3/7/88w8RssmlqRBTYlEon5R/5ypMcmpjNfSnhKZRRB/Wz1MqY+eOllOIDn/0eL7yYUTiMTtVfKVkMV0q8Jldo61XGJOfJGK8MGVOMctXOk5OTjIyM8MzuY0QjAY4cCdPb20tXV1fFm12+TWkqnsJ2l++6Ug6VBhOelOhtHmTk15RnLU3Z6sV8dVJW1dLswCdba30xK2N27tzJ17/+9YJz/cc//jEdHR25+6wQgrvuuou77rqLD33oQ2zcuJFvfvObPPjgg0AmuO7GG2/k/e9/P3feeSdf+tKXLpMxLUa7qq0XbfmugEbJmGcnjvLZkz/geOoMQkjMPFGOUqJkcaVdqNSk5HpGXUSMX3eI+C1CZh6RVIc9KR+aUHgSiov5qh2DcqSJlAJNKDaEZ+gP5k/WFSHNYtbLjMUCeulq/rlkpjFJAOsC3Yw5pYSAPk/G+IRHErim4zyd8+0D5cgkVfS/WaSlwXCi/RW+obz6ZL/mIjyNp8ckokxjQkCzuKnrTF2f60iN41b19iQ5b01qhIh5Zfg0Px2uXDTyfKKXpJbEV0eFe7NwCWIAltKJywXVkiXnSl4b1rfwvckoM24hCRwxbQSKW0PHC4KK9RpZRVIJztndHEwOItEBxerAXIFN0ZGLn7sINAL6TbwQG0OyOFIrrAdLyJguo5s3rHojN3XeWvfnLPViVjNY7D7G4/GGnS19fX3ous7YWGG1+NjYWMUW5VWrVvHII4+QTqeZnJxkcHCQ973vfWzZsqXktQ888ADf/va3+dGPflQ3z5A9Dv/0T//Ej3/8Y9avX08kElnZZEyjNiXP87Asi71795JKpbjjjjta0lyhlpGMMRaRuL1Y6EXexKVSxuRC55bIpiRxUUXZK42QMTKho0fK3yg0NAZmruEHX/eQqrwaYSphk7ZcLozOcUV/lJMnJxk/XRo0NjObIWO++fh+FpMtnF+QkK+MUUrxe5/8F146fj7vj6DQZH1DgqWeYlquRyJtc//HvsmZ6blcxM+FyebzUDxPIuu85hRDKcVcwqJW4HtWGeM0uBKcH262ZcsWPvvUWUKRTlzX5dChQ7la8mzeTH4NYL5qZzKWxFphZEylcLcLswn2jVzg/7xwAFPX8ek6hq7hM3RMXSv6d+YxU9dzz2ffYxpa7vnMvxee7wr6WdO9cJ8wDKNEnTQ9Pc3o6CiO4/DUU0/VtDSVQyKRWJSVY6kxOzvL7t27Cx575zvfycMPP8xv//Zv88ADDzA8PMyf/Mmf8OCDD+bqGh9//HHuuece+vv7efbZZxkfH2fnzp2cOHGC//W//hc///M/z+DgIMPDwxw5coRf+7VfW54veImjmfFTNbTC8l1t+/XYlD46/C/8YPxlbDL3wkqc01LpiYVQhXYglVmsqlbJrAmPsM8m4rMxi27kjdiTsgiYHkpB2jUK3luwD/MPZw9xuUYlgWJLZIpooHByrCMzldTzp4ctF56P6F1MW2sYT88hRGYzqaSTWeYtOgTZibWpSTYEJ1kXXBjj1GtTkkpwKJGdbLcXQX1h7G0Kjz5jK2eSNkXOaALC4v/pP0xAr09t+uLsBo6n+8h+67X+HgwtozqSCjaHQpyx9tLI6tYtwRF+tuNs2XyklNR5JhHFZ8bbftQsmQSlccwZLKBO0nKafIVTSL+Gf75gki5qFo3oKTw07gwfIagVPmeI0jgByPzmptww+5LrSOfVm3fpSQJFVerNBgBnEdQGuOCsZn+yelNS3Z+n+8kWdRnC4NXRn+Y1vffi1xrLlbtYAnwXQ8Ykk8mGlcU+n4+bb76Zxx9/nDe96U1AZiz8+OOP88ADD1R9byAQYO3atTiOw9e//nXe+ta35p7L5rx885vf5IknnmDz5s0Nf5+hoSG+9a1vEQqFiMfjK5uMaQRZz/PTTz9NNBrlxhtvbN6WVIxlJWPqlw63GsU/7SUjY+b/V/3/7L15nGRnXe//fs5Wa3f1Pt3Ts2+ZJZmZJJNMZpJA0JAA6mVxQeEKeBExgqLxqqhsP/CCgHIDXgRFgRhEUVkFDEsgksBkn33fp2fprXqt9WzP749TVV372j0zCfN5vSDTVWd56tSp83yfz/fz/XxdgXUZypRcabakjLGntbJkTLvSTvzZNXz/2eqyyJmEzW+/48tl20UXbBfzjuPEnZYm1fz5Ous5kkxbvOWjX+X8RGEGQzFldZ+YfFxmZYxt2vzi//kicccuuHYjEy2QMa5sukxJSsn50VmWDVY3saxWptTIuc4MT7FuaS8bNmwoaEsejUY5efIkuq7nvGYsy8pN1tHY1VmmVC6YODM6xXf2Hmf/+dEFO/fLb1jDe195V8X3A4EAgUAAXdexLIsNGzYwMTFRtaSpHLLKmOcLHn300Vwrxyze/OY38+1vf5s/+qM/YsuWLXR1dfHmN7+Zd73rXQC0t7fzox/9iAceeICZmRmWL1/OX//1X/Pyl7+ckZERjhw5woMPPkg0GmVgYIC3ve1tvPWtb70SH+8a8lDJcy+LaDTK3r17Wy/5roB6uyl9e/hZFLW2kellU8YUkTG2q2QUDaXQsQn7TML+dFkSqdHypMJxQIehMWuXL5MpVJmIEmWMgsO6jnFCeun+hmKj502w4+YwvdpKxlJdfOfiFGH/TMHnGVfirA8t5VSiUCmS/WxhNcXa0FjRe5SYq2avq8xbxJ9JdRNbYJ+YLPx5ZIAubPZPh1GUudhCy3jE3NJ5joup+tYIE1aQQ4kB8qPrU0lPyasAd3b3cC69t6FxbvKN8prI2bJEzIjl50Dah0+/PIb9KWeCYbkZUxYqoyQ2ftFFSk7hU27hP0bSyDJKlz5fjHW+Yfq0UnWzKmSJ+jjuGBxKDDLjBApK3VSchtqL14QUaM4G9llxTDk+b4f1ZTyDNoau55V9v0iP0Vx5/fOlTKmVMTZb5n3//ffzxje+kW3btnHrrbfywAMPEI/Hc92V3vCGNzA4OMiHPvQhAJ588kkuXLjA1q1buXDhAu973/twXZc//uM/zh3zbW97G1/84hf5+te/TltbG8PDniItEonUTM5lr8H//t//m9/5nd/BcRxs235hkDFSSi5evIiUklWrVrFs2bL5lYJfQc8YRV45o83iS5gzl1vgbkrZvJYrL0+ZkiVTSFl4nkYMfJ1pHQYLCZdBVvLMv7czOVlHfaoAV6Gm2mVs2rsXVHP+8n6plMXIxCxv+auvMpMqUmHZLnUmey47lvVEuO//fYNEZkLPr+YbaaFMyW2RjBkaqd06MdlAmVIlDE/MkkhbpDKGzvltyZctW4bjOExPTxONRjl9+jTxeBxd1zl16hQXxycLOnddDagUTHxr99FcWddCoaetPrWK4zhompYraVqxYkXFkqYsOdPW1pb7XK14xnzyk5/kox/9KMPDw2zZsoW/+Zu/4dZbby277Ve+8hU++MEPcuLECSzLYu3atfzhH/4hv/7rv57bRkrJe9/7Xj7zmc8wNTXF7bffzqc+9SnWrvVaZn7+85/n85//fMXxPPXUU2Vf37BhQ0nbxywWLVrEV7/61To/8TVcTlQqU5JScurUKU6dOsX69etZsmTJgpTZZZUxtUo9bduHUYaMMW0FI68LXiPtm1uBosicCsdxBU5RGYTAJaBatAdSBeMrRjPlScVIyxRtaphZx0vgFZZPZxWSSua/c9dHxWFd+7hXKoUsUfXowkHLm2C79JUcm+7nqfHzdARSZYkltczSQkGiC4uVwfGypJMtFbQqpSgTVogL6drdSuYLPjE37xgoPD0eQ9ckAoebOs9zW+cpwpoXM6VcvdJhcrCl4NGJdZSmOSGgaNzSGeCSebjmcQQum/2T7E11s9qY4HWdJ9DKfAeHUu2MSxOfNp+GtVXGJXqYcgeYdc6Xfd+ndpCwN/KV0fLEkIJDjzbLOl9t1UnaFZxMDXAu7alXQ0oKULAypsH9vumy5JRSK+NZbtxKF8Ozi7go5r8tdofewW92/QIbwptaOs7zhYxp1TMm24ChEbz2ta9lbGyM97znPQwPD7N161YefvjhnKnvuXPnCq5dKpXiXe96F6dOnSIcDvOKV7yChx56iI48yfunPvUpAO66666Cc33uc5/jTW96U13jCgaDBUrpq5qMqWfSN02T/fv3MzvrLcAGBwfnPVi4kmVKimx+YdkqRJHYV2Tc9xdeGeNN1JerTMlyU+AWlrO5TgNkzMzcz0iRCsaJZXz/vwOeF02dkJoAu3ogNm6l8UHGuHd+ZCgTiRS//sF/xyyWiLsuerrB0qPLpIxZ29PO6NlZ0poDPm+E+Y0hWilTkrI6GTMTS9EeLp+ZS6ZtJqZrK9mSGaPmVpQxJy96pnSVFC6qquYIAfAkkYlEgkQiwfFz5xmLe8/NrHKmuJXfQqHSYqtcPXE8bfKT40NsWNpctqhe9LbVR5CUCyaKS5pSqVTOcPn8+fNIKfn+979PJBJpWhnzpS99ifvvv59Pf/rTbN++nQceeIB7772Xo0eP0tfXV7J9V1cXf/7nf8769esxDINvfvOb/MZv/AZ9fX3ce++9AHzkIx/hE5/4BA8++CArV67k3e9+N/feey+HDh26bPfCNSwsWi1TMk2Tffv2kUgk2L59O+3t7QsxzNz5YY7wrATL1tEMChZaUkLS0knbkqBu4fEwoi4vt1ahKBIkpE2loATCr5kENYugr77W2s2UJ5VDvz/CbDyJ97WXec5m/5shq3RhcV1kHJ/qffdaxqg3H4awUXEBSVjt5L9HLSSX6PDbFT/bmcQoilRw8xa/qnBZHRzHqNDFydP7zt2D+Vcj5Wocvgw+MfnIXhMAJeOfuClykTu7T9ChF87zCac2GbNvdglTZTpN9egB1rZZDJunKuxZ2Nz7JaFLLDfiRB2NN3UdRS/6Dkyp8FyyHVtJLWxb9zyoYjljjkbKLU/EGKKTI5PbOGXGQcahDBnXo8e4KXim6pilFCQcnTPpXobMntzripB0a70Mm9O0qwkCFbKIioC0oxZ8t9XgxJZxAIGplHretIrl/vW8ZtEb8Sn1lTlXw/PBM2Y+yJhyvi314O1vf3vFsqRHH3204O8Xv/jFHDp0qOrx6p1Xq+1fHAdf1WRMLUxNTbFnzx7a29vZsWMHP/zhDz25zzxLaK9kmZIgjkoEp4GF/fydu3DSvFytraXMGjFfnm5KpptEykIyRtr1z2JZMqZT7WL88RUcONh4JsLVahMs2TLv+Ur6WQHBCZGAIr8cXInWKBHD5fGMWd0WYOi4l6HIf5blJzxiSZN40iQUMGgUritxqjxoz1+aYuPa8kHhZMwkXQd5mMgokFohY05dypIx9QUVqqrS1tbG2rVr8e2/hOF4ZmjDw8McO3aMQCBQUGqzUBP78FSMgc5SH69ymZ3/eOIgadtp2sOnXvSE61PG1BPw+P3+nEt+tkvTT37yE772ta9x8OBBAoEA9913H/fccw8veclLCrItlfCxj32Mt7zlLTlZ7ac//Wm+9a1v8dnPfpZ3vvOdJdsXZ2ve8Y538OCDD/L4449z7733IqXkgQce4F3vehevfOUrAfinf/onFi1axNe+9jV+9Vd/ta7rcQ0vHBSTMZOTk+zdu5dIJMKOHTvQ9dqLzVbPDzCdStIdruzzZ0tJ2tIJGHOqBctRcaWCpjokbT1XTu21fF7YZ4eiuNhpTxGjqg5tvjQhw2yorXYr5UnFCGqe10Q5Isp185QxCPyKybrIOHqOHJFowsUqGoqhOAgBunCZTC9nxp5kc/sAB2fPVBxH3EmxTO1gmGwnG8kS/2SJh0c+nJLyLpEb65F4/2XxicmHLubGartp3rrmcfp85RM9Sbd6rDFuhdgzu7Tk9ZWBCJ2+MaJWZdVFREnQb0xxNDUIwJ2hUaYcnd/sOoSv6L4Zt30cNg2EUruUb76gKhsZtmawZClhoeJjJn0bDxxrY8bylN0v6t/CjL6naEvJiyJHq94fKVcjaocZd9qxipauA77VCFRGzYma5UlJV69JxuiinZRcxxFlfrxhinFd8CZes+itaGJ+nqvPF2WMYTQek2eRSCSeV557lZAlYj7xiU8wOztLT0/P1W/gWwlSSs6ePcvx48dZs2YNK1asQAiBEKKp9og1cQXJGICQEmbGnboCZy58MGYn94XuppStJZWXqZuSi4NeNNE3VKY0o7HEXstPvuQnNtucJNQtpzMtQm7enQfWI92mkO5Uy6YOFVNSIXl15SAlywJtXDg3pxTLL00qbjo2OhFj5WBzkuZqrHc1MmYqZpGyCifX5w6f58d7zvALL97IisXeeFLzoIw5lVXGmPWRtK7r5kjq6GwSR8LKlStz3YMmJyeJRqMcO3aMdDpNR0dHTjWT7SI0HzgzOlWWjCmuJ55NpvmXn+wHYIG5GPoaUMY0EvBkuzS9853v5J3vfCd/9Ed/xNDQEIZh8Od//uccO3aMW2+9la985SsVnf1N0+TZZ5/lT//0T3OvKYrC3Xffza5du2qOQUrJD37wA44ePcqHP/xhAE6fPs3w8DB33313brtIJML27dvZtWvXNTLmpxBZzxgpJWfOnOH48eOsW7eO5cuXX5buX0IIXCl5Yvg8P7dmQ+51R7oFjQQc6ZK0tCIyRsk9I4TIGOi6YCjCU64s6Lgl0oXOQIK2Cl4w1TAf5Un5yBZNOUUZm1yHyszrvYEY6yNjBecVkFHAFB4xS0p0a0v58dgkq4OLODJ7ruZYVObmwlXBcbqNRNXti81VsyOzXJVZ5/IvwgzhIpBoOHQZhaWyaVfFp3hBhysh4VReZNpS4YfR6ygO3La09eKIk8Sc6jFjrz7LluAQx1MD9GpJOlUbC5dg0T1zNN3GqGsjlIUt682Hqt7MhfQ53JJksUC42/j8iUWcjknyE40zpoQiDmJn2wmWGFNlzyElDNsRDiSX4H0jSklnJZ+iIYFFvpmCDkzlYGEAlcmqNnUDBxM2s87CEDFb2+7k53reiCLmjzyppSi8GnAluildzXjkkUc4evQoiqKQSqWubjKmXBBgWRYHDhxgenqabdu20dnZmXuv3vaMjQ9EQxJGcHlMsIoR0oLMmFNX4MzFZMxlUsZkHrTuZSpTAvAphbODW+d5DWGQPh/mu583aKWHQzUyRiqe8iOXNBCtlSklu1SssFJew+241KngLMVCxb2uRI/B6FRhyV4+YVRcCnxpfKYqGVNOJiilRELVMqULw+UzWFJKpmImZpEL/t/+2y7OXprkjhtXsiLTBXY+DHyzZEyqATIm+3knYgkMbe7+1jSN3t5eent7kVKSTCaZmJggGo1y5syZgpKnrq6ulrIbZ0cn2XFdaYawOLPzr7sOMJtVELkL62/TiGdMK8FEOp1m06ZNfOQjHwFgaGiIRx55pGod9Pj4OI7j5Oqbs1i0aBFHjhypuN/09DSDg4Ok02lUVeVv//ZveelLXwqQM5srd8zse9fw0wVN03Bdl927dzMzM8Mtt9xSEFstNIQQRKVDbHaq4PXxdJxF/jny1pEujq15bZgzi1HbUQsNeyWkbQMQ6I5DUDfrajNdDgpu2Q4sUnrOKgHNJtzRfCm56aho89hqeNb2VAGyjImwKz3j3hWRKNf3XCopBxHIXPvpLFTc3OL2yMw0PcYKRpNRnDpinRF3BiEEPb5p1hQZ9pZD/hHzCXj3svdphC5VxycsfKL8/Hop3cGKgGdSm3AMnCpdenbPLmXWKSxF2dnZx4R9GFfWvo5dWhxDuNwWPsF1ukQIMPIW8lLCoXQbk1gLXpaXD6HsZCh9hOLAzyeu45vn1rFrzC15DyCaMunOy38s842zLXy67DnSrsqh1CAjdkfB68W+RgKXtDtW0I68EnqM5SD3l7yuiQCS63li9mLNYzSLHZGXc3f3L8/7cZ8vyphWDXznozvylUY2Dv/c5z6HZVnMzs5iWcVar6scMzMz7Nmzh2AwyM6dO0sWBfW0Z2waSgTcK0PGBJQrU8Mvi9rOiTxphm0raFUM6VpBtgW0dAWOrRYEXgsFg8YNfHvVPoYeWYIZi0GotQJ1t8Iv0TFELqGi2C6tyGJcINWrYQcrfDbXRU9d/hbVVeFIjFnKd3Ry8SIRWVqCvOvpvbSrsVybZ5+vkCS5FJ1hcU+h4i2WSHtuPBWkGFJKLlQx6J2K2yi+wuBtcsbLBuYb5iZaJGMe23uaC+OeJLjerkj5k3U0lqQrXL5OWQiRMxZbsmQJruvmjIDPnTvHoUOHaGtryxEzkUikoQn2zNhUzfFNxVN8adeB3HtmC6RVPai3TKlVMiYWixW0QFy6dGndZm+Noq2tjT179hCLxXjkkUe4//77WbVqVUkJ0zW8cNGIoiWR8J5TjuOUja0uB847JnayUD0xkpzJkTFSZktIBUlLJ+TzyFqvlXQpXARCQNw00FSHgN5obCjRFBczU9qTJes1IfHrdssLX9tV5k0Rk8VIegJNKKTK1DK7UrA0NM3qjmiZPcuTMboyd81CWprJuE1CmMW7lkVCmqw2fKxrv1ifd0nJmL2/aykd5hO6sFnrm2JZoPqYL6QiOTIm5vgrGkaPmG3snx3M/S2kZKvhY9w+WPeY2lXvN7HamGCN4QWKwcz3JCXEpe5581ymyySlilS3M1xkNuwTAzw3diNfPieolpi8lIzRm/ETCiopXta5v0InqHb2J5dQrs+MTymM3Rw3hS3Lt/YuhopeMrqwupoTSZ2ovVBEjODurl9mR8fLFuTozxcyptXW1i+EMqUsEokElmWxZs0a4HniGSOlZGhoiKNHj7Jq1SpWrVpVNtCo1Z6xpTGICIILC3LsWgiIyx8YAbgUTrr5dc2WraFp9U3KDZ83QwJlJzjbUjF8C+uZ41PUAhZC1iBjllkb+eFDCul0mpDrKTNkCyIeWUYZI4Fkt4qezDSkbGFN6gKJAQ3XqPy5VLM1IkaAV5g+T5OCMCV6vFzvgbnzFZcnZWGEOgkGg1y4cIHDhw8TDodzxEwkEuH0xckSMmY24cmFK3mUTM0kicXLS4qllEzHLYJ5mTTHcXPHNPNIk1aUMaOTMT7ypR/lxlgvGSOlRFEUZpNpTNvBtOtbmCiKQmdnZy5LbppmTjVz4MABXNels7MzR85Umyyn4imm4+XlwfkS1i/+eB9Jc44INheKYAc6Q340tb771XGcElKvESQSiYa7KfX09KCqKiMjIwWvj4yMVCxtAu97y07yW7du5fDhw3zoQx/irrvuyu03MjLCwMBAwTG3bt3a0Piu4fmNbGyVVVldf/31V4SIARiyU/hSheceSc2pTvK9vGIpH37dQghZxnMl0xI544WmKOBKlemYTsifRtPqNDUWbq4MSQiJX3XQarU8rBPzXZ6UhS0dFvm6mTULkwZSSta0x+kNlidiwIvvisuUjLz5rEux2Sfq7+6p4bAycizPk6Z+5O9RWjo1/1CxWe6bZE1wBK2OQOtiuiP377hjlG1nbrlKpnuSp2Q2hML2rhAj1pmGxhZWTHpVnSWqRwQC+BSXcUtHUaBbhQOWZMb20b7AnZOk9GMqm5kyD0CmtF8XbVyI3cZnjxmYddzOtnQJiW5mGeEXuveWqFlMqXI4uZhLdmVlniNt8pevCXmmbtLOlOmChe+MPcDZVJCYO1Fxn1YgpGB19Da0aDcnu042lcSqhRe6ga+U8gWjjMniT/7kT3jZy16Ws1m5qskYIQS2bXPw4EEmJia46aab6O7urrj9gpUpAYiOhTluHfArV+ZH5srChVO+GZ69gL4xjvRInmwwZZsLT8ZoReSeW8HA16/4cZ9dz8PPWmRTEULiRQ+tkDGqQIpChYcVVnB9CiQz93STqThHhUS/XpbwyUJYLsrCVoPUDylRU6DX4UGnOOUTQlMxM0fcmqaZ80TJEgi7zyZZ0a3T1dVFIOCpRGYTZvb0ZTExlSCRKi+DlVJ6yhhj7j6diqVyx8pXxjRLxkgp+d2Pf4NYco4EbcQzRgjBRMzrAlEvGVMMwzDo7++nv78fKSWxWIyJiQlGR0c5fvw4fr+/wAg4v455eHK2ouFwNrMTnU3wH08VZg1TC0SwA/SG6ydHWg14mummZBgGN998M4888givetWrcuN45JFHKnYHKAfXdUmnvUB95cqV9Pf388gjj+TIl5mZGZ588knuu+++hsZ3Dc9fZGOraDTKzTffzDPPPINb3FXvMuKclaQrVVguPJzMJ2PmxiYRxFK+jHeMIL+yNPvPYrWCZrgkbQPSEAqkq+YMpJS4UmEi5ac3FK976q23g9N8lyflo9MIAnlmqlKyNjJGb7C6sltAiTImpEYYTgl8UmMWhxf3HENBes4dwisWEcL7WwivfTWZf6ccDV042FLkVDei4Hwyc6284+iqJJ0nEc5eR++4zoIY+ArpMmhMcl1oOOcBUwu2VJh1/CQcnaBqecqYMmmsZ2aXE3c8VfvmtkusCa3jQLx8OU45qDj8j/bT3OaX+IvuVUcKpqXCatXBlJCUkJQG7SwcGaPQyQxLmbGOExImSdlGytrB3x6NMF4PC5MHnQgvjjzGgF5IGo5aYfYnl2IVm8oUwZIpBGEkEFTSKFT3I8pH0pmlLXMrpRyNUUviV/thAcgYTei8ZtFvs3LpJqLRKBMTEyVJrO7u7lwM2ixaLQG6HLgS8dPVCtM0+frXv8673/3unLDkqiZjZmdnefrpp/H5fOzcubNmVnIhy5QSKZ3wFbpavstZCJoHR6bI1yXklwpZC+gb42SUMTkyxtJgAScZALWYjMlrbW3PariWYFlvL0e/1Ud0bxI6Va9PnpQI6clPi+tYG4WrCdRMKwNXgVRn69fY9kGiT6eqftN10VpUxeTQanwpJVoctDo96IRD2YGPTswFnoZhsGjRIhYtWsRMLImQNv999DFGRkZynYS6u7u5OJkpj6vAxkSnEiSS5dVgrusynbAIheaIg2yJEhR2PcqSMU6DZMwHH3qU0enCzGSqQWXMWCxTNtUkGZMPIQRtbW20tbWxfPlybNtmamqKiYkJTpw4QSqVIhKJ5MiZS5OzFZU8juOg6zoPPba3hLBJ1dkxqhn01ukXA/PTmrGZzM7999/PG9/4RrZt28att97KAw88QDwez3VXesMb3sDg4CAf+tCHAPjQhz7Etm3bWL16Nel0mm9/+9s89NBDfOpTnwK87+33f//3+Yu/+AvWrl2ba229ePHiHOFzDc9/VCtTisVi7N69G8Mw2LlzJ36/f2GTWTXguC7n0kko6tpUqIwpfF4mLT1vzi1tZV2udERRJCgwm/Tj1y18RuHndaVEAQzFZTjWhqa4DeVACpsQl4fttqqIqX4WTVFy10Lgsr5zhG5/suL2WXiESdZsVaAJH1PpBF0+LysSROKr0u2mGEF1/oxkdeGQbkV6XAwpWaRPsyF0qeFxema9gikrSFCdJmYbJffapXQ7h2OeSdxy/wRrQqO0aQNljlZ2cLwkPMRLwyOEyqyvbSn4USLMBp8XC8y6AhANfTeNQhFLiDoBku4QALow+PyJl3F4utCct14MGifZGpwzgU65GsdS/Vy062u6IJH4FIOUm6LPmG3oN5p0Z2hTNCQ2Y3Y7plS4lDzLmsByJuyzjX6UivCJAK/t/z2WB64DYGBggIGBgVwSKxqNliSxuru76ejoaNiM9/lSptTKGBOJxAuGjEkkEgghcopzx3GubjJGURQGBgZYvXp1XfXPC1Gm5Louhw8fplNzCPfN66HrRmmF4+WB4yaBucxxgTJmQcmYNKDlTPmsy9DeWiuSOGbJGGkLRr42SM9ynR+d8iMmUmiup8hwFXLzUImyNetl4mbULlJ622T8TYSb53XiemSOyEvvpbtUUFujR8ygQqqnfMek/HFq8+kT00qM6UiMOA0pdIQD5UzpL46VtlkEGJ9JsGpxNwlbcNNNNxV0Ejp+8ow3DNdlaGgoV3aTM76djJOsoIyZmkli2bLAUHdyZi4ALixT8v5tNbDw+e89p/j+7hMlr7eijClnYtwKNE2jp6eHnp4egJwR8MTEBGfPnmXXmSmmZhNcunSpxMfHdV0mkyZfe+ZwyXET5sKUQ0L95r0wPwZ0zdQ8v/a1r2VsbIz3vOc9DA8Ps3XrVh5++OGcAe+5c+cKxhWPx/md3/kdzp8/TyAQYP369XzhC1/gta99bW6bP/7jPyYej/Nbv/VbTE1Ncccdd/Dwww/j918Zf7JruHy4ePEiBw8eZPny5axZsyZ37yyo514NnJmZIi1dJs1COeRIau45bpeYnYqC7nW5VtZy7v1KShVNd7GkSjqmEwqkUFWvdGg6GUQRnlcMCBy3sd+7RGSeq+Xf98qTWvOKCagWySrde1JOGhCoOGzsGiZi1JfIEpkLp+LioDI1s5gl7YXP43qVP/MNTXFIz8OtKaWkW4uxIXSJiNZc++ek491zk3aQxUwTd/wFBr6mq2bKk6DPmGZbxwUkEtM9iyBUNUS60T/C/4gM0VVBNWVKwWPJMJaSJpHZZCaTtAypJnFHJ1SBXGr2u1OV9QxbcSw5jk+s4pELG/mZ/q8QM8eBypUKlbAoMM3tXT/IjeWC2cHh1OKy3jDV4Fd8tKtjmd9q/ZBIdKWTcykTQ3FIZzq2zjjzR2aE1HZe138//b5lJe/lJ7FWrFiRS2JFo1GOHz9ekMTq7u4mHA7XjNOeL2RMs8ksx3GaKvO+mrF+/Xo+/vGP88EPfhBVVa9uMiYcDufq3uvBfAcTyWSS3bt3A7Dh+vXgPD5vx24EVqKyaehCwsVE0JZrl3i5yBg7Q8a4mUDocnRUUorIGOl4D7/x7/WTHg4QCPdxXQROjo4iXOh3DaZGEoQCBilglRHm0vmpOXKlhbHYPoEVzvvMTbS0TkUUzIhHxAjbRSqU9XJRTVneHLdJFMTCjexnS/TZyv4wFfcrb9jPTDzN6ESMvq5CJn18yiNjJjKqlfxOQqcngMfPg4RoNMrJkycxDCPnNTM+GatIxlyKZgx188iRqdl8MibTrl1KUmZjypjJ2ST/5ws/LPtevQqX7GQ9Pjun1jFtB5++cFNAIBBgcHCQwcFBXNdl1+h/Y7sJzp8/n/PxyapmHMfhGwcuYBVfEwmm3eIPqgp662xrDa3XPCcSiaZrnt/+9rdXLEt69NFHC/7+i7/4C/7iL/6i6vGEELz//e/n/e9/f1PjuYbnHxzH4fDhw4yMjLBlyxb6+gqzSwvpuVcLe8a9Ll6TZtpTp2QWHyPJ2Rxp/LXvP131GK4UKMiC6cB1BWqFha0QoBou8ZQPVUgcReBKBVeC7Xq/c1mF0CmHWnNf6+VJkjY9XZWMSbsOhmpxfecwIb1+1Uc2BlKFy8V4hJE0rCnwIBF1KX8WAnoNHxcpIWVrBPRK968koiRYHxym26jf96Ycsm2spyyPWI85PqT0SrRcBE9OryDp+ljsm+KWjjO5OzLtTrLcv4wzqVLfnpX6FL/ccZrBiuOHtKvwWDKErXjfSTKTrJzJU+XEXX9ZMsZ1IY1GoEJ3qEpQlRu5YF5EExFOTN7Jv5wWSCT3LnZY2z7CULIxMkbF4Q/Wfx+/YpF2NQ4kBxlzIrV3LAO/kiakNqeYl3SD8FpXm5nSuFFzlH4jhClbuz86tB5eP/CHdOmLam9M5SRWNBrl7NmzKIqSi5O6u7vLenq90MmYeNz7Tl4onjGRSITf//3f5w/+4A+Ynp5m+/btVzcZ0yjmU2Y7NjbGvn376O/vZ8OGDWjmKait9FwQaAtcolMNiqPhqF5m+nKRMRIXRSp5njELf5sqRZO9dAXqsWXMHAh69mumy6nTY6AKhCtZ3h0hNhynuyvAhRmTzpCPkfnI3ACp7sJrm73sMq+ltUtl4sJRwAqKXASZna/sonWnsF2Kla0tqyWaiDOVtERLNE7EQHVT42PnxkrImKwyZKaMEW88j2jZunUrjuPkMhYnT57k4OEzpFIWZ86cKclYjIx7cvp8ZczUTCkZkzLtnI9MJc+YZMoi4J/L+P7ux79RSlJkIKVHAPmM6r+RbJlS9vMDpBeYjMmHoihMJEykULjllltyPj4TExMcOnSI4ek4Pzx6vvzOCxj919tJCeanTOmFIrO9hqsf+c/xRCLB7t27URSFnTt3lvUouFJlSsPxWf76uR8DnknvjJmmw+cptOKOyaydpl3389mnnoVtlY9TrrrUsVRUtfoCVNUljiuwnPLPQkeKEuVstfNXIixaL08Cn2JjVChH6dI7aFd6OT57ii1dw/i1xhbeWWWM68K5RAfXhcMlHjJZM9rLDa2SU38GQ1OdpE2NtYtKW2iHlCTrAiMM+MqrZRtFwvUWw5N2lowxWGrMEhQuR1PdHE/0s8Q/wbbI2ZLq8H5D5UyeIKdHjfPajpOsMVI5ArIcUq7CY8kgjjIXtyQyJMysO7efqHCfJtGxXI1AA6VMQrmNS9Z5JpN38JljARJ5X4EiJMvDEzBSef9yeOt1P6LfmOGSFeFQcpDmG/pK/OqlppQ+AoVjySShjBrOzCt/86ttmHbzZEyfsYTX9d9Pm9bR9DGKk1gzMzNEo9GSJFZ3d3fOCLjV2GShIaVsyTMmS8a8UOInIQS/8iu/QjQa5aGHHuIb3/jGC4uMmY/MjpSSEydOcObMGTZt2sTixV7dpxTNsbfzgbaggCvTVRuf6iOR6apU3E1pIaHKuTKly6GMsaxCyapBgLM/DCDwFuhtQR8yWzYkybHQRmYRLFpgpSUSKyiww579XUHHI6dUaiKy/1dm3nUB6VPQLIGVIdBVy/O1KSBjXBctvQDr3EbiNClRk6DXwTVWCnCrjf/42XHu2Lqy4LWsWiWZtkiZNv48EiORMnPnAu950t3dnTMN//aPo0imGRuf5OzZs0zFbLq7O1m7cpALY556LW3aOUJrMk8Zk/VKyTcArkTGXLw0xeDiDvw+nf/7b49zIVo9iExZtcmYbJlSNFY0pkDz3YEaxfBkjHTm+Zzv4yOl5P5/+EqBCeflQl/75VHGwDUy5hquDIaHhzlw4ACDg4Ncd911FTOoV6JMyXRsfu+/v000NfdciiYTOTLGcV3GUjH+8z8fY6xbq5qYmvPtyHRTMhVsRcXwV/CpshUsS8U2FVwFDH/557F33DrImNy5S7edj/IkgKBmlXT76dAidGqLODB9AZ96ghu6htGb6PqUvXozph/bVRgzL7ChaBt5xciYyp8nZWrMOgaJmB/yxAh+kWaNf5Rlgcl5HUu+MibtathSY7kRR0Xh0ehaVgTGubF9qCxR4MoLgE5QmPxSx0m2+mczfoWVI5mkq/JYMoCrFJbrJqXAkRDPU8b4Kihf0q6OU6cBspQKUt3JxWQ7nzu2lgvJwu9bxQEBff7ZCkcoj58ZOMzW9iH2JJYxbHc0tG8xDOHUJOgqIaLdQNQ6TkgFU2rkX3tDNF8Gs9S3htf2v4OAOn+lNIqi0NHRQUdHB6tXr851s5yYmODgwYM4jkNHRweWZWGa5ryXns8XssbwzcZPiUQCn8+Hrlc3dn4+Qdd1fu/3fo/77ruPs2fPvvDIGLMFfwHTNNm7dy/JZJLbbrutUBKlXDkyRqU12Vwr8OsBEpb30L1c3ZSy53XdrGfMwt+mUhbeN3ZKIGMmCtDRFuDs0VEvolIEwgU783DRde86NGry5/jACilYYYEVVnIGu8ELFvlTjJ6oEIRUiImkJkARKKbM2f0oTmbbvLbTFX1iWtQhKxLqmiJdr211jaRl3rgaL3g+fm685LXJmEe6WbbDbDyF35hbHGe9XCoZ+E7Ppulf1M53n43y1MEhEmmLdcs6eONLbQ4dPZMb5tj4BL09XQWeMVZGGZM17wWwbZcvP36Qb+0+xmtvv557blzjESbROG1hP/ujw3xjV6mHSjHSpp1v7VQWWRnrRFGZ0uXE8FQMtYyR9LnxaZ4+P78Bc71oRBnTSmbHcRySyeQLqub5Gq5uZP3uLly4wPXXX1+1FTpcGTLma4ePcClemGkaTyVYjWfkaUuXXQf28Pkn98Gy6sSxLDJRtWM6IphRezjCI14sFctRsR2F/IlO020o482nCLfkuLVQjrCwHLWkXKoZD4+gauaIiXa1jR59gAPTFzkrh4joCTZ2jjRN+Ii8MiVDdQmqpbG0vEJ1Su1aO8MVQvvTU90oGqRMg1jSR2cwxirfGCsD41X7FjSLLBljSo0R01sjLPfFGEr2sCQwxea2CxW/16R7iV/tkNwUGMWXy6pVRsoVZYkYgJT0/GLyG0f4FAu7jJLLRCvbfrsE0kfMvYcvnuxk90T5GvD+wDQgaKun3WUGS0NR7lp0lMdj6zBrdEqqBYGLX2nOHDqiLeHZmVHCmjePm27h2kIRzSWn1gQ280uL7kNXFja5VdzNMh6PE41GiUajHDx4EJ/Pl1PNFHezvJLIzivNllLFYrEC/8bnO0ZGRvjBD37Ali1b2LhxI2vWrLm6yZhGL3wrwcTU1BR79uwhEomwc+fO0pv4Cra2FnKGK9X4ShNz9YmiQBmzsGSMLrS8bkoLr4zxBQqvr2k6hDKx2WBnG8eGhxE+gcy0c0wmvclA0+p7uLiqzJAvCmabgtTL39vGrIuVx/vpcRdXz5yjsC9kCRxAGt4bORNc1/WMggEt6alj1ISDUinAvBxJL0dizNKYV00TQWA5MmYm7gUQtusyE0/T25lPxpRO8PGkyTd+cIBHnzlFwrIYnpnl/O5MRkjAsaEppt0OXDVAtp3os7v3EvJrnL80J5nOKmPyz2E5Dl/64UHGrCTv/9p/86GvPcaWpYtY39aBP6Tz7s9+r67PWalDUT6yZUrR2JUhY6biKZKmhaaW/l4+++hzlW+7Bb4fG+2m1Gww8UKreb6Gqx8zMzNMTk6yY8eOukjAK+EZ883DxwmnfcyoaVKOd+5o0ntGzc7OEk8mOTQ5wqVF/rl63QrIkiZSgptUcKTATemYUQXZRBGs4whUIct2ZSp7/goTlO2Kgk6UAJaj4LgK/ioeIeXgKWMkqwPrODB9iaG4192mxzfLuo6xlsgH21ZB9c4BlCdjFoCJMV2FKSuAIxVcKXCkiovAkZnvUCooop24lc6pj7JXM21pdIbjCKB9MIkULi/pOFJXWVmzyJYpAZxPed1QlhsxLjkGW9ovVNhLssGY4SZfnGCdX5IlJY+lyhMx3hEFo0UG04qAhG3QrhUqyGyp1iQVXdnOdy7+HF+/oFKOmMxiUcCLc3x1ZtKCWoq7lh7nUHppXdvXPJ5iNWdEjM75VAiHqZwnVLqIjJFNdOy6Pnwbr+x9M4q4vGVCQgjC4TDhcJhTp06xbds2TNPMldUnk0na29tz5ExbW9sVIzPmg4x5ISSysknRXbt28aEPfYjPfvazANh2o/bVVwBCiIqZ6mI0U/MspeTcuXMcO3aMNWvWsGLFirI37JUsUxKkMUQYUzbn/t4KtLwHTH5AsZCeMQC6klem1EA3JdcBpcGhqeikLxaWs7iZe25pX5Bjhz1zQeE1WABgNqOwUDOLy+JbNL/0yAwrOMEKDyFXopoSx68gLImWlHMKHEeipiRuuURCnn8MgCvADSi5e1cA2C6KM8dhaGmJkraQvsoXqOVHdY2fqrAkeqwJo94mYqupWKrExHc24QUpjiuJJQoDllROGQMf+/yjPH3wPOMziZoX5VP/vquAZNh60zaCuuTz35/zQLl4aYQTJ04wOjsX5Dx14gJTiRTZRJGFyzNDl3iGS3xx/2FEZglR6zspbgVdDtlJIFrkGXO5MDzpEVi24+K4LmpmUj4xHOX7B05V3nEBYwdNUYgE6+seJKWcFwO6a2VK13C50NnZyY4dO+oOwC+3Z8xYPM5zFy8hgU1Letkb9ww1o6lkruOToio8MToOqk61BSLMlSlJwEroeN2U8loeUr8axXUhOekn4agEVZtApPrCU+ZVExcYCFcoT4qlfRhqY9e6TfXhV21cCc9NniPbLWpxcIrVkYmGjlUMVWjMpAVdvniu1XO5ls/uAjyQzyc7OZ6ortryUk2lyxVNk2gZLa5Pc2hT0wtKxMCcMgbmyJh1gSmCjk401V6y/VItzm3+GTpUqHdCm3E09lng1CjFGSnTAchTnpSSMZXgSMGU3cHnj9/C8Vjt+a3Xl1GyCUG7lmTGLvWfyuL23uNs7rvApDs/SQhd2A13T8oiqF7P4cRFAKyMSqhYGWOVdGyrjlvb7+ae7l+7oooNKSVSSgzDoK2tLVdWn9/NcmhoKNdKOduQIr+b5UIjGzs1e52yJd7Pd2VMlss4c+YMPT09bNu2LXdtrnoyphE0qoyxbZuDBw8yMTHBzTffTFdXlR73V1AZAxBSQ5j2lSVj8n8HC03GqELPlSnZVv23aXI8QGhR/U7L7WKAvf+1kUMHLcifRwW0hQymhueOJVyZMdGFyakEQhG5udVxXWwDrHBp6VE1CAcCIw6x5QpGzAt1hANSAS1ROfTJT3I4GlghFc0qDEK0dJHBbdqBsFo9HJAtmvhWioOkRE2D3qwJdpPxVbGJbzxlZiYviCUzHjFS8qNnTvH0wXOZU0n+64lj3g51XIbZIjNg03QY6Okknp67+IYvgGmaHD3uncMFDlwYq8gyuSoQEDhSImxQLFDs8guJVB3trV3XxZWSmTwCyqxDUTNfGJ6aK0VIWw5BnxcM/eMPn7tsYyhGT1uwqmliPlqteY7H4xiG8YKqeb6Gqx+NPMcvd5nSd0+cyj3WD54f44bF3exPRjly/iyLL457JurPHiYatCGt13wW55QxaXVOCVP8eHW9ubXaZZESUtN+ZKYUO52u9zc7d9As6VOpPCme9qH665sMg0qAxb6lnE+eBLywQhUS24Xl4UmWtU3VOb7KaFc2Yynes9ifUcSUU8ZUaxfeLIoXxPloUxMkHF/dfieVzI3nE/lkTMzxszNykm7NZMItHGOPmmKnf4pFWmPBy5Sj82RKR9bxWRJl1C5O0WtSlifRXAlx6WfaCvB3R+5kLF1KJJVDl2/ONuG6yCWejq4q2SaopvjtdT+ixx9nX2p+FDECSaDJ8qRObRVPzVzM/Z0lp9Ky8N5Lu/XbXNzV+Wru7PyFpsYzn8jGJsWqk2Ij4NnZWaLRKBcuXODw4cOEQqEcMROJRBbUALiVEm/wPGOCwfpVzFc7XNfFNE1M08x1x3rBkTH1ymxjsRh79uzBMAx27txZmyUUbUgURI3szEIhpASZpLQl3kJDFYU/cCFcpFQWvExJFUpTZUrx0WDdZExH/Ga+8mAbyZQNrovML00TMNgR5uToXKmLcEFkMlyO4xLuMDgdmyG+SOFJOYG7rnK7yUpQHIl/0iExoKFnVBOKI3F0gR6vcq8pAscncJHYbQp6ojRCUvPIGQlIg9oEUYtBVnkfGq9bkta8nRNCyqYk0sUmvomUxVTMM8z57k+O8fmvPMXQ6DSOlF7773mIMlOmhZSS6TwDX0XV2LhxI8NJAx4bwtU80qcmySQEUgdH9zqN5EiZPGImXYGMefrweW5ctxhNVZBSMpO0coovuLxlSlllDHhlVUGfzpGL4/zoyNmq+y1kjrOREqX5IGNCodDzPrNzDc8fNFPm3YrnXqP47vGTBX8fujjFqp4go/EYO+65m2AwyEwqBUadxqN4C8+CeKHkASIyTEnJqzmkYgZOnk9d4/54nmq1XHkSeO2tLUfFcavrQ/2KjyW+5RyZGeVSYojl4blnqCocVrRPMRBqzES1HKbTYRL4CWdKpgzFRRUOAa38wtdFoM7jkzlVgYyREq4PX2TAP0PcNpix/UzbAabtADO2n1nbD0XxqW+ByRjbVbByKhOXF3WeYFPQ8zsLZFQsYWGxMzDBUs2pm+zPYsLWeSqtU9LmsgEU31Vet6BMLO0KNEUymg5z0YwQ0dP8zaG7SNj1KUQBOvIyaitD0RIy5taeU7xhxRMA7IqvoeWgMoOAYjYVnhkiyPFEkWINge0q2EU+OnEnUUeILHh5z//k5vaXND6YBUA9JUCKohCJRIhEIqxatQrLsnKqmcOHD2NZFh0dHTlyZr79WVptfhCLxV4QquLsd7Rz506++MUv8s53vpN3vvOd+Hy+q5+MWYgypUuXLnHgwAGWLVvG2rVr66tjEwqINpDTdY1lvhFQ6n9YzieKJxMhPFXBgitjUHKZLquBMqX4WGXJZBY+EWJi96186weeQZl0JVak8ByqonDycKHniJQeSZJu95QvE0EJpCHU/LUQtieOCIzZGBkyRjgyV6JUcT/ARSJV4bWuLvPgVPJ+Cq7i4vq0hffeKx6y46LPgtqgCWLBIaVsqkwJ4PzI3O/11NA4F0an+c33/BsAP953puC48+VNmErbzMbTOHntgdJ5ra1dQOYnWzNlaTUhBK4BruHtkyVmUmb5oHnP8YuYls3tm1fgui4TiUJl3eUsU7o0OaeMyZJAn/nBM5ft/OXQaFtruFbzfA0vXFxOZcxUMsWzFy4WvCYQXIia3LJ8JcFgkHMXxrEVCVllSc1HpPDUtAVzTRklhyMqGpalkxp2ojCp4pQpBSlG8dEklbsnxdJe4s+RilfeJLNEkpfq8wmDdeHVRNNJRlNJen0RFiFYFPT8SKRUGAiY80LEWI7CqZk+XOUcO7rmkj8B1aqgjMkokOaxFCjllFceSWDaDjDADCHNJKSZDDDXWdCVMJshaLJETVhdWPW45xcjELjc03OIkGbRnSnn8gmXO/3jrDFMtBodksph1PbxnKm0RMQA6EX7p/Pq3NPoRNM+LlidjCQ6+fuzm3AbLBoP5xn3DgSncv/2KSZvXfcjbmwfwkWwK762bkVTLejCRm+yPEmwjgn7Usmr8cx3mY9ZZ5aIWvn+VtF4Vd9b2Bi+pamxLAQqKWOqQdf1gm6WiUQiZwR88uRJdF0vMAJuVdHbit8ezCWznu/IEly33XYbb3jDG/jEJz7B008/zeDg4NVPxjSCWsGE67ocPXqUCxcusGXLFvr6+ho7gei4gmTMlZG3F/98FOGVWCx0N6VCZUz9t2lqsjpp1emu4ZEvLePSpbn7RCourl54DhGzcRWJVARWAKx2pe7So0agON5DPzDm5CI64YAWr16dLZEIB5QsUVmOxc685eK1y6575K2wEkVzmG9GIhxBy3NyE7FfW9Dg0JPn+MPJb3BsaIyUZWP7BCkTMCp/QIGgOR2Oh5RpF3RSAjBz3ZRsXI3W7yNF4PrA9cE7/+373PDjPl7/oi3cvnF5bpPTlyYZGp3m9s0rPKVOkUfOZVXGTBUqY/adG+aJ4+er7JHBArKH5swU+/bty2WDAoHKRG42mPhpr3m+hucXFtpzr1kcGB0t+0h3JDxy6AyvXnOJT//zd2E9iDrLPKQtcBWl9lThFvqtASDAMhXMmVJ1az2Jp+LZIm2r6GXGLSUYmkNX2Cv1mEkHCt5LWRopS+fUxEjBfl3+OIvb05iOynOjg6zqmB+V9MHJRbjCI13UvJrmDj2JWmFBOpJuYzDQfBxcTI4lnfKKYikF01X8SBQBET1FJI8cUOvr5dg0Eo6BisPLeg/iy3j+dCoWpy2dYSfFDv+cCqURXLIC7LZAabJdcz4CioUjQc0qZ/OWeePpEDMyyFQ6yOGpO3Bp/HsM5Emcl4Sn+D9bv8qpWBdbIhdp17zv4mBykJg7PwlkgWy6e1K3vp5d08VEjIe4U1oN4UiHgNpG0p0pec8QPn550dtZFdzU1FgWCq7rIoRoOrYQQhAKhQiFQixbtgzHcZiammJiYoLTp09z8OBB2tracuRMe3t7w+dqVRmTSCReEMqYfLztbW9jzZo1/Nd//RfT09MvPDKmUplSKpViz549OI7Dzp07m6o/kyJyJbr6AeATzbOKrUAUBS3ZjkqWvbC3jiJEnmdM/T/i9Ez5cjMhVYzh2/jnf1WRMm/Cs13MnqLjuxI95jC7TMMOCPRZF7ttYcin7NybH/cIN2PkWwEyk0oTgKtVfghnQ07H34BkvUV5SEH85rhekkd6Cp6m0QQRIyyX2ViaWR3GTw9nXsx4/CzwjziVtpicLSJjMqVEs/GUVy42j3AF7L04yt5//R5+RWXbisX8r7tv4uzwJONTcaZiSRKmzb8/tq9wTJexc8rwZKFnzGd+8Gxd+y1kmdKGlctob29nZGSEY8eO4ff7c8RMR0dHQUe9VoOJeDz+gqp5voYXHi6nMubQyFjF9ybTKX7ju19Fmqb3rFYzJEENNYYd03EidnmZisj7t1uqlnFsgRn3U85a3q5DGVN4PoktNXRKn6+Oq5S0GJbS6wqUtPSKHW+WtE2RtDWeHR0k6fgw5yEZNpVchIWaywtoeWRMtxEvu8+kGeBCupN+/0xFsqYWJqxQ7vhSQsrVvam56KNLYNpq7Jm50PF52lV5Rd+BjEpDsl6fYcpNEMdGE2Q6PzU2ivNmkL0mqHV2J6oFRUhSjkEoo2yyMr4oroQnJ9awvfcsz8a78DfZhrmwi5LA0BwWhWI5ImbI7OSiVcV/s0H4Faup3FVA6WDfbKLi+4kKJKBPaS8hYzTHx/9c+kcM+kv9ca40WvVjKYaqqnR3d+eMgFOpVK6k6fx5L4GWbwTs99cm3eajTOmFoIzJmi0rioKqqrziFa/grrvu4vTp0y88MqZcMBGNRtm7dy+9vb1s3Lix6Zvi3Ox2lgWHUJhsdagNw1hgh/hKKPbIyZIxC12mJJgz5LPN+m9Tc7ZUQRSU3Rz7wVb27i2c7KSUOMG5KEBNufjHHYyYxNUFiQEvaFKTEnuBOtIKp/R71WyJmi7/feeIGJlJ8NWY+B2jMe+AMjnDpuGb8Q7meeA0f5xGb31heZ2DnFqtUIvVqPMUySVNG3emMAjIqlB+ePBM+ahznpByHR4/NcTjfzfEuoFu1vYGefjpY3x57yUuxgpl55e1m1Kege/ec8M8d7p8tupyYrCnkxUrVrBixQps22ZycpKJiQmOHz9OKpUiEonkskG2bbdMxrzQMjvX8MLC5Wxtvfdild+/C0ls/GYCUBEqNScBaSo4toLrlOk/J4tnNVFA0AgpUaTAqVCu4dRBfOS7syVtvYRwycIsMHmVpCydpKlluj6Vh8Cl3Ujz1MhSzEzJiVXF9LYeBJRO9idDKGJuTshXxnQZpYvYpKNxLtWFFApxx5dbfDeKkXQ7HXoCVUhSro6LgloUZ2bFXDO2v+4q3syeTY2priNL6NDTSAF+YfLSYJRFquS5TOkxgAU0QnGcNYMcsCRqg521aiHl6jkyxpYqjiv451O3sDQ0BcC5eCd9enMJ3uJyIYFkb3wZ6/yjzDh+DqcGWxp7PjThYCjNXBtB0l1K3B2tuEWl8ixdFBKAYaWDjaM/w+Daq4+IgblOmQsFv9/P4sWLWbx4sec9ODPDxMQEly5d4ujRowQCgYIkVrk4aT6SWc93MibbGEUIwblz53jkkUd48sknGRsb46tf/erVT8Y0spDUNA3XdXMfWkrJqVOnOHXqFBs2bGDJkiUtjeVvd23gBwd/ld/YkeBVm06wOPgMoqiF3ELBEFfGOJgi2WfWkG7ByRghmjLw9VpazqE9dgPf+Odu4vEyQabrYvtVfBM2vikXPZW91wR5dh8Ehy3QBenO+f/M5eaZoFQxy2TVwAs1smXoskbpkZRe96dGOYaWeL/svo6L4njibcUGu8UOTXWTRLZHpEnLrRjBuQucPkubNsmUVfLabDLNmYmphT15FgKODXtS9t3D5QOSelpizwemEykSae96SOBrzxypf+cF/K762uYmd03T6O3tpbe3F/Bksdls0NmzZ3PzyfDwMF1dXTkH/HrxQggmruH5h6uxTOnSpUvsvXix4vuGopI2LVJrwvhFAhCgyqp5Byume55ajkLJxy338V3PN0ZKSSBg4VSJMaQUOI4o6Yo09/7cvx1XYDo6Spl4TcpsebdkebCTd6z5ed70k69U/lAZBHSLvdGBgvbErShjpISx5GpMOVzwer4ypk0rjGsdKTiZ6EVmFNqzdrBpMiZuG0xbAbqMREV1ggeBiyDm+Os+10JNF1JCzPXl7Ih61BhDjsWoW9hw3ZISX51xzul0iMO2g9qkF0o12HlEQ8rR+MKpW5kww7QbKZK2xmiqjT69uUBPFMmnx8w2UtIg7SrsSS5v2IOmMprvntSlX88T05WJmGoQYm4N0a0P8AvB32SoGnl8hdGqH0sjEELkjIBXrlyJZVm5JNbRo0cxTZNIJJIjZ7JNC1pV78TjcQYGBubxk1x+CCH47ne/y1e/+lXGx8eJxWL4/X5e//rX88wzz1z9ZEwjyH7ZjuMgpWTfvn3EYjG2b99Oe3t9bduqoT3gJ+3Apx8P8unHN9MXvoG33TnNz64+RLu+r6SkZz6hc/m6HBSikBTIlSktsGeMQObKlBox8LXT3raq6yN+aAff+q6kbDRmuugpl+CYiyJLa1cKyoZsaDtjoZiS5KL5/cmUU8Z0Bf0Mx2Mlr0sgO2+7Wu3Ao0qyrcpOxW6HjSF73XwzeMfJmO+KVt1x6/hpSVd6zRVEdYPB+TLqrYRU2iKeLPy9WrbDR/79R2U/hpALW45TCZfLM+bs2NTcHwJOj9WvLFzI69JTpZtSMBgkGAyyZMkSXNfl7NmzXLhwgaGhIQ4dOkQ4HM6pZiKRSM1g6Joy5hqudix0mVLWs+/Y2XNMWpUVODcpbTw1NYPb55ubizS34kPbTao5JYrrKPU9NByBVF38PhvVcGsmfGxHrVFG4s11MdOT7Ltlyo1cV8OwNaYOhPj7t78dv6bhQyFdo0On6WroRVkb020+/ppNrON4yiNiVOHiZK6dVoUUOBHvxUZDSljqX8evDt7EY9EPNnX+lKszaQUzZExl894spq3AZSNjssSazBxN4n2XKenDyRxdwaVT9cqsigXMVp0T1kXL4LDtoCwAEQOwzL+ClHOQc/F2/un0dqYsLxEwYwYYinfiOePVe7VcQrpJ3PLjU6yC+NBTxSwFBLsTy0m4zZU+lUOHZiBpnPALq308NzPR9Hmzv93FvpX8Wv/vk5q2UJSRGntdOSy0MqYadF2nr6+Pvr6+nBFwNol16tQpNE2jq6urZWXxC8Uz5v7772dsbIz3v//9vP71r899pne/+90vTDJmcnIyFzDv3LmzZSfoLNr9hQ+a0Zjgvf/VwXvZyQ2Lb+O3d45yfeeP6Qo3x8hWg3aZFDjFkLKQmb5cZUrg5sqUXEfFrdAmsmQvSyGQWsyT31jP0PnCAEZmWlraAYE/KfHFq0xGMrcTSsZqJHzRRjUlsSVaS4RFPkSZ+C7oK58typ7RBaRahypGK1OIXQ9aXQFnVDEeGeO9JBzPDLkZiDoyu1JKFBdc3TuHVBpXBM0XUqZd4hmTTJv86NDZKzSi8rhcZMx//PggkOkY0mjMsIBfYr3dlBRFwe/3EwwGuemmmzBNMxdwHDx4EMdxCtpCBgKBEhXYNWXMNVztWMgypVQqxd69e7Ftm7ZVq+DoybLbCSD90D7Ue9qx+ubiLS2exGkrNXOVEqxEvrdTmQdMSZkS4AoM3UH31/cMrNZRKXvkpK0z5zkz50sjpGBVcCWvWnw7X/yvkzw9dSG3bxs66ZqxXen816wyZibZy/GUV4KkCJd2I8VkOgjIih4wZxOdJKUPlSBvXv5GtnXeQMqZxRq9AdBQhI4mDPp83ZxLTmZaBkM8ZRFPWfhCYUyXzP8ks3aUo9Nhjk1eh62mUZTSz5/vnTNtB1haZA0waxukHH1uzJkMS8Ix0IWNLRUcqWJLJfM/79+WVLFdFUuqWJk21abUsFwVy1WxKTTg7dYNXt57AUfOmc1HlETFa2XWETxdsnX2WXLBiBiAmHMIy+3mSGIgR8QATFt+zsUb83Pp8CXpD89yJNpPf2Cq4L0pK4CFt74atSMY8/CZVHRWB9YxYtXnK5cPgUrU6saUzZMxluuyMrCBX1n0uxiKn4Q7flUb719JMiYf+UbAS5cuxXXdnBFwNBrFsiwSiUQuTmpvb6973C+U+OnNb34zBw8e5Omnn8Y0TW677TZuueUW0un01U/GNOR3kdl29+7drFmzhpUrV87rj6g9UJn13X9R4W3/0Q/8Ii+9Ls0vbzzKjYP78eulrtzNQKGyEdVCQhYpci4bGSNlrkwJwDZVDH/1QFG6XmnTVz+1Gidj0iszx3J1sEICFIGWcPFNVZ80s3Ntsdo4MO6gmJKZlfq8dFZSyihjsmavlSCN2kSDrMNPpiJaVLDkVDHkXUenqJ1zg8esBcWWuEbeg73aZxB1bNMCUmmbqSIyZlo4lT/GFZDFqEKQmpwhmUxW7SJUC47j8m/f2M2vvfrmsu+fHpnk0YOnvT8E5fwxq2OBvqOgoROqQHqWQ37Ns2EY9Pf309/fj5SSeDxONBplbGyM48eP4/P5cqqZcDhMIBBoOpj45Cc/yUc/+lGGh4fZsmULf/M3f8Ott95adtvPfOYz/NM//RMHDhwA4Oabb+aDH/xgwfZvetObePDBBwv2u/fee3n44YcbHts1XP1oJP5ZKGXMxMQEe/fupbu7m02bNvHZ5/ZU3HZVCqb3XkDdUfhMcroVxExpyYIb15B5DxXXKZO0Kft8Feg+p8Y2c6gV7zgumHkqD4Fkib8XnxJiNGXy3OQl/njdICHfUMF+7UJnXFYnY8p9hVYTypi4FeBkwk/2wwY1C39G7aNVKIMfT4eI2mFuat/JW1f8GqrindevtvHbqz9asO27D/w7j44VJyJViOfPhZLeoMRVXKJphzbh4C/Hn+X9e9oqnZ9OJvo4Hl/EqsAYt3fOEXv/NnwzccfXQi/EOfQbBnf3nCPhFpaodGvlzY2htjLmgqWz32LeknnlIIhgy2383yMdXEoX3ieWq3Fipreh43UGknQFvPVHvz9/PSPZm1ie+8tTp7VGxoTsLlRNELNP00xgFNFu4OhMayVFnXo3v9b/66jCWx5nDVevVrTqx7JQUBSFrq6unDJGCEF7ezsTExPs378f13Vz79fqZhmLxV4Qypg/+IM/4IknnuDrX/86Dz/8MA8//DAveclLOHv27LwV911xOI6TC0I3bNjAqlWr5p3NrEbG5ON7R3381lc3s/NvX8cDT7yJofidSJpf8AAocrb2RgsAVxaSMcpl6qYETiEZU0NGrJlh9n1+C0hwTa9kRQqJFYJ0l4LVlmlLLSWhS07NqTqX+HBBFt1HvhmXjuMmol5NarXzlIl9k4m5a17sb1KPaW9B96IrsNAvJJi8fyt2CwOpsauw3EIiBha+rUIVpEyLqbzW1i5g19medaGhCMH13V2sHJJM7hvliSee4IknnuDYsWNEo9GGFmNSSt725//ON390uOI2n//BbjKe08irKF7orVKiVA6Vap6FEITDYZYvX86NN97Ii170Iq677joUReHkyZPcfPPN3H777Rw/fpyZmZmGru+XvvQl7r//ft773vfy3HPPsWXLFu69915GR8srLx999FF+7dd+jR/+8Ifs2rWLpUuXcs8993DhwoWC7V72spdx6dKl3P/+5V/+paFrcQ0vTOR77s0HpJScOXOGZ599ltWrV3PDDTegqiqHx8Yr7uP7hrewVmeKzPaDClIres0FK10Yh3ieMfUZpMsGzMPyOyrZbuFc40pBzCyMDdeM9nFD2w3snhzhQnKSTj3EQKCTtiJ1dbtSO0Mhyqgw0nUqY7JfpemonI714eZdjJBmoisuinALzHuziNsGMWc97133bn5n1f/METGVENFrx7jZ+FEREkXI3Hmr3XHl2ltPWwEkgovpSMHrZovGxlks9fv42Z7TJUSMT1iEyyh5srCq/HaGFpiIURnAEbcTtTfyF4d6SoiYLBKZls71DqPdlyJkeDFpj2+ufD5hGwVlSW7TQZcEKeh2lpDQpgi7GnHZeOv2iLaUZ2aGa29YBcv8y3ht/+tzRAzMtY6+WnG1KGOqwXEcfD4fAwMDbNq0iTvuuIMbb7yRtrY2RkZGCuLQ8fHxEoVmPB6nra3xLiqf/OQnWbFiBX6/n+3bt/PUU09V3NayLN7//vezevVq/H4/W7ZsKZukauSY5XDbbbfxoQ99iL/7u7/jnnvu4T//8z8ZGBi4+pUx9SAej7Nnzx5UVcXn8y2YnKleMiYLyxX8464A/7jrerpDm/jtO2a4Z80ROo29CBrLQAk5C9RuITbfcGRhzeblUsZInIK6a8vUoIKcVxtfwQ+/toLUGQtFCBwdzI7yJTr+CRetnoqvbDdNl7LusXpC0nnMZHq1jlMutVMnyiljYvH03BCKWkLXMu2FjCqmWdVOBYudeiGy5Un5x8MzHfY8XRofl8g7Tgkyhr2lOwlk0STqZl5faIYqlbaZzCNjnAALmgmrF9f39SCPzDL2lCe5Dm9cxp133snk5CTRaDRnwJZfchMMBssGIlJK3vGeL3N6bJqgv/yC4vTIJI8eyFPFNHoJJI0raepEb1tjc0S92afitpDf+MY3+OY3v8m///u/8/jjj/Otb32Ll770pdx7773cc889DA5W7jzxsY99jLe85S38xm/8BgCf/vSn+da3vsVnP/tZ3vnOd5Zs/8///M8Ff//DP/wDX/7yl3nkkUd4wxvekHvd5/PR399f1+e+hp8e5Hvu5bd1bwa2bXPgwAGmpqa45ZZb6OjoyL03EiuvLLghECH2nOfNoM1ahc8LReJ06qjMqWOcWZ3Sh4rIdFSqgLxHfyNkTLajkpQQM31EfMmsJRpxyyD/QWWMuKw/v4rtL13Ll87vAmBDu/c7L44h2xW9uEdCCUo/ocI717+GXbN/XXU/y1WwXQWfanMqtoi0O3ciTTj4Mh18fIqNUjQn2lJlc/tbuLf/ZdUHl4eIXpvgVvKIJV1xUOsoPY85PmxXKfC0yRI0SdfAchV0xcVyFVwUBLIlZUy7lubOrnMk3VJCIKJUV6hXsps9bfo5alf2PWoFqlhDUvYwap7Er9h88shKpqw6FCp1hkEh3URXHcCl05f9/JJ9iaUF2+XH6z7RhqGEmXVqq1RCahjF9hNVz7PIGCDhnq5vYHnQhI9zyQBuC96a3XoPvzn42xhKoWL2aic7rvbxQWkyK6uSaW9vL+lmeezYMdLpNMFgkIcffph7772XeDxOMNhYAi2bzPr0pz/N9u3beeCBB7j33ns5evQofX19Jdu/613v4gtf+AKf+cxnWL9+Pd/5znd49atfzU9+8hNuvPHGpo5ZDtn21kuWLOEd73gH73jHO/jWt7519StjajGSIyMj7Nq1i66uLm699dYF7QhQnNVoBNG44P98J8KLP7mdX/rn3+QHZ3+RpLum7v0FDkFx+WVa7hUiY1xp11TG2CmV6ee28bXPL2Mq5hJb5rnaukZ5IkZYksBYYzLKap2FVFPSccxEizUvzRRlFCOW6aAbArSizyDqm8vdfMKjicm/hfwGerLw8+RfP8VtkgSpmNmUXgzcKNGxwLxI2pwrU3Kz92M1LLBoZm1XhDWjMPatc4yfnKu9TyatXBeh9evXs2PHDm655Ra6u7uJRqM8/fTT7Nq1iyNHjjA2NlaQrfijD3ydoxe9uuxkysIsMuSMJdP82T99F1fKq04VA/X7xWTRrBR4zZo1/P7v/z5LlizhAx/4AN/85jdZv349f//3f8/mzZsrenSYpsmzzz7L3XffnXtNURTuvvtudu3aVde5E4kElmXR1VXoEfDoo4/S19fHddddx3333Uc02ngW8hpeeMje3636xsRiMXbt2oVpmuzYsaOAiAGYTpU35Qw9fGZuLEUlSaJo0S5tUVEtW6qMKf/8LSRjqj+jbdsLlSXgSIWE7RHQccvAzlNj6LbE93cBEIKtHSvo1D3Sd2O718mzmIyJKLVjynxlTEDx8dBtv8nPD27BJ6orUSbNAKarcTbWTdwuDPVD+tyi1ac6BcoYKWFz+683RMQAtNehjMk/j6E6BeRMZQim7blEZNrVSLlG7r2Y48+93iratQQv6jpBqgwRA9Ts7lNOGTPiruKow7zGHVIKNLGZuLyZM+lJRszj+MVqPnF4RX1EDPWPx69bgKA3GCOie3GN6WpMOYXrkfx27gG1G0OpXQ611HcdCSfOLOMoUsEQCWQTAZFf2ciINd3wflmE1DBvXXIfbVqp+iLbnfdqxfOBjKnV8Skbh1533XXs3LmT7du3YxgGTz75JK985Su5cOECn/zkJ/niF7/I2NhYXefMT2Zt3LiRT3/60wSDQT772c+W3f6hhx7iz/7sz3jFK17BqlWruO+++3jFK17BX//1Xzd9zHIQQuSuhet6v9Wf+7mfe/4qY1zX5fjx45w7d44bbrghl+lbSDKmUWVMJRwbVXjHV/qAl/Iza1/C/9p+gU29z6BR3fg3pIVIWKVddhYStkwCc0xxth7blQqOK+rKbDQDSREZYxbeqhMn23nuKxuJBw3srkLypVy7aIDQiEO9/mICwJUIV1adtBQHOk6YzKzQMTsaX6yVK1NygTKCmbogpWzdy0Y22W9IlslH5ZMxDjjNPHHKXAspve9G6jWyoFdgDo0nTZKWg4JnFl2LLFoorc6q7g46hh0ufKd8diq/HA5KDdgcx2FqaopoNMrJkydJJpNEIhH++esnOXB+jtSRwGg0xpL+jtxr933865yfyZRWNqOKWWD0tl8eMiaLWCxGJBJhx44d7Nixg/e9730kk8mKCoTx8XEcx2HRokUFry9atIgjR+prDf4nf/InLF68uIDQednLXsZrXvMaVq5cycmTJ/mzP/szXv7yl7Nr166rsu78GlpDI4sIRVFQFKWl+Gl4eJgDBw6wdOlS1q5dWzYAn0yWkjEbwhFG/usnub/LkjF5H0U5ZsOiOpXC81Cm5Ca930Y2+5/KkDHpPJ8YRcDaXcs4b42DAFUovKh3A1+/+Awb2jxlTEegcMztSi3fqrmW3kv8PTy04zcIaT4uXryIYmpQpcppIh1kTds4J2Z7So4Z0vLJGBsrbyHdadzGXb2/WGNcpWhUGeOr2p2qEFNWkG7DU2VMW4XXcMb206knMGV9wUWXHmPCKk1sdmhx7ug6iVEpgAQCorrywiy612bEjexOH2LeZnipo6pbiVoJZq258tOAch0fP7SUWAOBY70tDvRM0NwfniWspwHJntjSku2CSh9wDgBNhJiwKquIwmonQaWNofRRXAkqsFjpZ8Ypb+xddXzJRTwnLza8X25/ofObg79Fr1Fe2XC1kx1X+/ig8fgpGAxy44038u1vf5tkMsnWrVsZGBjgr//6r/n1X/91tm7dypve9CZ+93d/t+z+2WTWn/7pn+Zeq5XMSqfT+P2Fz5ZAIMDjjz/e9DFrIf97e16SMel0mj179mBZFjt27Cgw9lnIjgDzRcbk4wfHNX5wfDmqWM7rtqX4lS2nWd72NIJSKW9Aac13pjm46MLAynjH5GdpbFtFNRbmWrvSKpA9ZrNg0XNt7P7ORmbwI3vKTCayPMGhx1yMmcYmRCGzBr7Vl8tCQvtpi/igJNnXwE9KyhKDYADXV2d7znKHVIsW/5eJkJCAliwddP6pO30+xisKeSujXMv4EsPeJlAhYdoyYok0UgXXbcG0uAV0BfysSvsYevhimafIHBKJ6oFlcclNMpnkAx97uICIyeLS6HSOjPngF37ImRnP6O9qVMVAc8oYw6jf8LcY5WqeWzFOroW//Mu/5F//9V959NFHCwKMX/3VX839+4YbbmDz5s2sXr2aRx99lJ/92Z9dsPFcw/MDzZr4uq7LsWPHOH/+PDfccEMJiZjbTkpm0qV1wp2PXSKfMi4uUxLKHNEvk5KBDx7h7Advwg3VNwdkOxvlH7SAjKkx3+pDwIr87L8gZec/DyRvWXkXJx+b4TzjubO8pHcT/3nx2VyZUmew8DcfKUvGzE3aIjP2l/Ru5MNbXw3A0aNHGRoaomNFF0m7vJdgytFQhct4aqDkPZ/ioOUl0XRFcn1nBwDD8Xbuu/7PKl2GqmiUjKnEE3riksI3L6Y7WB3yvIam7cLzzNqNKWOuC42wa6qQjFnth43tJ3LEQzkIXHzl2l/mIS0FjvSqy6Pm7TztPF3XmGpBEAZlM6PmCCnrVMF7AbGB/3tokESzGbwMNCGwyyh71EytfmcgSUAzSTsaE057yXYRvY145vI5UmXEHGGZv5OkWxgvLPGtY9Q8R8yZBClxUAnIADHlTMNjNkSIISUITnMNTgSC/znwRlYEVlbc5pqBb+toZYx+v5/JyUn+5E/+hA0bNjA6Osr3vve9qvs0k8y69957+djHPsaLXvQiVq9ezSOPPMJXvvKV3Hw4Hwmyarh677AMijM7ExMT/OQnP8Hv93PbbbeVOCwvVEcAgPbAwnm2OBIeetrPL/zDBu781Bv4wv7/yXh6GzKPLwuK+SeD6oGhzH3ufDJmIU18HWmVlCkZThv7/vUGprWA17a5AkrMYl1JaLi2aW8JJBkD39qbCiB8wSZ03ppzzau1j1PKkzi6aFrZIqVsvHXwvEFSViSV99rMbKpAkSClhGZqmzOGvbWMJkuuolLpjXp2rg+RsB9FFUgV7GBtVcy8wpFE0ir645MMPVm7VjuZbKy++hP/+DjPniovEX38id0888wzfP4/H+e/DuZltxSuOlUMLJxnTCU0WvPc09ODqqqMjIwUvD4yMlLT7+Wv/uqv+Mu//Eu++93vsnnz5qrbrlq1ip6eHk6cOFH32K7hhYtm4qd0Os0zzzzD+Pg4O3bsqEjEAMyk07hFz+1VoXYufmlP4ThmbAoe/HnPkPZ/HUVLuQx+9CDUZQxf/gHk1quMcaFtr3dN3ArBwM2dS3ndspfQ1ePFo9m4dUvHcm7sWEFI82KozlAhGdOhlcZ0uuaQ/eztup/fW3s3H7nxNdi2zbPPPsvo6KhX/uXvrjjkhK2zPDTLy/ruLXkvv0Qpbvp5z7o/QxVpbBnk+4dva7oko1EyphGMpNuxMsbJxYa+s7kypdqZD13Y9PtmCChz12CzX2NL+4GqRAyAX1hVp3Mp4evRrUzaPh6ZWs4Js/ICv14oog9X3M5Fq4NzqaOk3KmiMW3kgcP9LRMxAIPBUoLFp1q5GCagWRiKw574srL7p925a5pwvH+H1DlPtKDSxoCxivPpY5gZ+wNvPSGIyD5kuSxqLYi1TDdJxAC8qu8X2dy2peo2V7vy5GofH1RugFAPbNsmlUrlvGD7+vp4/etfz+tf//r5HCIf//jHWbt2LevXr8cwDN7+9rfzG7/xG5ft2l7d32AepJScPn0658y/efPmshLvhSRj/LqGcRkYyOkUfPj7bbzkb2/hVf/0m3zn1C8Td67Dp1wZIZOel71RipQxCwVHmgVkjBZfxHf/ZgvudO2Sj+JneiDqojbh6yVkxvOkgdgkOObQfsaCOvxRRNEE6mgCqTX/k5TN+KeUPVDjm2sJWX7for/7OwrJU6VeMiZ7HFsiyxn2tgjva27+uIamsHpxO6Zlc+jsGK6AOlXTrSuYXYmWkBjTEv1wrK57D0rLlKrh43//KD947lTF9zu6B3CNdj734yO534uEK0gOVsfijsac+VsJJqSUJBKJhroBGIbBzTffzCOPPFIwhkceeYQdO3ZU3O8jH/kIH/jAB3j44YfZtm1bzfOcP3+eaDTKwEBpBv0anv9odGGtaVpDyuLJyUl+8pOf4PP5uO2222o2T5guU6K0ePdEyZNXnbEKXhMiU6o04dLxLa+c238pSf8/nqrv+Vmu1LVOMsYXlWjjmRr/MmRMp2Hw4RveBEBfb3tuvACKUHjr6pfmti1W5HWoxWSMRFVchJCsCHXw0sGV/PrK24jFYjzxxBMAuescUis/TyJ6kp/t+VWWBxeXHD+omUip8fLeVyHTW1gWXMyq0FpeNfD/ETE6ql+MKghWq5nKoFzXJijj81MEF4WL6Q6gtNX1TAPKmB4jjhDQpXua0Z0dYa7r2As1vGAAglXKlwB+MHUTe2LL+Nz4Oj43sh2VUnKjXqhiJRY7OZeGC+kjGbuAQvjFJj52eDHdvlKlal0ouuRdvtJkc1dgTlurKi7jVphJp/x9l3TnFG/TtqeMjdneNRswVuMiuWQWxhBZvZvdhGK6S9/AgVjzbazvbH8xL+p8cc3trnVTah2tJLNiMc+ao5H4qZlkVm9vL1/72teIx+OcPXuWI0eOEA6HWbVqVdPHbARX9zeYgWVZ7Nmzh7Nnz3LLLbewbNmyij+OhfSMicVi+NXL+6M8FRX876/3cNsnfobP/OhnERUehAsJPc8zprhMaaFgy3TBBD1zehn2uKweeGUybvlzpmJKAuNNGuy62W5KjX3nvimXjhNmWXPefOSP01Up639SbymNzG9n3QqaKmuqpIopU16UT0DJ+siY7C1XYtjb7Medx4lVCLh+TT9+Q+PkxRmSaRup4BFGCz2BuxI1KTFmQDXBN2OjNtBuvVaZUhaffvBxvv3E0aqfZ2Imyf/3H7so6PraoipmIX2NGyVjahnQ1UI8Hm+4y9/999/PZz7zGR588EEOHz7MfffdRzwez3VXesMb3lBQv/zhD3+Yd7/73Xz2s59lxYoVDA8PMzw8nAtmYrEYf/RHf8QTTzzBmTNneOSRR3jlK1/JmjVruPfe0gz6Nfz0od5klpSSs2fP8swzz7Bq1aqKybFiTBaZ9w4Gw1z4fGkph5pyEHbR3CAkXZ8+X/BS23NROr9dx4KsBhlT7VnTcciGuEC6pWSMJuBvtr4FPZMo6++PeEPNe1Ze1zZHiPS2FZExmr/gEekRMXBH72qm5SSaojA2NsYTTzxBX18fN998M7rukR4hrfJiP6Jdx8sW3U2Pr3CbkOrQ6SzmY5s+xKsX38NAwBvvK/v/F8uC61kcbi6+jMfjHNt9oOZ2zSpjAM6nOoEyypgMGZNta11tyuk2vGdhlxFnUwgW+3fhVOvAIxVEcjHjkxuZSlduoDHp/jxHkp7C4gczS1FRUWWk1kcqgapcT0Lewpn0DMPmUWSFVlt+cQN/fXCAlCNZ21bda7ISir8KTVVKFoQdfu/3qmKzPnCRS3ZnxeMlMgoVXQSZdbzyuVFrlKW+9VwyT5Jyi/0uJU6mhtmmsRg9qHSyd7ZaEXZ1LLNWEDncydNPP82pU6eYmprKmakW42ovU3q+kDHNjjEe977n4iqYamg2mQVeWdTg4CC2bfPlL3+ZV77ylS0fsx5c9Z4x6XSaXbt2EQwG2blzZ826/YXyjBkeHmb//v20BXzMmAvjk1ILh05b7D3/c/zOq57A4sxlO29+M+XCMqWFVcbkt6jUMFCs+jLt+SRIaNip2hGp6nEkNQ18K0GPe52WplfrngdMlXF6HXdKr6WEulUgUjB/i/8GrleBKqYMikcUjc5NoEJmroFbw3Q4o4ypadhba6zFp2jxcl23vIfZhMmBE8MFrzsqniNdnWic+5KoaVBTc/sK2yUw1thzKZ2yanYK+Ny/PMFX//tgzXsr4ZfM2HPZratZFRP2GQ37f81HmVIjwQTAa1/7WsbGxnjPe97D8PAwW7du5eGHH86VgZw7d64gwPnUpz6FaZr80i/9UsFx3vve9/K+970PVVXZt28fDz74IFNTUyxevJh77rmHD3zgA/h8V6YE9hquLtRDxti2zcGDB5mYmGDbtm10dlZeoBVjqkgZs/pEgqEKSj4lbkNeQxbfgRih56ZLtuv5xhDpZSESN1RTImR83/JzAXUoY0Qa2o7Z3v4JgVsw/0j+cN3PsTQ4N8jFizu8/SocOlgUvxqqRlg1mM2UdaiKy9bICl69bCPP7D+KmrDZs2cPmzZtYvHiQpVLJWWM7QZ4x5o/AKDbKLwmPxe+gy3pQdoyXZ5WhLoy4/UGvKajculTJUSjUfbs2cPyJUtQLyk4stLCWrZExgyn2pmx/dhFJmQJ18B2lfqUMbpHCEybAW7p3IdbluwQhNUVxJx+9sSSTFieae0v66X3HsC4/SJe1PdBvjTy8dxrKgoq9ZExjiuYclagqW3MmBdqbu9jM391sBczk+xaGRpHE07JdamJontUSsmiQJhLyTnSJGyk6VBjbAhexJYqe63Kn8mUaVQMgmoPMIkAVgZ6sStEOEKCm3kvTZr6TSAEcXcJSbc5Emp1YA2/vfZ3cNdJotEoExMT7N+/HyklXV1ddHV10d3dnZsTr3ay42ofH7QWPyUSCfx+f8P733///bzxjW9k27Zt3HrrrTzwwAMlyazBwUE+9KEPAfDkk09y4cIFtm7dyoULF3jf+96H67r88R//cd3HbAVXPRnj8/lYs2YNAwMDdUnFVFXFNJvvNV+MbNemoaEhNm/eTM+ZWS5MN8/ItgKhqpydMPjgv76Id/5SN4727GU5bzKWhEysnv8VLKQyRk5uws1Ls4+dmMx0OKq2k/efrOLEmHExYi3k13NlSqKp9nZaWtJ5zGR6lYFdxmhQON7Hcf0VrqNKfQSLlFCJpGjw41e3Ki5/7nxVjMiOR4iyB5qaSpA/62YJL5lHxmR3K/7krRAx84lFnX58hsHRs+Nl329YFVPvBc+SMOmirJaUtJ1NN0zqSOmVKgVD5Rfi//KVZ/jX7+2t+VnWbVrEd/cXdUGYD6+YBRIWLe5sPPvbSjBh2zbpdLphMgbg7W9/O29/+9vLvvfoo48W/H3mzJmqxwoEAnznO99peAzX8PxFo3NWrWRWPB5n9+7d6LrOzp07GybxpvKUMb2BABf+7r8rjyVuk2W1pQs9nz1XcdvF/+8IZ9+/FWtRhWRdrTKlCs/g8Dl3Ti0QU3A75vZ5Se86XjFwa8H2g4szxFSV6y7E3KNNURQ6ND+zjklA1fj1lbfwu9e9hEuJKQCc6QS33voSIpHSRXBILSWfXCn4pf63EtQ8BU5ED6ELFUt6QVG/0YVMzX3Yte2F7Yc3ddVuR5yPc+fOcfToUTZs2MCSJUuIjAeYMMvHx63mixxUjsbKlQMIZh1fTTJGEw4deoITsR4SyR2Izq8UKE9mLD/DZjspZ4Bx2wSm8vaW9OqlZslnZzYQPPtL7B/Zj5Rzx9JQ0GooYwRBjiXCCAV8ygxkSnuqwWArHz3UXWCV1GEk6fHFGE41rsTJh4uk2xfIkTGGYnFD2xDrQ8MIAfvjtUsxDBFGE2Fgkg3h5ZxPH0Wg0Kf1M+UUJq16jJVcNIcASIlEWTIm4egE1cISpi7tBp6YGSmzdW0sMvr5X4NvQVN0MGBgYICBgQGklMzMzDAxMcHFixdzJSrd3d0kk8mrOllxtRv4uq6LlLKlMqVQKNTwXNZoMiuVSvGud72LU6dOEQ6HecUrXsFDDz1ER0dH3cdsBVc9GSOEYHBwsKZZZxaappFMltZXNoN0Os3evXsxTZMdO3YQCoUWpKNS3VC9r2s6Ae96aBN/8otdBNuru0rPB8LBEDPOBABKnsmZ7SzEA0AwefJWvvxwXps5xyU1Zc11yK2hpBCOt09wuLVyNSFldfKnDih2XuvrSOH1Eo7E9ZcnGLwuNMWpi/LnkKJyOVNTsU+d5IAE1DIdlLKlTuWSYKbl0NYZYDZpZsgur6NUwaZZNVLm4SukpEzT7Dowv6v5no4g3ZEAx85GkZR6HwDYDapi6oKUKCZoqdJrKpGELpp1t2wvRqICGfOVb+7h8998tmL07Cqe8qW3PciR6cKa9flSxSxUmVKjJUpw+Wuer+EargSqKWNGRkbYv38/S5YsYd26dU1lY/PJmA0XHYbSlYkfJeEgMw9TY3cCfaqyr4QAlnxon9dhKVhmXGUfJl7pkaj0MSR07c5rAe36cTJxaL8/xHs3vq5kF79PB7W6+5gi5t4XQtCh+hgC7uxbze9e9xJSqRRn9h6mW/i5+brryxIxAOEyZUqDvlu5peumgte6jHZG0t4zulMLI+XcddwUKVxgb+sr9pgpD9d1OXLkCJcuXSpQR7VrwYpkjFLBL6YRnEmWV+7M2v6ara279DjHY308OrqWBzbdxaizh6h1lB5tE09OBzmbzipCSpO5YSWFXym8V8fs9bx06UMkulJEo1GcibmODMIBM+Ur23pcEd04rGfYHMKv1Z/YNbiRvzrUVUDECCQRPUmff7ZhMqYk2YXEnyk13NBxkV9e8SxtmucDk3R1LpgdNY+pK0FcqbEhtILz6SOZ47ooSpAs7yUltGl9OSIGwBYmivTjirnngys9f6B8MqZN7ee52WhDnzOLdrWdty65j6BaajQthCASiRCJRFi5ciWWZeVUM9FoFCkl6XQ6p5opboF8JXG1K2Oy5V+tkjHNoJFk1otf/GIOHTrU0jFbwVVPxjSK+TLwnZqaYs+ePXR0dHDTTTfl6qGvJBkzm9f9xJHwwf8Y4M0/+xpWLf8GkoUrnfIbPsjwWwvZTUnFx+EntvOjZwsnFf+EW7AIVRwKvSmKIIDAqIva6iWRlG093SiEC+2nLGJLJKne/GtWWUEhtQbUFS2Y/paeuKKopQTClahlNhRZNXiFg0SCfmaTpveduqVGxoqb+X4FXreM+VqVN8nNhAMGK5d0c+T0KONT1Z373cA8esVIiWJlSJgK96FvysZINH+TppKlC5xvf+8Af/eVJyt+DtsHQgpcIUl1ClKxoh/aVdpBKYtmyJhWDHybqXm+hmu4EijnuSel5Pjx45w9e5YbbrihJbPC6QwZE/H5GP7UrqrbqgkbGwNpQ8/fnEHU8MPSki6DHznI0Luu98xcCj5Eec2ndIVnDFwG+pTENz33Xn9nJ5eYxFAEn7zxtypmahVDrZrFVdW5+TqrjAHY3LmYqakpdu/eTU9PD9vDaxkIVy4BCxaVKYWUAd664q0l23XnkzF6mGl3bjG7JNhRsO3icG3T2ayHYzqdZseOHQVd4iJ6oOJ+EkHaVjFUp+kpslJSZtbx11TGxB2Do5OrCYoQ23pX8MzkJmyZ4q6e9/Ds7OeAYk+TORSrYsat5ezs/Sc0xaC93aC9vZ32dBujCU/dokrBxXOTKBuV3OSt0IXJekbSJ3FprBWuzs185GBHSW4wqJloikufr3yL80YgpQRh8ro1T7IlMlSQ7zyb6qEei1FNBAiqOidT+wpev2QOscy3HqTNpD3GpF1KqOi0k85LciUcI+cDBKCgMmp2YsqJhj+bT/HzW0vuo1Pvqmt7Xdfp7+/PPeuEEAQCAYaHhzl27BjBYDBHzHR0dFxRMuRqJ2Oy80krnjHhcPiqNlGeD7wgyZhWPGOklAwNDXH06FHWrl3L8uXLC26CK0nGTCZKFT//+EiYl27+FX72lm9iy9oyx2ag5H3+hSpTMujkB9+6iSNnilhrt7TUSDiUzTjkwz/Z+go+5xkzDxBA+LyNakrig97gK6kZpKBua20pZWaCvrwPKgloqfLXRrhQrXw55Mv78qQsucbC9UrCcoa9zdn2IEUD+5XZUFMVNqxaxOnzUfYfr20S6ajVP3cjEKaLlhJVFS9K2iXYoE9MMYpNfL//30f4xL/8uDqhJIRHuGkwHit8Js2rV8xClSk1qYxpJZhopub5Gq6hVbRapmSaJnv37iWVSrFjx46WCcXJjGfM5imV8zPl1YVZKElvHMHvT6HF6kuwZTssDf/W6rqeH64rUCq41keOzF0Hw6exqK8HRid4z8ZfosdXWYWg+7Wq59aUQjImonqlVYstnaeffjoXd568YNNtVM4Ih/PKlHxKgF9a/Fs5I+F8dBve804VCu1qkCk5V17b6P0Rj8d57rnnCAaD3HbbbbkkpZQSKSVtWmXFgCsVZswAAomh2vg1G11x6k7+VMOMHciRMUKQaR5Q+NlSjoGLws/2bQJgaeA2NrS9ioDWzi2dWziZPFvx+PlkTNTu59aef0ZXCj+rKuae7z7NYO3adZwV7diZcqfU1BJGgkcb/mya3MZHD0XKirQjujf/9vkbj/2Lr3mX7xy39/2Adq1wTjddlfPp+srXfIrGqVR5I+cL6bMM+AaJlxj5elBlqOArizk+rLyAqk27gSOJxrsnKSi8afH/YtC/pOF9wbu3g8EgK1asYMWKFViWxeTkJNFolEOHDuE4Dp2dnTlyJhCoTEguBFpJFF0OOI6DEKLp+CmRSBQQvi9UPC/IGJHx7KgHrXRTchyHQ4cOMTY2xs0330xXVymL2ua/cmSMaTuEQwaxdOEC6nv7NC5OvJLXvvhhFN/YvJ+3oEHKAhj4+uwl/Nu/bWJssrTe25iRJaUZnpKiNIgo2E6h5RKj+VLGZCGA4KgDDsSXamWPLWlMFSM0Zf4XrXX81IRboYMSZFori4rGyT5NzW0nhChpxSwciVQzpI6ueAv/+eDESgx8BZUKoNrCPvyqVkrCuC5UmFTs+VLFOJLAmIMTqvJ4diXhc+nK79eJfDLmsSdO8FcP/ghZ72coUyooFTl/yqAFQqNkjJSyZWVMMzXP13ANlxv5nnv56uAbb7yxrm5JtTCVShHUNMY//WTtsSQdZELS9bmhmtvmo+25KOn/CjL5irx27ZVKfCuY+AoLOg7OkTF3vGQ9jpbglYu3cmfP9VXP7w8ZVX/rhjb3HFEUhYhioKHgDI2x7cYb6enpAeDGzmV0+yqTX0Ft7jkm3PU4srxfTo/hEUedehuKolTsGlMLWaPewcFBrrvuutxnlFLiOA5SSiJa7UWTRJB2dNKOTo8aYWN7H4cSh0BJZt5vHLN2LWVM5qhS4b7VdwKwyH9D7t2bI5v514vfqLj3Gr9nFjtpd3F9+HNoMliyyNTy6t00PHWUoXZhO1MArOi7kclYHLPAi6Y6VHkLHz3UVnRN5uLfOTKmcWVMzrcIl9es3M0tXWfKGixP2uvr0t0v9i3ibPokssw36BMBOvVOLqZPVx6PKFxbxWwfdiaz06Et55mZ4XK71cRr+3+N9aENTe0Lpd2UdF2nr6+Pvr4+pJTE43Gi0Sijo6McP36cQCBQoJpZaKLk+aCMaeUaxGKxnwpV8fOCjGkEzZYpJRIJ9uzZg6Io7Ny5s2JN4BX1jAE6Av4SMgbg4HnBx75yD3/46t0If+26t0Yg8liN+W5trcxexz9+aTWpdPlj+aZLAwel9Sq0uiBc5t24wlXBSIAYcsqTKCrVOwvlQUqJF0vO4yKvPM9VsomWrnxhcrdIhU3UnBcMSCFKy5ScDCHju3Js/2w8TcwtJTvUlMQpE286KqXS+Cbhm7TRki5OpaSolISHUvWKp6oilSl9/P6Pj/HRf3y0rlupEskmGlB01YN627o3imbaWsOVqXm+hmtoFY0msxKJRM6Ydc2aNaxYsWLeiMSpVIob0wEujtRePCpJm/YvjTY13/d8fYj00rwOSxUeJtJVoExHneB5tyDZcM/Pb8a/WGV1uLanSjDsr8pH55MxUkpELMUSNcidO3YWPCeWh6p3NtKEhl8JEtGW8Z8Xo7xuSflwvjvT3rrL8MiYeu+FfAwNDXHkyJGcUW/++B3HyS0II0b9GWzpCj6w+ZfZ0NHPcGqcPzj0kcwbjU8is46/oKSlGNmvY9DfR7tRqlzoMbrQ0LEpLdvt0WbpN2aYcdrY0P4gbdqiHEEP5AiZfGWMlvE60pXO3O0VMgZZ5N/IUOondX0mxd3OXx0OlaSL2ow0s6a3PsmSMd1GDAUXt8EJeFkoyutWP0W3Ud67RkqD40k/lLku+ejWu3AYL0vEhJR2AorBqHm+zJ5zcGVhYjHu+FCQaMLPmaSBW8Gnrxru7X45t0Zua3i/gnG5bsXnnxCCcDhMOBxm+fLl2LadU80cOXIEy7IKVDMLofBoRbV7OTAfnSh/GuKnFyQZ02iZ0tjYGPv27WNgYID169dXvbGvNBkT9lVu7T2VVHnfv97Mn//yIozQD+fxrHPBynySMYmhG/mXbwxWrAPW4m7ZQKx48V7r9aaRr4yZh1g0v4W1XqYddFnT3ioQah2qmGYuSY19hCurls/krlmFoE/mf0+iUH0k3YwSKq+uvmlCrD7/YwBCQYN4qrA1c7lLqyUcnGDpfW8H54k5cCV6vFQNBt61kQoExyy0eWoYl0iY7D1wnr/8wqMItXLp3Nzwyteg6ZpKSNOYcFpX6+SwQGTMQEdjWZZWDeh+Wmqer+H5DyEEU1NTTExMVFQHt4JE2iL5j8/UtW3bD6Pok9UXgdWw+P8d4eJHbiERyauDKTZAzypjil7v2jN33sGlXWzcvKTu3297JFBQ2l0Mv+6F3bFYjEuXLtEmdHYsWdvUgqPHGOSRES/eNcqUKMFcmVKX0dYQMQfes+/o0aNcvHix5H4oJmKEEET0+hebL+7czIYOz5PDp87FtTf4VxAYjnA4NM6EMoyq1mbj4k7tuNx2Fd64bHvF90NaO9NlvEyuD54n5gRZ0/E5ev2rgLkOMdnP7zgOKvnKGI/0MsTc9fIrA3QbZl1kzMzkRv7hYuk8JZB0+JI5MqY9Q8ZoiqTbF2csXW+iwWVD56P84vIDqFXajSvKTcTc6j557WqIgJpkxikldDq1blxpMWHXbkNt5/0IHSlIOAY+xcZQ1jNqNV6etD2yg5f1vKLh/YpRrIypBk3T6O3tpbe3FykliUSCaDTK+Pg4J06cwO/354iZzs7OeVHNXO3KmFbLqBKJxDVlzNWCRoLYRpQxUkpOnTrFqVOn2LhxI4ODgzX3aQ9cWRdtfw2psOMK3v+lpdx376tYMvifBa37mkcFMqbJbkoKGqd338r3flI98+OfLL8yVCpwbf6xvO3nYd0j5rFMqWoL6wxqlSdlLQi9kujCdtDzBVGD+ailisltJCv3P7JM7wsUEqQiCjxjlJQDiLma7+xxyp2yloqn2LOxzLaRsI9l/Z3sP11Hq0THRTVLB2LrQAMkWjUYsy7GrINbpo234kiskEKqR0NPOKjNr1NyOHV2nL/6l8dx2jxBllKFS5FSko4o+LIJqsyl8OsaS/1hjiYn57dEaQG4i66gH73BwCUrR2+WTPlpqXm+huc3EokEZ8+exbZt7rjjjgXpGLJk0uXYqfq6obRCxID3+Hjx91N85+cCuBU+SpaMyX+qazMQGJ+b+O/5+c0N/fY7uoJUe3j5dZ3R0XGOHDpAOBxmieZnRZOkVzS1jGnrOFCNjMkoYxosU7Isq8AvKP8Z5rpu7n9ZIgagXavPL8Mng7z3hp+b+1uZI2OSsQS/c9Ov0NXVhelY/PvQkzwy+gzTjKBUrI2uDoFExc/PL65cYtatd5WQMSouq/yTLG3/FIv8c6Uu2cVvdpHpum6BX4+wPA8dlTxfH7WfHr32b8q1d/APF8vPF5riEDbmJumsMgag1zdbFxnT55/h9Sueqmn6K9B5arb6veJXfHQZGhN2afzUpy8m5kyScuvrGJWW6RydFXcMQGC7GntmGydi1oc28MuLXtvwfuXQLNkhhCAUChEKhVi2bBmO4+RUM8eOHcM0TSKRCN3d3TnVTDMxxtVOxlxTxtSH5wUZ0wjqJWMsy2L//v3Mzs6yfft22ttru8gDtF9BzxgoNH+rhk99p52fv/lXuH3rf+LIyi7x9WGO/cifDJvppqQTZtf3bmHPsepMp5J2UcosfCFj4FsENel6nQ/0+SzZkV576xbhETHVv7dGTHsBz+/kCiTaa6ligDnypMKlS+Z38BEgMr0alZiJNDRwZM2OTNnzSClbUhx0RoIcODGc6QAk5o5X5pCqKXNjzYfjnz9VjHA9RZgZKbwZpCvRTIklBHZYZXJDAN+EQ+iCWdm7pwYcBb784yO4CriaqGlWbYfwfl95auGQT2eJFuJ4dBJCl7dcrhm0a4LHHnuMzs5Ouru76erqqmm4l5UBN3uf/bTUPF/D1Yl61BCjo6Ps27ePjo4OTNNcECLGcVzG/vHpeT9uNRiWZMk3U5x7tb+sULOcZ0z78bl4R1UVXvKy6h4xxejurrwgllKiSIf9+/eydfNmEokE6ekoqzvrayldDL8ydy5DKd/VIEvGdDegjEkkEjz77LMEAgG2b9+Oruu58WcVIUDJc7EeZYyU8L4Nr0bN9+HIU1su6unLKXAMVef1K+7g9SvuYNpM8IUzj/GTyb2klYkGeH8vobMhtKbqM7xb7+Z08kRBqc1qf4wlbR9mSXBb1TMoioJPnbv+YV+Ivr4+LqSy3bAUhN1Ju9KJQK2YJHXs2/n40cq/PUN1COtzsth8MqbPP8uhqj6+krsHjvCi3mPodWQZNWULUatyaZAmVJYG2hnJa1GdxWJjOVHrApasX8I74Y6xwlhOzDlLzPaugYNARcNpoFvsEt9S3rT4zQVlY62gWplSI1BVlZ6eHnp6epBSkkwmiUajRKNRTp06hWEYBaqZej26ng8Gvq2QRT8tZd4vODJG07SchLDSD2h2dpbdu3cTDAbZsWMHhlG59KcYV7pMqRGJ6Tef1TkffRWv+9lHMGXj7HIWbt4DtZUyJcPp4z+/spWh0drX0D/hVlyLlZQjSUn40jw67WbPk6eMkaKyIW0tuD6lpmIgq4rJv29dKvMzYj5JpzpRlyomg2p+OzOzqdwBpfBIAOm6SENDyRICNXxn6huEQBHgFpMMeYqZRn5PiiW9e0/OmdTOpypGn3XRkt59X0wCqnaRYbIQpLs1zA6VwKhFYNRuiLtwFUj3GLnyfKl4fkaV4GguVptW0F5WAQZEgDPnJ7B75/d+1BSBM9+GTcD6ZYu56aabmJiYYGRkhGPHjtU03LuW2bmGFyqklJw4cYIzZ85w/fXXo2kaR4823vGlHhz88REmDzdnwtkspPQ6K/b/wGT450tn0xIyxoHOfXPJglvvWENnV2O/3d6+NsbHSxNgjuNw4MABhG2ybds2eru7OX36NO1CL2kxXS+WBHty/66pjDHaEULUVMZMTEywe/duFi9ezPr16wuMerNqGKCsWrBaa+ssNofWsqNvZe7vVCrFnj17UHUFR7j4K6hHIkaQt627l7dxL+fjE/zTmUd5amYPul664C8WxQK8adnPVB1Xmx5EFRq2nPv+7+x5EyvDd9T8TACuNXddly9eit/vx+/0QBoM0Y2m+jyTY3U5U86pkv0t+w7+5mj12FhXHEJ6mmy2ImLkkTFVlC5dvhivX/EUi/3TdX0WUNgTM4DyZIoA1oT6uZg+WfLeMt9qLqZP4TasypckHB9SCmL23HUIKCFibn3j7tS6eMuS38anzN86rZEypXohhCAYDBIMBlm6dCmO4zA1NUU0GuXkyZMkk8kC1Uy1BgA/DcqY+S6XvRrxgiNjsl+64zhlmcVLly5x4MABVqxYwZo11Znycmi7wmSM5TRGOuw5ozD8Hy/lD17zLJY42NQ5JeXJmEa6KWmJFTz0pY3MJurYx3bRklXqWIue8b5JFy0lC5snzcfaUNJyRybHEDUNeWUl095yEQUgkVSmqkoP4Tba4KaSQWsdqpi5bSsbvc7GUqhtam6AwgU15eKENNSiFqa5YTe5Lm8P+piKVc7u+IyibGIVRYZieRk2YUmk4RV0zZsqRnoTtDHtzI0j95ZETbllv0SpChIDBqlujdAFE99M7S/IFZDqNpBa3gSuZP2KSi+0KyTpLu/7yk+oKVJw4fwUVqR6eV0zcC133gyR87G4o522tjba2toKDPcmJiY4evQopmnS0dGRU80Eg8FrZMw1vCBhmib79u0jkUhw22230dbWxsTERNPdKGvh8S/X7qA0//Ae6O1nHMbjCnZR3s0tImMClyRqXhL+np/f3PAZ+/sjRKOFZEwqleK5555DURSWLFpEJOJ1OGrWUDeLZYG5lsOVyBi/ahBW/XUZ+J4/f57Dhw+zfv16li5dmns93x+lWpvaWsoYxTX48JZX5/7OJka7uroIOH5iTgKtDkXDklAXf7bpNXz93BoePPvfrG/v4ljyOEIrnesFoJghzjx9iJmuYXp6eujt7S1Rf0W0MJrQC8iYPl99iqWRkRGmopOQub/8qrdO8KleKb5fG8Dn8+G6Lr2+DUwlCskYy7qdvzlWe22hqw6G6mIoDqarFihjghWM5O7oO85LFx3GaMAJ26dcz1C6sqJ+Q3g559OlpO1y/1qGUs2TuaPWRdYENpJ05j6XoQSgDjImqAR565L7aNfqq3KoF5eD7FBVNUe8AAWqmdOnT6PreoFqJqtUywoPXshkTCKR+KmIn54XZEyjnjEAtm0XkDFZI7ILFy6wZcsW+vr6mhrLlS5TSpmNmRMDDE/Du//pZt7xCp1I756G93flXI2q0oRnjDm8ic99dQWOW98Dwz9ZnWrIV8YIWxIcnX9VDBR5xlQgRqrB0QRSrVGeRGOmvQCKprTctbsiKnxGCegVysbKoVp5l5QQCfqZiSdAgBVScUKaN6m42f0Lx1PWy6aOUha/T4NMTCGU0qMYGUPl7KGqqZ+y951qSmwDbEMWGg23AD3m4PhVtIQXMOWXDBmTDooQVZtMuIbC7AofiaSk7Vy6ooLJFZDqMZB5njQulcvkpJSku+eUXfnXx3UlQgW7PpuAxrBAN3hxJ6VyhnsTExO5DJVhGASDQaSUJXNKvfhpMaC7hqsT5eKn6elpdu/eTSQSYefOnbn7upkGCPXAcVx+8vX6jHvnEzLvOeofd7EUQbov//3Ca9OVp4rp6WvjxltW0iiWLO7k0MELub8nJyfZvXs3fX19bNy4kR9MPJZ7r5VW0wBLAp4yRkEUeJYUo8to9wx83fJlSlLKXHx800035RaF2feKjXoroZpnjJTwB2teQVD3GIuxsTH279/PypUrWbFiBb4D3yDmJKp+jmK8dPF6vnFhDx/c8noAfjRymC9f+DFD9uncZCWQvHzx7WzvvZWxsTGGh4c5evQooVAoVzYSiUTo0NvwKwapPMPasFZ9ESil5Ny5c5w8eZK+xT0cnPW+d3/GA0dXvDIlv+q1WVcUhT7/Ro4nvlVwnMnUBuB0zc+bJVRCuolmq+h52TFfkYlcu57gdSufZnlgouZxi3EkEQHKEyAbQis4nz5S8voK/1rOtUDEZDFjSxJ5Pwld1C6Z1ITGmwd/i0W+/pbPX4z5KlNqBIFAgCVLlrBkyRJc182pZk6fPs3Bgwdpb2+nu7u7gNS9WjEfyay2tsY6YD4f8bwgYxqBoigoilKQ3Umn0+zZswfbttm5c2dLZoo+XcOnqaTty9RfuQjl2lrXA9OBj/7nZn7vFUvo6/82jax2HDcJmVZ9jZQpCRQuHbqZb/6wgQek62LEqi/68wn+0IhTt1qjUeSrO9o7AsQmktV3yIOrUrDgrYSqpr1lum5KZKZ6dgEnhzKXf0lPO6Nnp+o/Ro224H5dYyazndmpl5633jKlGpfBZ3iPODfPE7hg9yJFUs6rpgyUrLeN6eKi1vQBagTC9Y6bM6fODEJJu2gpB+nTan/lQuAEBVPX+TGmHcJDZsFvwxWQ7jZK7kupgUCUMFFSSsyI8O7RDIp/a1Z4/lUx3snn/5BQvZNSvuFevnR4aGgI0zR57LHHiEQiuQxVvR2SrnnGXMPVhKz6oVzbak3TFkQZc/DHR5gaqbdEYmGgJqDjkMXI7QpWZ+YzS4HMzFVqHEIX5j773a+4AbUJsn3xwFypY/Zar1u3jmXLliGEKGgC0SoZs9jfhSqUmmqSHp9Hxihm6fls22bv3r05hVR+FroRIgagTfOjIHDLPMBXGoP8wpLrCwiMTZs2sWjRImDOxFcT9S9LgprBju41ub9ftGgDL1q0gT878CCHkx4x4Doar19+J4aiEw6HWblyJZZl5brc7N27Fykls10WepHvTlitvFbIElgjIyPcfPPNDE3/BDKVQgG1iIxRBnL79RjXFR1H8JL+bXzhXG0yRs90lgoZaXyi8N705XW2uLXnNC8fOIC/UreLKvCr13E0Wf63uja4nAtmIRGjoLDEt2JeiJiI1suh2cJ7TKW8F1IWAsHrB97AquDqls9fDle6DEhRFLq6unKlOqlUimg0ysTEBGfPngXg2LFj9PT00NXVlVPNXC1o1dPmp0VZ/IIjY6DQxHdycpI9e/bQ1dXF9ddfPy9GR+0BP2Oz9TmEzzem4vUTAuXwiW938Yu3/TLbNn0DR9Z3LFsmAW8xUS8Zo+Jnz49u4cn9HQ2Nz5gp39Y3H1kDXy3uYkwv0KoNCgiFnkXtdZMx+S2sq20joGoJkxTeEPK30HQVa6FWqhkUj0jXFOzZxkhA4dZodJT1bNHLqy6yGpZqCptqxEkWbSG/9zVWKHkxi0hV4VY+ZlYZo1gS10fN8rN6oZgSJ6CiT88FTkICriR8Lk2qR0eVFTuFlxmowOzQmGhX8Y/bBC95GbN0t45rlAYVbnbuLvo4dgCcwNz2aspFOHMbucoCqWKgPHM2DyhWxlRDVjqcTqeRUrJ+/fqcaubs2bOoqpoLkrq6uip6j/201Dxfw9UNx3E4fPgwo6OjJeqHLFRVrem51wyuTIkSBQ9NNekpL5d+PcWZX53rsCQdAQjaTuZ3jYS7X9F4iRKAYWh0d4c4fPgwFy9eLLnWHXlkTD0eLtWgKSoD/i6mrerxaK/RQYceJmUlC5QxiUSC5557Dp/Px2233Va3UW8lCCFo0wNMW0XtkF2Vj930WlzX5ciRI4yNjXHzzTfnMvtAzgBXb9B49ReXbyl5rU0PQCZcm51aXGJurOs6/f399Pf3I6VkenqawyMnUGOiYFUkky5SK/0tOI7D/v37SSQS3HrrrQQCAfTZuXNkiSUjp4yZS0iGtf4CE18hQmztXEa3ESRqVm8jnVXGhHWzwGjYO6dNQE3zupVPsyY0VvU41XAmuQiYKnl9RWCQsUznrix0DHqNRZxPn2j6fFmElS5Ox0JM2kXXoMb98D96X8XWthtbPn8lXG1lQH6/n8HBQQYHB0kkEjzxxBP4fD7Onj1boJrp7u6mra3tsqt6itGqge9PizLm6rnDqqDRm0lVVSzL4uzZszzzzDOsWrWKzZs3z5vj9JU08U1aNqFin4sG8eUnfHz1v38RQ9RXqiWxUMooYyp1U9LdTh75+h0NEzEAvunagYmQgOMSGnYWUh+C4sy1Z7asvEV7lZNKKXHr8boQWVVM9c0KxCJSYskFK1CaO2HRon/D8j4mJqoHCcWodQXcbPY1f8Nqypgm+afDZ8fo7g5WHNBs0iwYR7UmA7nSIdvF8c3fnRe85I1BS+adXEL4fKb+PVPG1vAZFUGqT2dyU4DYUqMiQehmfsZSzF1mR5VYkbnt1aSLPlvIftkBFkYVA7gLYFCtKoK+9sYzLFmZbSAQYHBwkM2bN3PnnXeyadMmfD4f586d4/HHH+eZZ57h1KlTTE9Plyx4msnsfPKTn2TFihX4/X62b9/OU089VXHbz3zmM9x55510dnbS2dnJ3XffXbK9lJL3vOc9DAwMEAgEuPvuuzl+/HiFI17DCwVCCBKJBE8++SSzs7Ps3LmzLBEDhZ5784mhIxfn9Xj1Ir9MSU0BrkSzYMk3UznVqXS9B1/XnrmEw5ZtK1g0EKEZmKZJPDZGNBplx44dJde6KzTHYLeqjAFYGuip6BeTxerwAKpQCsifyclJdu3aRVdXFzfffHMBEeO6bu4eKGfUWw2RMqVKb1xyF22Kzu7du5menubWW28tIGIgTxnTQJkSQKev9NmaXy7VVqPUSAhBR0cHN63dzIruZXPjkQbPPvMsjz32GIcOHWJ0dBTHcUin0zzzzDNYlsUtt9yS68aX/x0EMp4xmhJGwZcrUwJQhEpInfP6CSi9KEJwV9+cwqcS9LwypXy/GIDRdBv/e8P3WiJidJayJz5V8vpiXx+zzhBunqI+IEJ06l0Mm2ebPl8Whh3iXKKNaDERA8gqy9QXdd7FXV3VjZlbxZUoU6oXWaJozZo13Hrrrdx+++0sXryYeDzOnj17ePzxxzl48CDDw8OYZnNVFa1iPsqUWqlmeb7gBauMOXnyJPF4nG3bttHZ2Tmvx2+7wr4xHQE/cdOqvWEVPHVC4dLEvfzuq57E5FjN7Q3FT8qNF7S2LqeM0VKD/NuXbyA61ThhpMXcEnPeSgiMu2jp6tu0mlvPX5ibeV49fYvaGR0u30NQagItbiN9Co5PrbhYFa5XylRzfMUbLPCkUE4VM3J+at7PM1NGZZR/7mLPmGaxbKCDkyOVa6YnZuYmfyk9skFKWXohXDdX2Zfu1edNFSNsiZ4pkNbyCqU7Aj5i0ymscF4g0uS1kJrA7PI6IemzErW49E3Lkj0CqUikdHOGvUiJmpTo8Ux2MKtoggWl8hv1UaoHA5G2gnaq9aJcMKEoSo74WL16Nel0OqeaOX/+PFJKPve5z7Ft2zYSiUTDmZ0vfelL3H///Xz6059m+/btPPDAA9x7770cPXq0rN/Zo48+yq/92q+xc+dO/H4/H/7wh7nnnns4ePAgg4ODAHzkIx/hE5/4BA8++CArV67k3e9+N/feey+HDh1akFbG13B1YGxsjOeee46BgQHWr19fNUtZyXOvVTjWlSnrlkXKmOy84p+U9P/QZPhnDaQj0KdkQTzRjHEveIa0zz33HIv6wtx22/ay17AjOH9lSuCRMWcSI1W3WRsezJ0P5sqnrrvuOpYtmyMgsmqY7HVrJqPdnqdKAehRenjt4i08/fTTBAIBbrnllrLXZa5MqfWkaacxt3jr9tX37PUpBksC/TmrlIivnbvuuovJyUnGx8c5duwYqVQqV866YcOGgpIQXZkbd0Ax8l7vwJdHxgCEtQFijtdZrMfnXf+XLFrLl8/vqzpGQ80qY9IYwltcW67Cc9PLSSp+1qvV74NaODnZTbGFQbfeiUO0oE11u9qJKmDcap1kDaudnJoNMi3KK8+dCpLgzeEtvLL31WXfm09cbcqYfBSXUPl8PhYvXszixYtxXZeZmRmi0ShDQ0McOnSItra2AtXM5fhcrZAxUsqfGmXMC46MSSQSJJPej3rHjh0LEmRe6fbWYV/9rbirYWhC8L4vbOfPfqUbjF1Vt9UVg5Qbr9pNyYmu5aEvryNtlf+Bq3EHJ1T5R+mfqj8oCYw7IKo8SIRAIlsjY/Lix9m88rCunnBZMkYikZqKggNpF0dXs1Y7Rdtljl9UilGty4GUEjlPBEAj2LC8j8P75jer6ahgO663li+U/uT92/tP1U9cg5zwGSoz6XTV8p5E2iISNJjJeDFVOp90Mp2pgFTP/D02/eNW7nPkK2P8mkoMsILqvBFTUheYnaCkQY/NtcrONwaWQmJ2qDmySY+5qKny6sQ6/bibG+sCHHNRE6oYqC+Y8Pl8/z97/x0my1WfiePvqarOcXp6cr53Zm7Oc5MkooWEDLLABotgCdgFkbQG9N1dC4MEDiAwXlnfBwP68lvLu17Ahsde27sGZBuBbAEK3Dt57uScp7snd66q8/ujumqqu6tz9wRp3ucB3emurjqVz3nP+3lf1NTUoKamBpRS+Hw+VFVV4Tvf+Q6GhobQ39+P+fl5vPWtb8Xtt9+esqRJxpNPPomPfOQj+NCHPgQAePrpp/HDH/4QzzzzDB599NGk5b/73e/G/f3f//t/x9/93d/hueeew4MPPghKKZ566il8/vOfx3333QcA+Ku/+itUVVXhH/7hH/Ce97wnl0NyiAMEhmFw/PhxhZTLtGyi514xEM0jeKAoUD1I2CDixpn2SQHhLh7BOwis0zv7a3OYcO2Otpw3tbKygp6eHjQ3N+Po0aMpBzrllh2ioNA0JUAy8U0sw0nEUUs8GTA0NFSQUW86qBOVHJwJX2x5K1555RXU1NSgvb095XplMkaXg2dMKpTppWe9IBDUmLJP16k37pQT2TgLGIZRBq+VlZWK6TUAvPTSSzCZTEo6k141nDJxO+MOPeOKK1OS1l2NpRj5V6aT7ssr5Y2wcgZs86lnGWVljIET4CABzAcdGAlVAwyLSNQBSlkQkt+9q2eqMJRY+iTowbJbCGKnDK6cq0JY3Ma2kDpKO1tYWScWgmXYIKmTm3gNsrLZ2ILfqfkAmHRjgCJhPytj0vnZMAwDp9MJp9OJo0ePIhKJKF4zPT09AKD437lcLhgMpRnXHnrGZIcDQcZkeyN4PB709PRAp9Mp8u5SYK/JGKMu/rTpQVBf5sDU+gbEHF/sgQjB57/Thv98nxPO8meRahikJ9KLMlWa0trYOfzdPzcm/U6GYZWHaYXH+nHtm5IJi2BySesRoUl0FBOMKrWJj+78m9FJaUaJj0BRz8Sl4BCagg7iCAhPAQEQmQy1+Qx2Tkm+ZEyuEiGZHGAZLM8V13RRJBS8mY2pQJLNc5P+XQAR0dZcgd6JpYzLuRxmbK7ESzgJ4sm82OWPQL2+aKoYCBSmxahyPaivf56XBMGCgQEXklMhIBFWhXQMCIFoBMIGaaaY8yPuNFCWgsZKhLgtAZyfAqoI7DifnhLff8WGM8/3Qa6dCUII3G43vvrVrwIAXve61+GNb3wjVlZW8L73vQ/b29v47d/+bfzFX/yF5u8jkQhu3ryJz372s8pnDMPgzjvvxIsvpifNZQQCAUSjUcWrZnJyEktLS7jzzjuVZRwOB65evYoXX3zxkIx5FUNOi8kWas+9YkHYo8CDeGVMclpexc0oltoA09LOS/LNd5+CTp9915hSiomJCUxMTODMmTOork4fWFBujSdjClbGmCugz0Bg6BkdeJ5Hf38/AODy5ctx10SxiBggvkypjTRi+dY42tvb46KytbBTplT4i8UcI0MEgUGdI3syxqbbMVq3sjsDwMXFRdy6dQvHjh1DfX09AEk9JpsA9/X1YZzMALFd56JEibm2cEfBJiQC2bgdcszCSgbGOobFbe5m/MtSaiNcnUrWukHNWA9bIYoVeEv523F31e14bunXEaX59dk2hGMQVV4xRsaAKqMBa8LKTrsjZdiiPvCk8JIXC2PHUrAcy5H0pE5YjK8CqNRX4sP1H00yWy4VDpIyJh30en3chJGsmpmfn8fg4CCsVqtCzDgcjqLtc6GeMfkoiw8iDgQZkwmUUoyPj2NychKnTp3C4uJiwS+4dNhrMoZLYION6xTBXy6h1sDA1myGs9WNNSaKyY31rFOf/vQfK/C+O96F0+3/ByKSmXmOSA8+tTJGEFgQqsPoKx14/oY75bqZiAjbTASCPvUL3riaPs46CbtAVKsnGCJRQRqDU2DbH0mKuhYZgOpZMAG1CWuixVqMF9ETgM9sVExVATe7popRtanOZcTCVOoZi1xACFBXaceEf1v6IxNBJCtjsjDw1VrN0QYX+iYzEzEAYLHolfUp/1WttKm2TCI6mTBC5cV7ZBpWeTAARErBBeOv/2hUgGBmAIbEH4Ni+drGkpd4fTxdKHu16DYFsGFpW7KZJ5W3j1gb9ulsUSqY8iy7EAShoISCYDCIO++8E29/+9tBKUVvby9mZmZSLu/1eiEIgpIyIqOqqgpDQ8mRolr4vd/7PdTW1irky9LSkrKOxHXK3x3iEEBp4q35PVPGxJcpUY1Hlm1cBFF98ZYcSpRkgmN9fR1Xr16F3Z554F9uMSmTZsUqU8oUBx0MBnHz5k1FkSd7MMhGvaIoFoWIAXaUMY1sOYYXt/Do698Etzt1/1BGPmlKqSBHS/Mii2arM+vf2VQEjJWzgFKKqakpTE5O4ty5c3H7wXEcqqqqUFVVBUopwvMv47nFQQDAK798CQ67A263G5yzJckQ28rtEHYWbqfstN1WmZaMkQ18pTmZRryj8h24rfz8TpsYM6JC7mTMdlSPV/w7pAhLWNQbbViJzimf1eubsYwZCCj8XjYzNqyEK7EY0S73VyMoRpQ+j4214aG6j8PC7o5SQr4/9jMZk4/qhBACh8MBh8OBI0eOIBKJYHV1Faurq+jv74coikowQXl5eUHChkLKlCKRCCKRyGsijfLAkzHRaBS9vb3w+/24du0abDYbPB5PSeIZZdj32DNGDcJTBAmFnQWEsIj14W2sD0sD6Ao9i8pjLhjrzNg0iJja3Egbjf29n5vwOu+78Pbbn0WU+uK+k1+QJIFBePlfXo/esTTmSpTCMRoGoTtpNEngRXDBEhQmJBAmOf9cdQlRChgN0oDMt+YHZQH5nSRSCtEQOz7qfdToY7kqrfCu+3dEBRkG15TETAjziNjMGnKHVU41gqSKCWwU/tK1mPU4WuvC5IQHM55NUJOUXKG5y1Tjz2zKlRJg1HNYD6YvT1KD5di4zcunhGMJTrZVo2dqGQIRsdVYRFWMSGGZi5VGifF+MQAQjQiIlsm+LTufZ5MglQsYCnABQGBE5X7RbQhgI9ILm8rKLIKdk0CK2wZNFJnnIRR4w7GmvH4rCELenZHEmmdCCM6dO4dz55ITQIqFr3zlK/ibv/kbPP/884deMIfIGaWIt+b3hWcMIGjcDkwAygOtscWJppaK5IU0EAwG0dnZCY7jcP369axl/oQQsLF3bTHIGIfOggpDahJobW0NXV1dqK6uRnt7O37yk58oiVkyCSO3pRjlGFbWCD1YjC9TtDucWRExAGBk5DSl4pExgsDgqF3brFoLNm5n0GdhzRgcHITH40FHR0daoo0QAqdl5/s3vuGN8Hq98Hq98K1QzEf+HeXl5aioqIDL5YJVpYyxcjsE+RFr6rYSULAMhU5swf11v40zjmTDX47kR1IMbtciIqc7AWgz12AhMgr5RdxkbMNcaCQpwSkfmBgrfOFqLISzI40CQghGTlLnf7j+YyjXZ3c9FQPyvbFfy5QKVZ3I0Ov1celiW1tb8Pl8WFpawsjICMxms6KacTqdOW2zEDLG75fK4w7JmH2CVDfC5uYmurq6YLVacf36dWX2shQyWzX2WhnDq0phdJsiRI7AbjNhbT3eAEuICFjs8wB90t92huBoaxmsTVb4zcCMfwurgfjfvDDEYn7t1/Gxt/0CEUwon7OMnKYU35apxTJAQ0kjwzobARcrv0hlzmvKVRWTLQolYxL+Nph0MBg4LG34QTgG4GMPagY7iTcqMiZR1aE3cvBtBLJvk9z+3XgRqMu+KMWJ5koMFeAVU1vlgNtowFTvAnrXgxCNkpkxEwFEEzRJqPhoa6kdacuU5IqdhO+ONLnQP7mi8QNtyLOUCtcQW1/bkUr0jC9B0EnbiDqLqIpZE3bK3CjABuNvjnCUh2A0JudZF5kFke9JViSglErx1cyOszRlASamEqa7ODlU1N2kwO++4TJed7I5r58XIw0gl5pnt9sNlmWxvBxvxri8vJyxBOJP//RP8ZWvfAU/+clPcPbszgy//Lvl5WXU1OwMBJaXl3H+/Pms23aIVz9KUqa0V2SMqq/ERKXnWSIYP1Fqji/dVp/VeldXV9Hd3Y2qqiqcOHEi7wFRodHWMhI9YWTMz8/j1q1baG9vR1NTk0JOyWlJhRj1aiEcDmN9fgXlYRfmolHYDNmTwYpnTI5pSlowsjtkzBFb9gEeRtYAHeEQpTy2VjawEdpQoqszQe3bo9frFSNVXjiGzQ0/vF4vxsfH0dfXB1uZEagECBiYmR1y4WgKMsbKMXhrvRPvqX8vjlpTX6MsyT11JiRw8FGr8s4/bmnEfGRYKdRuNrZhJpRarZMLjIwF65EazGVJxAASGWPhODxY+yE0GlNbIZQCaqJyPyKXMqVsQQiB3W6H3W5HS0sLotEo1tbW4PP5cOvWLQiCgLKyMkU1k+neKKT/tL0tCQsOPWP2MRYWFjAwMIAjR47gyJEjcYRNKWS2atj2mIwJxSS/hKfgTQzYCIXZrE8iYxJBRYqVkVWsjEjpMgYAp5rscBxxIGxjMB/2Y2lrGxPLBH/817fj0XeXQ+R+BQBgY09qJkEZY9DrAL82GaPb4GH07XTCCJXaTNXRz6IInb+woZcyri8xZ2E0cDBZDKDrfugMLMSQKM0UCHRn0Kzq/MUpY0QRJ5orEAzzCAYjmF3K7mVEyS4MgqmkztjpqBJ4FzLLRxPBMATtTW6I62HM9C7BywBhtxFU5TnCiFRLMKS0I6ldqRalsfKaRCKmwYWBqWQihgIpVxaUJfQJ69qOxV5ThkrnslikmEhhmdu5ZwigJCrJ4E1SiRJogutQkckYdWIYISTJFJuyBIhK/j5xvGCp+cFirZ8C77t4Cu99ff5KlGKQMbnM7Oj1ely6dAnPPfcc3vGOdwCQOl3PPfccHn744ZS/+5M/+RN86Utfwj//8z+jo6Mj7ruWlhZUV1fjueeeU8iXzc1NvPzyy/j4xz+e8z4d4uAg11ndUvSf9srANzEZjw0lL8P4AdEKGIwcTp5PTipLxOzsLIaGhpKSiPJBMZQxQDIZQynFyMgIZmdnceHCBUWdIl8LPM+DZdmiqWEAaeDU1dWFMr0DfavzAABjDqWhhhiBwhYhTUlWxogik3OCnpW1YI3fgJEa0NHRkXWJqiFGxiQeTY41wOUywOVyob29HcFgUCJmRBMgcHjxly/D7XbD7XajxumEgeEQFqX7xaHjcHe9DmEyAgO3lpaIAaQypVwxuFWjvPePm5sxH5FLYQka9UeKRsToqBHroRrMRnIvo3p31Xtwynq6KO3IBcUmK4uNUpAxidDpdKisrERlZSUopdje3sbq6ipWVlYwOjoKk8kUp5pJ7CsVYuAbCARgNpv37fEvJg4MGUMIUWSVQ0NDWFxcxPnz51FRkSwp5TgO4XCG3OMCYOb21rly3S/F8eo2RUTtLEhYhMGYX8LS6vQmVqelgTcBcKzSDNcxFwQLh2//Xwfe++YyWBz/oshqDeF4zwE9l+Im4UU4piJJLyYmSiGoyBj9RmbvFC0UyzojF+gNHAzG2C3DMKAARAMLbjsKkZc6feo2KcoYUYTBz2PoxSkAAG9iAWN2tx7LMaB8iYtCZDIm9m+GAF5v9l4xNoseNQ4TvBOrmHpJ8sLg9QwiLkNSWQ9J0+9MVBIRipzIB5OBw0YO5UkyNvwJPfTY73VyaRgtbtSyfkMAk3AcmHB8o3krF9cWGQWKveJBqVSKp05TYnfOERUoYGCk/Zc3Li93QMiYO48243ffdr2gdRTS4YlEIohGozkb0D3yyCP4wAc+gI6ODly5cgVPPfUU/H6/kq704IMPoq6uDk888QQA4Ktf/Soef/xxfO9730Nzc7PiA2O1WmG1WkEIwac//Wn88R//Mdra2pRo69raWoXwOcQhgFeXgW82HQXGD4gWoON6Ezgu9cLqvuelS5cUc+xCIKcpJXqK5Iojlh3FHM/z6O3txfb2Nq5duxZHBMv+FxMTE6iurobL5SoKGSOb2DY2NqK63AFx6hYAwMTmQMYUMU1JVsaA5rau7e1tkDAAFjjZcjwnrzC9QsakP54mkwkNDQ1wLtWDIXocO3YMXq8Xg4ODiEQiqGKMmBGl/tfb6hrhpT8GKFDF1mZsA5ejMoYXGawIDoAB2i1NWIxIxAsLFm8v/yD+r++/57S+VNDBgI1gNebF3Cb5ynR2PFT/2zhlSy7J2g3s9zKlQpOKcgUhBDabDTabDU1NTeB5XlHNDA0NIRqNxqlmzGZzQaVU29vbsFgs+/b4FxMHhowBgFAohO7ubgiCgOvXrysmZIkoZZnS5uYmpsdGS7LubLERCAEihWDaGSzqdMW5ITdXAthcCSh//+AX1bj97b8OoX4G4xNXEQwdAbDjJ6NLQUw5x8KaA28mIoJyBESQBoKGjTxnhfbg3tTpObC6HX8RykBS+VCACQuguvgHjpyspNvmJfVMDHED2Qwja4GKYArc2Uy/VkiPmAqDakVFaaCxtgwOlsVkzwJmJnZmOyJWDrxNp60kIQBEMfvzlwPzcLTJnVV6UiK2AiGwhIFIYy/eGPmg4xjo13lEq7niqWJovCpGRuIlwRsZpS3xv89iG6IIZPHyYyIS8SKSnd2jLFEUXTI5VlfrxJxnM76TeQAmKi5UVuKP33dn5gUzYC9qnu+//354PB48/vjjWFpawvnz5/Hss88qBrwzMzNxHZxvfetbiEQieNe73hW3ni984Qv44he/CAD4r//1v8Lv9+Ohhx7C+vo67rjjDjz77LOHvjKHiENJPGP2ShkjihmfVUxA8oe749faIIrak3iRSATd3d2IRqNp+565Qr6HCyVj3DHPGNnHRqfT4dq1a4phLwDFH+bcuXPweDy4desWeJ5XvEzcbnfc8tlidnYWIyMjOHnyJGpqahAWeDCEQKQUJi57MqOoZUryupD9/vh8PvT29sJRYceqsBGXrJTLNjORMTKsXA0YwqGivAIVFRWK6qCp14uZLYmMcazo4KuUCobMTOb25ErGDG9XgTIMQlEdViJjoKAwEBPeVvk7qDM2q7v6eUNPjAgJzZgXV3P63WXHaXyo7jdh5Ypzr+UDOdZ6v5IBu6GMSQeO41BRsXP9+v1+rK6uwuv1YmxsDEajEYIgYHNzEyaTKed+1Gsl1ho4QGTM6uoqurq64Ha7cerUqbQntVRlSouLi+jv70dLfS1wc7Lo688WYUGEeZsiaN0ZsOlKZPAaWA/hX79jRbjhOFYbzLDPLeOyuxxctQnrXBQmkUO5SQen1QKLTg+OMNgYXsVmMKC5PtscD8pJoz1R5bWSO3L8XaGRwJBUKvI6BFGUfFBEicBgoiLERHNXEWD90biIbABx7SAaU3eKgSzHICoWTQeRGrKSmwIsQyAKybHTMjiOwbGmCoQ8fsx3LUBdECQCiLj0EA1s6mNNCIj61lQtlkg8EKjUMomHIWH1bXkQMW67CbUuO6YX1xChQtIe+71BabPqePECodsSwWZ4NPFGZue+SDwmWbSDRAGaoZKS8BQkqlHexxBQUQRhCBhCIACwWI2gvvgZrZIqY4oge2ux2fHNj9xblObsVc3zww8/nLIs6fnnn4/7e2pqKuP6CCH4wz/8Q/zhH/5hzm05xMFFPmVKrx4D38zLEBDUu5w40lahmXK2tbWFzs5O2O12XLx4EVyeqWxakAdSxRhUyUa9iT42iUa95eXlcLvdOH78OLa3t+HxeDA3N4dbt27BbrfD7XajoqJCUdSlgiiKGBkZwdLSEi5dugSn0wkAMLAcqk1WLAS2YMxBGWNU0pSKV6ZkZLMjmhcWFjA4OIgTJ05gODqHyfVZWLncntkGVk4cze5+s3E1IKqyYFl1cLa6CS9szYIjDO46egH/a/N5bJNNbHq20LfeB7fbjfLyck3iTIjmEslOMB91ISiwcBv9ECHCwthhYg2o1NfCwmYfCZ4KOmJARGjBeCB7VkcPDvfZ34S31r4euhzIvFJgPycpAcUz8C0GCCGKErexsVGJfh8YGMDk5CRGRkbgcDhQXl6uqGYy3SsyGbNfybBi4kCQMZRSDA8Po7W1FQ0NDRlPTLE7E/JLZ25uDufOnQNrtgL4RdHWnys4hoFAVYoSESBp5LXFgG6RAeopBALMde4YS1afrcPsaABeBOAFAF6ELpA8sFWg6hxRjuRVogQgu8GaehmRFkD8SGBZBoIgSuVJAgVhCEg0Zv4aU8eowbAEJJKs/Ek3kCXYqVPlxTSmKTkgYwJP7EuGpjYTLHOY0OR2YH5wCeO/nEr6XmSAkNsIpCpbU4GJpiDGtFQgma4PApj1Oni3tcm/VL85WV+JkWkPVtdUPkuxbRFR8lfyzG0g7IoRS8UgYyiFZVbDtCABvG3nsZysjMnMVLA8BZ+OjKFUia0GkrkPIkq1au3HqzEws4J1fzDpfJXWx6gw4tStN+F/fPI3i/YCL4SMeS3VPB/i1YFXU7Q1zbJm9a1vOKepCFpeXkZvby9aWlpw9OjRog8K1GRMIZA9FNvb29HY2Ki0k1Ka0qhXXXJw5MgRhMNhJQFoamoKOp1O8TJxuVxxz0C5FCoUCuHq1atJJp7NNicWAls5KWPkMp9iRFsbWD1AAXsGVQWlFBMTE5iZmcH58+dRXl4O27ykQLHlGJ8sK3uYLGWj6kQlNWQT39OOGjTX1aNebMKQvw/1FY0whU2Ynp7GwMAA7Ha7omiyWq3w+XxYXlwHsggD80atEHgXgqIeTkMILCOijHNDpFFs8D5UGurBggUDFiLyG0txRA9eOILRHIiYJn0tfoO9A3SFx88nfw673a4M3jORg6WArIzZr9hrZUw6cBynlHJevXoVkUgEPp8PPp8PExMT0Ov1SjlTWVmZJsl9qIzZZyCE4Pr161m/WIsps41EIujp6UE4HMb169dhsVgQ3av65xjOuSvQt76TdENiJSalBMMD+o3k8hJW/aASReiCaYgYSEoHCqk0oqABbuz3NPGzVIsLImiB6iHCMghFopL0ORY9RFTKFfXAmRLAxLEIRTXIGLWCJsUxsFqNWBNSx5DngqMN5RhbSP1ClNtd6bTAt70Rtx8t9S5YKMFEzwKGRrRlprwh5g+T5UuLSVEGpU08ZAAFWprK0ZdBFaPjGETlDi8FhjRMfhUyhgI6v5RSJhiL9yLm/CK4LE6pUn6oalPKvzVAoum/ZyKxajG1Ga9qvXodiwilIKxEQnnW/EnrKCkZU8Bzwcro8N1P/hYMRZy9LqQu+7VU83yI/QvZcy8bFNtzj1K6Z54xNAuSg9OxuPuuc4hE/QopQinF+Pg4JicncebMmYwpZvlCfi7kS8ZQSjE6OqqQCWoPRbUiJhujXoPBgLq6OtTV1UEURaytrcHj8WBoaAiRSERR1NhsNty6dQsGgwGXL1/W9FVptjnxy+XZPD1jiuWBwaDSmFrdIYoiBgcHsbq6io6ODsXXy8ZZQUBgybE8Rm4/m7UyphpacQZyvPXl8gYAQJWhFkP+PrjM5WhtaEVraytCoZBCnE1OToIQAp7n4WytRKZiIJ4yGAtXA4IdFi4MAytgO2KCk+OxJayhxtCsEGIW1oYtYT27A6ACBx1E4ShGsiRiCCX4jao34R1Vv6YYOIdCIWXwPj09DY7jFGIm1eC92Njvypj9TMYAUMbhLMvCbDbDbDajoaEBgiBgfX0dPp8PY2NjCIVCcaoZuc8k959yxTe+8Q187Wtfw9LSEs6dO4evf/3ruHLlSsrln3rqKXzrW9/CzMwM3G433vWud+GJJ55QSrgFQcAXv/hFfOc738HS0hJqa2vxwQ9+EJ///OeL1rc7EGQMkFtnoljKmM3NTXR2dsLhcODChQvKza/jWBh1HELR3Z/tYQlBqHsVjF498qcQd6GcxegTwZviL7ygf2d0yYbEzEoXueqEy7ayNgUIkWIr090I4s6on/CQyqMKeHARAmxth+IVNhrHnYLCZjbAv5V/hzYQiUh8T64HScMvJKOfUGwXTPqdTtXp1mr4FzaxeHM+7U8jVp2k5MjlgZRKFKNRkiNfT1IZ1c75lhcts5vSEjHVLiuqnTbcml5OuUxSs6hEGAXcacqtcgWlsM5mZmJ4A5PWLDgbJVlSWZz69wIFIz+21MdWhda2KtwaWUIwEoUUFqZxje9DMsYABv/zo++Ew1JcD5RCpMCvpZmdQ7w6UGxl8cLcQuaFSoRsuoy3vb4ddocJq6tBiKIInufR19eHzc1NXLt2LWfz7VxQiDImsZ2JRr2CIGRNxGi1Sx4YHTt2DH6/Hx6PB7Ozs9je3oZOp0NFRQWCwSA4jktaf5PNCQB5ecZwRfCMAQAGLOpN2lHR0WgUvb29iEajuHz5cpxvlo21wMQac0510sfazZBslTHVoBqqk3qzE3qGxRWXlNRVZZCMey3szvk1Go2or69HXV0dJicnMTk5iYqKCqxtRYEMrz9P1A4CBuHwVRhMP4U/qsew5wTuO9uIn/n+N2oNzcqyFs6RMxnDEg6EtmM44Mlq+XLOiau+dvzWubviPjcajXHkoDx4Hx8fRzAYhNPpzKnkJR8cBGXMbpBS+UJWFSceQ5ZllXMHSAri1dVV+Hw+TE5O4q/+6q8QjUbhdrtz9uf6/ve/j0ceeQRPP/00rl69iqeeegp33303hoeHUVmZnJb3ve99D48++iieeeYZ3HbbbRgZGcEHP/hBEELw5JNPApACEr71rW/hf/7P/4lTp07hxo0b+NCHPgSHw4Hf/d3fzfPoxGP/nsUE5HJDFENmmy46GwDsJsOekDFnKyqw9MspsE07LzkiAvwuzDwZVwVs18dfMp5lyUuCiYhgskn9oUgdbZwrcvKWiG24kEEkAVa9fiCmsKFITgeilMJk0qcnYjJcy3oDh5AoSIRTrm3U+IGYZnAukUkULMPAwnFgGYnkGvtFek8kEUCk3ABRz+ZOGKn3n5CYoW8Kci5F0+VlA5FkGQgD4FhjBUSBYmzWixXPdsyfKEO7VOQEzwGCQbX9At/HTIiCC2W+8qP2+EYmJkxlc0GkvA8pBRvZ2RXlNCQszsTIoLXNYMrt0RIa+OdTushSgqc/dC/qyguvc1dDFEVQSg+VMYd4zaBYZAylFGNjYxgd2rvAA5rFJNVdbz8LQCIgBEHAyy+/DJ1Oh+vXr+dlaJsr8om3DgaD6OrqAsuySe2U1TD5EjGJkL0gtre3EQwGcfToURiNRni9Xty8eRMsyyrlTOXl5WBZFs0xMiYfz5hipCkBAAcDmm1lSZ+HQiF0dXXBYJCiqxMHszbOAiubv2lstmSMha3QVMawhMExWyXOlkkkTKVeKmeycPEGvrJ9w/LyMi5fvgy73Y6FwAp6N/4+5TYpBVZ4O95W8ev4P9M2DPgqseS3w8EacZvzHnRtvIAaY7Oqjbm9T1lw4HAc/dsaymMNXLWfxbucb8HgykDa5RiGgcvlgsvlQltbW9zgXS55ka8/rXjlfHEQlCe78YzKF9lOZMmqmfr6egiCgHA4jH/8x3/Es88+i4WFBbz+9a/HPffcg7e+9a04d+5c2nU++eST+MhHPqIkTz799NP44Q9/iGeeeQaPPvpo0vK//OUvcfvtt+N973sfAKC5uRnvfe978fLLL8ctc9999+Ftb3ubssxf//Vf45VXXsnpeKTDgSFjckEhnQlRFDE8PIz5+fmU0dkAYDcasLKZLN8vJQgBhAEptYaJqJUxQCRcejKGjQL6zfiXx9ZWCGa7DtHNaHY2LpSC6gpUxSjIwMaofWgpNFUsuSAYjCBKpG3KlR3qMiVKKVgdi3AggwIiMTonAYIgJkVCZw15fapBvM+7BW4jEkccUYZI26CAdXwdADA34AU9UpZxwC8yQKjCVLAHT1KbtT7P0JZQhFeOlc2sx9Hqcix6NzE8md2sjBpqEiDkYKRas2LsIgXM89mppARjQidCw9Q4E0iKRwHDx5OHVP2POLEXBcsSrG0EU25P3L00xYwgIvDhc43QhTawtsbB4XAUrQOlltnmg0AgcKiMOcSeI1dlcaGTWbLywO/349LFDgDfK2h9+SJTmVJVjQNnLjYBkIjTSCSC6upqHD9+fNcGYXK8dbZYX19HV1cXKioqcPLkyTijXlkRI6+3GCQwpRSTk5OYmprCmTNnlD5xbW2toljweDwYGRlBOByGy+WC3ip5yJhymLU3sMUz8AUAIzHjiC0+fnxra0sJAkl1jq2cJWfzXjW4jLM+EghhQFLMDv5mw1lFaVNpqAEBgVmljBFFEf39/dja2sLly5cV9QDHpG/3lmhEFRtF9fZ1ULEXExtuAEC5joGO0eOuivfAye2oiaw5kDEMWOiZ4+jdzEzEmBkjHqj9DVyxncHS0hIYhkEkElGuWUJI2vsvcfAuxysPDw8jEomgrKxMUV4k+hnlgsMypcKQj98ey7K4++67cffdd+MLX/gCFhYW8OY3vxk//vGP8eUvfxkWiwX9/f1wu91Jv41EIrh58yY++9nPKp8xDIM777wTL774oub2brvtNnznO9/BK6+8gitXrmBiYgI/+tGP8MADD8Qt8+1vfxsjIyNob29HT08Pfv7znyvKmWLgVUnG5OsZI8cXRiIRxR8mFeym3Y8DPVNRAc8vpwEAbDjeqyQUKo6/SCYY1pOkIBDWI1mPWYtZTZXJmJYRqaKGIJSCEbXmIbLH9OxqMkmi3iGOgEYyX3eZxvhRdamR1oI0nogDVOU8PAWNfSj/dG09CBh0mseKDcQrSzJZ+eTqD5MttNQQJJZUpQW7RSepYihQ5TCh3GbB5MIaekcXs9iYhmktTxXxVNQOUEORiBhIZIFhK/OVJ+iJFJWu/m0enjGayhhKwaR4RJCEgxGK8nDYTfBspSZj9k20NQV+767ruN7iVpz7BUFQZtHKy8sLim4ulIzJt+b5EIfYKxTquScPeC0WC65fvw7/Wg7m6kVGJpLjLW8/C4YhmJmZwdDQEAghOHny5C61TkIuyhg50bOtrQ1NTU0pjXqLFccriiJu3bqF1dVVXL58OalkS61YaG9vRyAQgMfjwYrHAxYEU6NjGAtRVFRUwG63p21TscuUrIwFbfYdMsbr9SpmzM3NzSnbYuOssLG5xVqrwRZhcHxPzQnl3wbGCAdXppQpRaNR9PT0QBAEXL58OU4ZkSna2kB42MQrmJ+fx/LaTnk3Q6Vr6KS1A6IqHMTCObJqLwMWJuYkujczl4Mfs7Tg483vRYW+DHNzc5iYmFDSv2SfIwAKIZOJmFErsyilCAQC8Pl8WFlZwejoKEwmk0LMOJ3OnMiLg1Cm9GojY9TY3t5GTU0NHnroITz00EOIRCL41a9+pUnEANI9LggCqqqq4j6vqqrC0NCQ5m/e9773wev14o477gClFDzP42Mf+xh+//d/X1nm0UcfxebmJo4fP66IPb70pS/h/e9/f977logDQ8bkWqaklmlmg42NDXR1dcHpdGYVX2g3ZciOLQGY4R0lDhtRkzEUgUxqjGK1IbGPJlLQHBgOamKKNcbNYmMJ/y6EiRFERLjkh4pcRiKyABvNnWnSIj8SPyNymVEKj4/49uS4/QSDYUpTM1xhuw6CJUd/mGzakGqTWkQEASwmHSIxEsxq0MGzGoRnNZj08yxWJW1foGD9IkQjg3AZSevZkg+Mkex4nYhd45mTj4GvxjiKDSe3Qdl/UVI7yX/7Q1FEhPRG3Hkrt4oJCjx02wW849opANILl1KK7e1t+Hw+LC0tYWRkBGazWemM5aqaKVTq7/f747wcDnGI/Y5ClMVLS0vo6+tDc3MzWltbJWPRPYq1BtJ7xjAMwZvvPoWBgQEsLy/jzJkz6OnpAaV0Vwdg2ZAxcsnX9PR0wUa92UIOrxBFEVevXoXBkL7PSwiBxWKBxWJBc3MzGj3DaKypRTAYRGdnJxiGUWKzXS5XUh/bUOQypQZTBWx6iYifm5vD8PAwTp48iZoa7RQjGTbWkrN5rwwCArYIMxWGhPKuSkMtzKxVKbEyGo24cOFC0kA3kzKGgsNbmj8N7ogez/as41eLkt1vJBTGv//7v8elZ+l0uqyUMQQMLMxJdGYgYlgw+M2au/AbVW8CQxhMTU1hcnISFy5cQFmZVE4mlwXLfkfq5xDDMMq7O9U7XH0NyvHKcjnTrVu3lIkauT+Q6Zre72THQWhfIWSM3++P83nR6/W4/fbbi9E0Bc8//zy+/OUv45vf/CauXr2KsbExfOpTn8If/dEf4bHHHgMA/OAHP8B3v/tdfO9738OpU6fQ3d2NT3/606itrcUHPvCBorTjwJAxuUA++dnWq83Pz+PWrVs4evQoWlpasnqZ2XaZjDld4cbyizPK32oyBiIQ8Bcv/SBriGKSZ0omCHq2eGRMhhURgYKqrnBSQGIBNKPDJSmFyAAcT7MZJ2uuQmlfIgsTizHOhWCR06rSbk+1K0wSGZN8WEUAEXfMH6YUEAGtMuskvxRIzT/eUom+MWlWxx+K5tf5lI+1SKELiOCNBKFyUnSiqcXlwNZz2RlYCqbMxzcrA98E+RkRqHbpUgpyz7cRQCSUukyhiOK2gtb/ztPt+A+/dinuM3Vca3NzM6LRqNIZy0c1U+jMzmGZ0iH2A0rtuadO9Tl79mzczCS/B956SruE1O/8C1eaMTE1BJ7ncf36deU+L3QAkSsykTGCIKC3txebm5u4evVqnDqlUKPeVPD7/ejq6oLNZsPp06fzOh7NtjLUV1XjZFkFRFHExsYGPB4PxsbGEAgE4HK5FHLGZDJBz+jAgClamdJFV5OSijU7O4uLFy8qg/50sHIW2Ln8CHSC4il71Kg11EMIiLjZ3QmXy6UoSRLBZlDGOPR3gZNTn1TpomU2O86cOAOv14vx8XH09fXB6XQi6Eo/yUtAYGNP4+ZG+jTLaoMbn2h6H45aGkApxcjICBYXF3Hp0iXY7TuEj7xP6ntRvr5l1QKQvWqG4zhUVlaisrIybqJmcXERw8PDsFgsCjFjt9uT1nUQypR281mVKwoJPwBy7z+53W6wLIvl5XhicHl5OWUi3mOPPYYHHngAH/7whwEAZ86cgd/vx0MPPYTPfe5zYBgG/+W//Bc8+uijeM973qMsMz09jSeeeOKQjEkHNRmjFbsnQxRFDA0NYXFxERcuXEgpfdKC3bi7ZIx+MhT3NxOhSroMoQDPizBaDQgFM+TaFguiCCLkVskhcEWr/MgOdId7kEiK/LZORAqqSiSiRDUoJgAr0NySxQnRHHHqOBZR1SwiEWnuKo0MfFNyeU7yKJ2oliu6P4wGiEBBU3BdcX/GuJKbg/OgoLk9vBLWTwkAkYINiAjbGEStxSdiAMC2GMVWFssJuuQSJUCrTCnzhUbUhs2UaqpiEqG+psMhPsPyGbyadgF3NNTj9975+ozL6XQ6VFVV5a2aKbQzcaiMOcRBQ67KGLlsIhAIJKX6AICwp8qY1M/L+iMcDAYDLl26BI7jlIHebg9wCCEpyZhQKITOzs5dMeqV4fP50Nvbi/r6ekXdlA+abU4l2pphGJSVlaGsrCyunEn2mrFYLHC73TAxxetXn7TXob+/H+vr67h8+XLWz2GGMKgyaHtFZgIBKRqZpIZbrMHNmzdRX1+Po0ePpjwn6cqUIqIOb3B9UvmbUSl49AwXV24WDAal6OzVWSDlKgkc7Bn8KgMR88byK3ig7j4YWX1SnHimgbZaDaO+3lOpZtK9q7UmauTo7L6+PlBK41Qzer1+35cpFdo/KTUKnczy+/05pdnp9XpcunQJzz33HN7xjncAkK6b5557Dg8//LDmbwKBQNIxlNssvz9SLZNPCl4qHBgyJpcbQr4p03UowuEwuru7lVmRXOOzdrNM6URFORZfnI37jEAiZEQDUUgHq1m/K2SMCIAVsx+OUUrBW1jQIj/UNAfwKqhJBVBtpUXGbYgU0Kd42NHYALYQqYCsTiBAhBdixBGAiADRxOQ+5s3UFiZ+mURljHo1gpFBpKz4/jCJkEyQNbaRrkSnUHkGAZgwRbichajPYv/iLqbs0F7lwsKzs5kXBBDVKlECkg18M+13glqNiWanplGW0VBGJW1CUyW2ezjpKsfXHrw759/lo5opRs3zIRlziIOEXDxjEv1htCa/opE9VMakeOebrRxe/+YzaGvfIRsKiZkuBKkMfDc2NtDZ2Qm3241Tp06V3KgX2CnnOXHiBGprawtaV5PNkTLa2mw2o6mpCU1NTcpz2OPxgI0S/Nu//VtcOVO6CdVUiEajmB+YAkSKK1euZCxHSUSNITkCNxsQUnwyxuPxYHVwEydbT6GhoSHtsunKlAzc7TCpvHAY1TWjZ+LbbDKZ0NDQAF0lxUsz/5C8MgqYxXb8ajs1EWNlzfhw47tw2XkGgDQw7+/vh9/vT4oTzwaJxIy6PC8f1YxOp0N1dTWqq6tBKcXm5iZ8Ph/m5uYwNDQEm80GnU6nrH8/kjL7vUypGGRMrsriRx55BB/4wAfQ0dGBK1eu4KmnnoLf71fSlR588EHU1dXhiSeeAADce++9ePLJJ3HhwgWlTOmxxx7Dvffeq7T93nvvxZe+9CU0Njbi1KlT6OrqwpNPPon/8B/+Q977logDQ8bkinRSW7U/jDwrkit2s0zJMhvFusbnbIRCNOwYcJpMegClT3iiDM1qgEcphWBkwNt0gJgmcjdvZHg4qvtUeW6aEKQkkQgQl6aULyiAyjILlta2lePK+iMQYibRuXAAuSgggGTPGBkRhw6iufj+MFqw243YCMbLYSkomISmmfQcQkWIXAWkazhUzpTU+8S8HMVqlsvyWiVKVEPLleFyI+pHnkjBZMnNEjn2PZtxSKnf/WlOSZ3Ziv/fx+4rSscoG9WMyWRSOn35dHoSa54PcYi9QCk897T8YbSwl8oYUdB+YL7hzhNoP9YW95k8gCtGrHcu0CpTko16W1tb48xmS2XUK5eZLSws4MKFC3C5XJl/lAHNNmdW0dbq53DF0M9wvu08PB4PJiYm0NfXh7KyMoWcyWbSVI79NplMOHv2bF6DwbzJGBDoikjGzM3NYWRkBOdPXUgyJdUCSwwgYEERfw3zlMHrXb8b95n6ztYx2m1OFW1tFtrRE1pL2Y7TtjZ8rOl+lOkkA2Ce5+NMh/Mh2NTQKmcqVDXjcDjgcDhw5MgRhMNhrK6uYnZ2Fn6/Hz//+c8VxUy+BGEp8FogY3KdzLr//vvh8Xjw+OOPY2lpCefPn8ezzz6r3D8zMzNxx+zzn/88CCH4/Oc/j/n5eVRUVCjki4yvf/3reOyxx/CJT3wCKysrqK2txUc/+lE8/vjjee9bIl7VZIzWS3Vubg6Dg4NJL7lc4dglMqbd7cL8/53T/I6NUEQByUSXITAYduEBIYqxkp30HRaBA6I2PWhMVcIGeYAr8kMjkzImYbYpZ2WMKGqWCSmfpOjo5QJJtUPh3VAlTlAKKp9LQpBLgVXGfVQNuHWrITAp0p8EM7drMwGV1U5sTOzEIYpE8jhJNIt2WI0IbRRGNlIAlIN0Xku4fy1uJ+b+dT6rZQUOcd5GCvK4vBhhxw2ZzWAcHEfKidJlkc0dmkmRVihSrb+MM+A7D/9WUdIqEpFKNTM3N4dQKIQXXnghLi4z21m9Q8+YQxw0ZPLckz0fZmdnce7cuYxkI7+HyphUZZ3v+O1rmp/nkmxULKi3KXucTE5OJh3bUvnDCIKAvr4+RbFQrOdVs82ZU7Q1AFg4E5xOJ5xOJ9ra2hAMBuHxeOD1ejE6Ogqz2awQM1plpRsbG+ju7kZVVRWOHTuW9zGy67IvjVCDIQS6InjGUEoxMTGBmZmZOIPbbMASM3iaUBxNLsCuiydziMqoL1EZI0MiY+LNDMt1Z/FSCkUMSxm8gb2Ie8yvgzVW3xSJRNDV1QWO47IKR8kHWqoZmbTMRzVjMBhQU1ODSCSCzc1N1NfXw+fzYWpqCrdu3YLD4VD6AhaLZc9UM/vdM6aQ9snJWPk8jx5++OGUZUnPP/983N8cx+ELX/gCvvCFL6Rcn81mw1NPPYWnnnoq57Zki1ctGZMotS3EH0YLu+UZ41wWUnpOMDETX0IByhDodCVmSGM+MWBTD9xEBohaOYjqmX5BLI3nSMz3I+WgWpaUyCVKOQxuKaUAS8CERYhmRv0FGEIKq5JJ+HGF04RlfwiMPIjeDEG0m/JT86T5DaXSQJcCMM1sQuePl00I6nKsXXy3sGTHpFgEBeGRpIoB4mW1cVBHgauXj4gQVftEAYh67EoSkGsTWXnFALESJY1904z7zqSMiT3ySFRl2itQ7fsv1xIo+WelnojRWL+ZcPjOJ38LJv3uzEjJs7U8z4NlWRw9ejSvhKZca54PcYi9RjrPvUgkgt7eXgSDQU1/GC3saZqSBrFy+nwDahu0lR97ScbIpMj6+jquXbu2K0a9oVAI3d3d4DgOV65cKeqMf6Up9/JMKxuvfDGZTGhsbIxLxvF4POjt7YUoikr6j9vtxvr6Ovr6+nD06FE0NjbuyQCZQeFkjDxW8Xq9OXndyOASyBiRElxxfUqzrTL0KdrMEAYmxoKguA0AqDd04Jdr2kRMnbEKD7ruhXGLxczMDG7dugWbzYZgMAibzYbz58/vCnGQygRYVrjmopqRyQTZ76i1tRWhUEjxmpmcnIROp1P6AmVlZSUhm1Lh1a6M2d7efs30nw4MGZPrg1WtjCnUH0YLu1GmdMTlxOwPUyexsOHY6IkClE0d91YsENknhhBQHQDVWJ5C8oXhNaKP2ZAIsCVoGyHa8T/y1yoyJtbI7FdNKSjLgolGIcZuExIV0VRuw9T6ljT4L9K7fnMzGFMNSUlI+uVthOwmVWOybHsmwolI/9Oth5KImIjdgHCtBVwo1hHdRY9WhpEYIpHQtB4nTCoSJcXybJhC1ElkHWVi/koFdNDsRhab4cwDi3qXHdM/zU4VAwC8OYeXVSbhEy+dNja8U+PMRCnEbMiYbJtQamVMwt86yuCZj9yHclvhz+1cIXcmMnnNpFLN5FPzDADf+MY38LWvfQ1LS0s4d+4cvv71r+PKlSuayw4MDODxxx/HzZs3MT09jT/7sz/Dpz/96bhlvvjFL+IP/uAP4j47duwYhoaGcm7bIQ4eiuG5t7W1hc7OTthsNly/fj3rQYegYRK/W+A1tn3X28+lXD6T12ApwDAMwuEwXnnlFRBCcP369TiPk1IZ9W5ubqK7uxvl5eUp03l2GxbWlPK7xGSczc1NeDweTE9Po7+/HwBQU1NT8ERrISCEKSiaWybkAoEArly5krOvCgBwjDmu3DiKY6g0Hk1aTt2dSozSVkPPmBEUt1Gjv4APN/4nTAX+X0wG49X6d7lvx3vr3gY9owOqgNbWVqyurqKnpwccx2FjYwO/+MUvFOKsvLx81xQdqVQz6vtKXk4u+1P7MyXeb0ajEXV1dairq4MgCNjY2IDP58PY2BhCoRCcTqeyjyaTqaSk4EEw8FWbjueKfPtPBxEHhozJFbJnzPr6Orq6uuByufKO6NPCbhj4VqwRzKT5Xo63JiKkVKVSDpIEURklUQAiy4CNxh5sJhZRK6c9+y6KJfYdyZI1UAiZNEoaeRGRAjGVERFoLHWHR0uNE0uz64Atdg0R+T8UeoMOej0Hg4GDXs+B4xhwHAuOI2AIA5aRzo8oiuhaXo/bXijOw0XIOS58p+HZefmYFuNLfYI1FvAOQ8qUp1KDIZAURxnKatQzOXElNoJERiaCRCmoQCEaCURj4S8sm8WEzfB2xuVqwhwmszyOIgvNFCUAmuciszJmRy0nHy6Gp5pWMPJWcz7lJX73q5U3DAX+/HfuQXNV9jLtYkJrZieT14zBYMDf/u3f4td+7dcQCARyntn8/ve/j0ceeQRPP/00rl69iqeeegp33303hoeHNUtCAoEAjhw5gne/+934zGc+k3K9p06dwk9+8hPl792cwTvEwUKi557sYdLS0pI2zUULe2ngG43ETzpYrAbc9sb2lMsXOyEjGwiCgMnJSVRWVuL06dO7YtS7srKC/v5+HDlyBE1NTfvGnNTKZUe4yx4fdrsdoigiGAyirq4Ofr8fL730EgwGAyoqKlBRUQGn07lrA1YGJKXKJBOi0Si6urpACCnIV0Udb00pcN75Cc3l1Oc8VZmSBA6V+jP4aNOnAQC1xgqFjHFwVjzUeD/OO47H/WJjYwO9vb1oaGjA0aNHQSnF2tqakp4VDocVLyC3212UCfJskCk6W773ZUImE9nBsqxi+t/W1oZAIKCoZsbHx2EwGJRJGqfTWVQCqhAvu91CIcoYuUzptRKA8KrtjXEcB5/Ph8HBQbS1tRX9hVPqMqVGpx0zz6afXVfIGCpFA/Ol6kTEElqUo0ekAaSgI4jadKBpyqPYkHYJSbFACJNmMJngGQNkJGNomAcXESSGQKSghEC/5IeOZWAUgboKG0ajIak6iiEwUSAS5hEN8ogis32yyACoTv1wMSxugiSeRwaZLHqyA4mpRLBzLv0NVojW3Y1pT8TyzDq4YGZfnJRlSlq+AKIIkQV4KwOqK859r8+ic+S2mjD1C22PJy1EbalNkjWJlyzLlOITs2jSdU8T15/DISp5mZJMclLgj9/xZpxrqSnxBlMjU2dCy2tmamoKS0tL+MQnPoFAIIA/+7M/w9raGu65556MaRgA8OSTT+IjH/mI4v7/9NNP44c//CGeeeYZPProo0nLX758GZcvXwYAze9lcByH6urqjNs/xCFkZXGu/jBa2EsD38R79413nUrrrbfbZUpLS0tYW1tDeXk5zpw5E2fUq561L6ZR7/T0NCYmJnD69Ol9Zy5uTaOMSYSczrO1tYUrV64oM+iCIMDn88Hr9aKvrw+iKKK8vFwZ+BcyU58JDGEkdUiOkE2HzWYzzpw5U9CgXR1vHaKNaDJfStHWnevJmKbNryu7D5ccV5W/a42S98x5+wk81PjbcOji+7Orq6vo7u7G0aNH0dTUBEC6fmVSQh5ke71ehZyRvYDcbvfukmdpVDOCICAUCkGn04Hn+YwmwICUEmY2m9HQ0ABBELC2tgafz4ehoSFEo9E4Ba3JlP21rgXZxHs/kzGFeMZEIhHwPH9YprTfkMuLSBRFbG9vIxwO4+LFiygvLy96e+ym3OWDuaB2W4fpTCUJqjIlEIIIX4JORMwnJu7oU+l/EVcWL7USKy1qau1YXNzU3ozWhwJSzuwzQR5MVACnKuERzDoQSiFGBIwPSbWyYr1VIjUoEA7lFiVONcq1SKy6iIR46FaDEBWjY5pyN7RXnmZhSsEEohANBlCOAeFF8CYWvFVX8nCclKAUhKdY9way4gK0ypQIlfxlErv7FECgliuqPwybRalduZ+HL4eELd6S5kWlScakX7dWYhkj0Fgeffy6s61+S2pCKS8YSqVzRoFHfu0a3nzmSAk3lhm5diZ0Oh3a2trwve99DzzP49y5czh9+jT+1//6X/jEJz6B48eP4yMf+Qg+9ankGn5A6oDcvHkTn/3sZ5XPGIbBnXfeiRdffLGgfRkdHUVtbS2MRiOuX7+OJ554Ao2NjQWt8xAHA7kO5DmOQzgcxo0bNxAKhbL2h9HC3hr4xv9519vPpl18t8qUZIPWiYkJOJ1OlJWVJSUmiaKY0Wg0F4iiiMHBQXi9XnR0dMBu107K2UtYslTGRCIRdHd3AwCuXLkSR7CwLBtXzrS1tQWPx4PZ2VnFfFU2AS62+SpLGBhyJGPkePiKigocP3684Pao462P2/5jyuXUSmNjGpVkhzPe7LrZVIcP1r8Tb6m4LWnZlZUV9PX1pY1GJ4TAYrHAYrGgqakJPM8nkWculwsVFRUoLy/POZY8X6hVM7KR9traGs6dO5eXCTDLsgrB1N7eDr/fD5/Ph5WVFcWMOhvfuVSQidr9bOBbSBnV9rakQj9UxhxQyIZk0WgUdXV1JSFiAIBjGZj1OgQiuQ3Gs0Gdw4aZf8k8u87GZrzlkoQtfyDjb3KGypKFgkrlSEhTWqECExJK4xWjQroBchKBBIARRIgaSh7WHwXDSyVVIhNvIOsoM2PLt3NsE61ockIackC/JJmuESWlicT9Jztot0rnDUKwSZ0EUc+A4UUE620xBVDGnxcfoij5m+TAH8YpY9RlZyryg1IKUU8gGIv/gmIzEDsuqwkbL69lvT6RAcQ0qh2tZKx0ZUqEp0oClUzwAdIxZoQE3xj1ZnPs+5XUMyZGKD7QcQbvvu10CTeUHQRByLuch2VZbGxs4GMf+xguXryItbU1/OQnP1FmtLTg9XohCEJSjGlVVVVB/i5Xr17F//gf/wPHjh3D4uIi/uAP/gCve93r0N/f/5qZeTpEbhgcHITT6czJH0YLe2rgq7rXWo9V40hb+njg3ShTkhUda2truHr1KmZmZuLSlEph1BuNRtHT0wOe53H16tW8vEh2A+k8Y2QEAgHFuyiT9QAhBHa7HXa7HUePHkUoFILX64XX68XExAT0er1SzlRWVlYw8cUgN2WM7KvS1NSElpaWopxvWRkTECtwwn5n6rbGKWOyv78TS5JkzM/PY3h4GGfOnMlJccVxXFzZr5o8GxgYgN1uV0gNu91e8pI6WQ24tLSkGCgXIzrbarXCarUqBFSi75zL5VLImWwIKLXXzX5FIWVK29vbIITsWgnbXuNAkTGEkLQd2bW1NcWQzGazlfymtZsMJSFjGsMGTGUxu04owEQBxOxcgqFIcRsSK08CpYiaGPA2PcAQiWTJAoSnQBakTSEQUnWc5M8TN5+4OKVg/TwYdSoTw0hlLpBKkdyV9jgyRllnHrsmanqbiGAEYWd1VC4ryXHlKZQxTDAKozeIrTJp9kjUM+AFBoKJzd+fphDkQcQAKQRNKp8cCgreTLIiCvMBm+F50mqwYiLizXp9qVKUFORCjFEqRVmLysFQPoeocawLsQcq5btfBN7afgSfvOdq5mV3AcWseS4rK8O73/3uYjYva9xzzz3Kv8+ePYurV6+iqakJP/jBD/Af/2Pq2dNDvPawuLiI7e1tVFZW4sKFCwX3o/jo3ilj9Ca9UjqcSRUDlL5MKRwOo7OzM86oV95mqYiYQCCglMCcP39+X3tFZfKMWV9fR3d3N2pra9HW1pbzMTIajaivr0d9fT0EQcDq6iq8Xi9u3bqFaDQaV86UjyJDUsZkVwa1vLyM/v5+HD9+HHV1dTlvK3UbpGPYbHlf2uWIqoNpKDABampqCpOTkzh//jxcLu2ksmyQSJ5FIhGFPJuZmQHDMHEmwMW+limlGBwchM/nw+XLlxUiQKucSSZm8lHNJJpRb29vw+v1YmFhAcPDw7BYLMq1mIqAkgmh/eL3pIVC+k9yrPV+3r9iYv8+lXPEzMwMhoeHFX+YsbExhMPhkm7TZjRgaSOzoWcuqLRZMPNc9p4TbETcMeskRVQDqMqTRI5IKUlqZYDWDaLypSDREsVZJyAaTdNxkhxzETd6VJN5oghum0/mPGL7ybIMeArojMWLe6QaD2jKEbCBqNI2AkhxxHK7szyMmosJIkxTm7HtxF4mOhbhCpP0MiZU20anVKdOFOMSk+Szl834nmgpU+T1MBS8iZQ0tjrdy9VuMmDuF9knKAE5pijFkDJpKhKzBEoiG2OPBiH+h8ptkKsqBijdMRYoLrTU4Iv3v7k0688DhXQmwuFwzjXPbrcbLMtieXk57vPl5eWi+r04nU60t7djbGysaOs8xMGGKIoYGRnB3NwcHA4HysvLi9IR3ktljLO6DGsA9AYOr7/zZMblS0nGbG5uorOzEy6XC6dOnVKeK4QQCIJQEiJmbW0NPT09qKmpQXt7+74f2CRGW6uxvLyMgYEBtLa2FqW8kmVZRRUjD4g9Hg/m5+cxODioKDIqKipgtVqzOnYSGZN5WDUzM4OxsTGcPXsWFRUVBe9LfBtMCIkOXHCkJ/5ZstOfMXH59XEppRgbG8P8/DwuXbpU9NI3vV6P2tpa1NbWQhRFrK+vw+v1Ynx8HH19fUpyUUVFBcxmc0HXtyiKGBgYwObmJi5fvpxSPVbM6Gwg3neupaUFkUhEUc309PQAgKKYcblcSklesZ8VpUChyphDMuYAQRRF3Lp1CysrK7h06ZLCyiamAZQCpUhUahXNmBQ8WS/PhHfMOUPFqs2mNM4nRlJQ0LiZdC3fCLUxLBMWY3HNpUUkU0ePIo6AUZQDogjdtvbxorHBZkW1HYvrAbBc4sNkRxqTZZbTDrQGsgKFaNKD0B37X8nENz61SW53OkNk9WCdUgrL1IZCdNAYOSaygGDilJhytTfNjjoHxSdkEogYABD10rXEhDL/XCtNiYhS2QxvKf21ltJAGMAxqwOTwdWs1yUSQNSnP8BZG/iKFAy/829pOekEyuQMSbhN8j61JXwvHq924+v/8W2l20AeKKQz4fdL93MuNc96vR6XLl3Cc889h3e84x0ApHfcc889h4cffjivdmhhe3sb4+PjeOCBB4q2zkPsX2Tq0EYiEfT09CAcDuP69esYGxsrmncK3eV0IjWMZVZgg+KONx2HJQujetm4uNhYXl5Gb28vjhw5giNHjsT5wxBCsLq6ipWVlaIRYACwsLCAwcFBHDt2DPX19UVZZ6lh4ZLLlCilmJmZwfj4eMlMh9UD4iNHjiAcDiuKjKmpKeh0OkWR4XK5Ur4TJDIm9XWmJi8uXrwIp9NZ9H2JUKDSeF/G6yiuTClNtHUqJKpISh1BzDCMklzU3t6OYDAIj8ejkDMGg0E5R2VlZTm9t0VRRG9vL4LBIDo6OnJSRaUyAdYy4c5GNaPX61FdXY3q6molwt3n82F2dhaDg4Ow2Wy7EptdDBRi4PtairUGDhgZk1imFAqF0NXVBUoprl+/HudOXaqXqhpmXXEPX7nFhNl/z14VA0iJSiRm4BvmBXCgyJxLkwHizhoooRCMLAgvxo8DU6gUKKhEeOyCKgYAQuH0ZWIkRsYo9AmlgCBCH+BTlmmQmA+N2WYA1gOp024gEQHZxEnL0CKxGF4ENXJx3ickIgDQmK1IEPrErzz+n8YlP9jITkdYJplEky5OorpzsktoGCOKmtHVoi5WVhTK3GHXOg1MFBCLJ1zKefsAYNax0M8HcfxETezlSyEIInhBhMCLiEYF8LyIaIRHJCogGuHTpigp0DgdREBSMhIbVtGDquo8ih3yMVEZI9XgZdhhrSaViPNqMFvx9Md+Y991LgrpTORb8/zII4/gAx/4ADo6OnDlyhU89dRT8Pv9SrrSgw8+iLq6OjzxxBMApIH0rVu3lH/Pz8+ju7sbVqsVra2tAID//J//M+699140NTVhYWEBX/jCF8CyLN773vfmtW+HOHhIVea9ubmJrq4u2O12XLhwARzHFbX/lDyZsXsgRgOwEcqqRAkovjJGbdR79uzZOC8oedAmz/qPjo6iv79fMS+tqKjIq1RGNh+dnZ3F+fPnS+abWAokKmMopRgeHsby8jIuXboEh8OxK+0wGAyoq6tDXV0dRFFUYpmHhoYQiUTiypnUCgqWMDCx2h0SeeJ4bW2tpOSFgWnCibJfy7ic+l2bqs2pIIoi+vr64Pf706pISgmTyYTGxkY0NjbGlZwNDg6mPUeJEAQBPT09iEaj6OjoyDtSHMheNSOno2WjmnE4HHA4HApJKEdnT09PK6bcsmpmv5UgFjqZdaiMOQBYW1tTHMhPnjyZdMJLTcZ4vV6EtjaKus52xoapiC+n37ARyROCMrEBuEmHYLAAhYzsExODYGClgV9iH44QqZQmkXShsTjrEhv3ygiHNcqMYu0AYoNRleMoAwKdPzURAwBOlwWb/gj0sfIkUdWBTRzDUo4B0pVKJTYr4cFCAVB97NpVbYcJ80qzqaxeEcUkhVIqcFth6NcTyvQYACKFYE142eTtRpwlhJgiBkgiEihLIGbp8aLM5MR8UGJ/5ESGFYJULwX3aAgbfAiexc2s1sMAEKozy3m19osAYPgdAopEaZzZtGL6q1IOAbFEJRk0NwJRjVKY95ZzBvzlp8zELLAAAQAASURBVH8Tuj0ctKVCIWkA+dY833///fB4PHj88cextLSE8+fP49lnn1UGcnLtvIyFhQVcuHBB+ftP//RP8ad/+qd4wxvegOeffx4AMDc3h/e+973w+XyoqKjAHXfcgZdeeqnoEvlDHCwsLCxgYGAgSbFRTGUxuwsK2VSIEAZ1DS6cPJudMqSYZIwgCBgYGMDq6iquXr0aV8Kh9ocxm804duyYkrji8XiwsLCAoaEh2O12hZjJ5lmijnuWzUcPEnSqEh9BEJQB/5UrVwqOAs4XDMMoZSLHjh1TztHi4iKGhoZgtVqVc8QSFkYNZQzP8+jt7UU4HC45edFmfWtW7xy10tjMZR/3zfM8enp6IAgCOjo6ShoVni20Ss68Xq9yjiwWi1LO5HA4lOPD8zy6uroAAJcuXSo6mZEuOlutmpEVM5lUMwaDQSnb8vl8GBwchE6nw+TkJAYGBpTy0vLy8j0nMmQCKt/+06EyZp+DUorZ2VkMDw/j2LFjaGho0LzgOI4rCRlDKcX09DRGR0dRV+nGr5aKQ8g4TUbMv5CbKgbYUcZQIhEyZoM+fzImIcaaUqrtaxFbgIhUKX3ZgZYBSQmRalBJk7+nBElkgBY21gPgdCzYmPKJV19HCYeDsgTIwcOZJnZMGSgqI/UAmQmrr11Z3YBkA+K4lUsROiQqwDQX72UkMgAIkRQSiYSQhrqnaGdQTcSo2ynLtFkCymZXFUUgzewKBgJ9JHZMaGkFPfHbT4ZpW0DAF0JlZfadXpEAgiGLF1Qqf5golVKYYqa9cW0UE/9WXTuq9e6XMiWrSPC9/+fdMBn2vkOnhb2qeX744YdTliXJBIuM5ubmtMb2APA3f/M3ObfhEK9eqP1hzp8/n0TKydHWxQCzh9GrmyEBb3vn2azvwWJFW4fDYUW1fe3atbjBt9rwU+35oE5ckb0jPB4PPB4PJiYmlDKMVMk/4XAY3d3dYBgmKe75oCFxXwpRKxQTWudIbTAbMPmxLCxhha9EeXk5WJZFJBJBV1cXOI4rWHmRbRuzgVrcbs5SGaPel4sXL+47JQaQ7MESjUaVcyTHoZeXl6OsrAxzc3PQ6/U4d+5cySOiU6lm1IbAcvuzKWeilEKn06G1tRWtra0IBoOKOmhyclIprZP3dbcjsOXnaKH9p9cK9t+dlAaiKKK/vx8ejyfOH0YLpVDGyDMdco3kdM8YgPGirPuEwYGpHDwnZDBhCkKpRMYQAoMx/1NKxAR7Ej2TQuESW0oj8YnhaUlNVHMFoUBDYxkWN5clFU8WLypRpHBX2ZT9iER2rqNEA95kMio1okYWop4FoxIvUPVzKkEZk1zTQ9OKWOTFzRPryT81cJrnK+6HxQYfS01Sf5bAulBWIllEDmAzcIgbodBOWtJWQmrQLiBpU5TCNhaESJGTqiPqMmRRopR6x2R/mKSyL5EmkZAyCUNo7HstlVsOKGaZkp1wePiOeljN+zNmFSiOzPYQh9gPkMuUIpEIuru7EYlEcP36dc1r9NVQpsQwBOsBHm966+msf1OM/d7a2sLNmzdRVlYWF71MKVUGXZTSjOaber1eKZWRyzA8Hg/6+/shiiLKy8tRUVEBt9uNUCiE7u5ulJWV4eTJk/s67jYT/H4/urq64HA4cOrUqX29L4kGsz8euAUTo8fIyAjC4TAcDge2t7fhcDhw7ty5fbUvrKrePRsD31AohM7OTlitVpw+fXpf7Us66HQ61NTUoKamBpRSbGxsYGlpCcPDwxBFEQ6HAzMzM3C73VkbNRcD6VQz2ZgAJ6pOTCZT3PNifX0dPp8Po6OjCIVCKCsrU1QzuxEXXSgZ4/f7D5yyrxAcKDKmv78f29vbuO222zLK/Ipt4Cs/iORIQqPRCLupOIMIm1GPxRdyS2KRwUZobEBPAAbQ61Jc+BmMXyGISeoIwaRal8bzidDkcZ2W8mJPEGsYxzCYH/WAyZKIkWErsyhMdTC4I31JJF+yJWOiVp2UdqM+YDG1SmKbAYCEeSQedCaDelpJw9ZYTjTEYqx36dyQaLzKKvWC0n8EIwN2O80OUhHT65ugMaNeJUFMVJXmlBoJm7FNBiFSAogCuBxeOBFndj4AqY4dE6Uggsq0V4aKTCWqz5T1CTL5VQCKdPlYCYf/76O/jqnRoeKssEQolIwpNOHhEIcoJjY2NpSBbrqZ7WL2n7hUfZISw1XnQvv1oyhzZU+IMgyDaDQHqWsCVlZW0NPTo2nUq1WWkC0SyzA2Nzfh8XgwNTWF/v5+ANJsf0tLy4EZJGthbW0N3d3dqK+vR2tr64F6djIMA5POgOONbWg8WY2VlRUMDAyA4zj4fD68/PLLyjlMFVe8m1BvP5Myxu/3o7OzE+Xl5Thx4sSetz1fEEJgNBrh8/lQVVWFI0eOJKlJZJIznVFzsaFWzcikrVo1oxWdna4EiGVZhXgBpJJp2WtmbGwMRqNR+d7pdJZkPwVByKjuSQe5zPu1ggNFxhw7dgwsy2Z1cotZpiT701RWVsbNOtiNxUlTOm0uw9T2el6/ZSNS2Ybs5ZCy45NuvJrgEwNIZS2iQU3GZH74EiGmitlFtcLOxrU/FoO8RGLk+PLQmXQIxxQxfv+OXJsmKoWyUAFFTSxEjoFg5nZKwKChMkhUxsiQfySmry9hQzwARtMDJm1yT9IIfiflIWdQCsJTMCluPblSTN4WJdI/hTTto5RCNDCalxUB3bUyJbVbD+vnYVgTAYaAiBRMlr43IiTiKYuNpQQTlcqTErcYdw/LRr5qNZRYuGpNLEI/3wwWz3ziN2BkaZxHw34bRMgDqMOZnUO8GjA/P4/+/n4cPXoULS0taZ/vxVXG7M197ah2Zm3cKyPfMiVKKSYnJzE+Po4zZ87ExdCr01XkbRQC2dTTbrdDr9djdHQUVVVVCIfDePHFF2GxWPbVoD9bLC0tYWBg4EClPyWCIxxMjAGrq6uKF1NTUxN4nldKZTo7O8EwjGIuW15eviflPuqugIVLPZaRI9nr6+tx9OjRA3M9aSEQCODmzZtxpJLZbEZ9fX2cUfPw8DDC4TDKysqU87QbahIg3twXQJy/jFo1E4lElP5TpmeK2WyG2WxGQ0MDeJ7H2toafD4fhoaGEI1G4XK5FHKmWH5GhfSdAKlM6bXUfzpQZIzJZMp6tqZYnYnZ2VkMDQ2hvb0djY2NcQ8iWxGirc16HZZ/vpj37wkFmAgFLAAIAZfqphRpkt+J9Lm2giFxwBhnPJviWcxERVAdkxSjuytINB2ROz6CmJcaRKQUwZA0Q+bfDoFlCahApdIt9WYyGBVHzRzClUaQKJTyJCDW1oSBsVrhQRL3BzGPnhSDaRLmwQYFwMBI601IzxFMutTHgRBQQYhvTz7vWzFWlpRBwSNzRSKDnVSndM9sElP2aBnaitg18k9N+jhGAgATa7RI49Op0iDqMmRFiKQz2GUFgBWTBVBEowwt7lww2bYyDQocVxkpg//5qXehwm5Ef38/LBaL5qzPfiBmZJLosOb5EK8GbG1tafrDaKGYk1nMHpUpldW4cOFyS06/YVk2ZwNfuXze5/PhypUrcYk/akVMrmqYTNscHh7GysoKLl26pEQkR6NR+Hw+eDweZdAvEzO7OdOfCyilmJqawuTkJM6dOwe3273XTcobHMNiw7uOmeFJnDx5EjU1NQDiS2VEUcTGxgY8Hg/Gx8fR19cHl8ul+AHtllExQ3beseYUZUqrq6uK0qupqWlX2lUqbG9v4+bNm6ipqUFbW1vSvag2aqaUIhAIwOv1wuPxYGRkBGazWSFmnE7nrvVRtMqZgsEg5ufnUV5ennP/ieO4OJWd3++Hz+fD8vKysp/ycXA4HHnvZyHhB4A0mbVb6Wn7AQeKjMkF8ks1XzdnURQxNDSExcVFXLx4UTMe0FEEMuaszYXpjcmC1iGXr1AGIKkG6xSgGqVKiT4xgKQAEEwJl0YsiSfVgJ1EJdKDahABuwGjkYOz3ILFlU1ApND5k8t8coE/EFEUMZQCVpsRW+tBUF0CGZNmYB21cAhXmsBt86AJiiWq1SdKcdjkNCWpzEhjIZHCPLEB3mWKrTv+HIgcAW9Lb+bHCBSial+0yKC0yJKIUe+joHr/U6Jt4kupCN4mXYvKusX4i3a30pTkVC3LdBCMmj0SxZ2kpwyIOrM0VUy3TxSocFiwvOGP+zju2MfaKhM0IkeK4uVUSJqSgTL4Hw//FmrLbeju7oYoijh//rwyu5NtrfRuQW5LIWkAr6WZnUPsb5w4cSJrguXVoIw5cfkI2BxTHXNNU4pEIujs7IQoikr5ugx1qUExiZhoNIq+vj6Ew+GklCGdTofq6mpUV1dDFEWsr6/HzfSrfWbyic0uNuR+tsfjQUdHR1zi1EEDpRThQBhTsxO4eP5CykhxhmFQVlaGsrIytLe3IxAIKEbN8mBYHiyrk3+KDTlNiVJAzyQPBVdWVtDf34/jx4+jtra2JG3YLcjqnoaGhrjywVQghMBiscBisSjKJp/PB6/Xi76+PoiiqETQl5eX79q9xDAMQqEQenp64HQ60d7eHmcAnGv/SW1I3dTUhGg0irW1NXi9XgwMDEAQhDjVTC77WUiJNyCpmOrq6vL+/UHDq5aMkWV/+bBzsmM4z/O4fv16SnmavUAyxqjj4H1xqaB1yKBE8kQRUnUkKE0eSQmi5qBP0DPJAzdCJKPghM9kMGExloxDgOjuxdvYrHpsb0YQCvMSESNQ6LaiUpQvQ5AvIbPq88d5xZhlMobVJmNEEq98kYkYNiQkJShRDVWMtJL0BrvS8U/wrKEU5qkNSbAgn4+EF02g3pp5IJ6q1ieT11BsGSaaOylCVaU9BERD3EQhGFllf5T1q7ezmwa+lIIJCzB5+bhjIhEemRsiAuBN2b2cMh3LcrNJg4xRKauUz6T/Ri3FGRDla+CrEwn+4hPvREOlQ0kZSfSsUHcq0tVK7xY5UygZ81qreT7EqwfFjbbefTUGwzL4tffclvvvciBjtra20NnZCYfDgTNnzsQNPNRlBcUkYoLBILq6umA0GnH58uW05S0Mw8DlcsHlcsXFZs/Pz2NwcDDn2OxiQx33vJfR1cUApRQjIyOI+sO42nElJ1LJbDajqalJGQzLRs1y8o+6nKmYSUyyMkbrrM/Pz2N4eBinT59GZWVl0ba5F1hfX0dXVxdaWlrQ3Nyc1zo4jkNVVRWqqqoUzyav14vZ2VkMDAzAbrcr56mUpYGhUAg3btxQjLoJIXHpTIVGZ+t0OlRWVqKyshKUUmxtbcHn82FhYQFDQ0Ow2WwKMZNpPwslY2TPvdcKDhQZk6vhGSBdELk8wGQG1el0ZsydtxXoGXPO4caMb6KgdSiIHRpeSEXGIL5UiedBKKOhRKAQzCmOl0ABliglS4oqhBdjSUUAGAIq0pQKnWJAr2PR1lqJhZUNrPoCSuUE4UXoNqNFUUpsbYXi/jaaY4qGJJIq9l+OBaLS4C1q4xB2m6TyMZFIx0aFVAPapHYnHsKEkhwKwLASACfHYGt4vwgEqc9nXKMyL6IJUUxO9ckGlCbHfLMAVJOxlAGouixMg4whWiRjiSCIFI4h/055kgxRm9RMRLRMn706JcP6TBrPpXhlDJSoc0FHksrr8kYeq+FEgv/vo/ehudqpmKBfvHgx6UWtFf2oVSstL1tq1Yzcmci3Y/Vaq3k+xKsHxSxT2ovSmNvu64C7XluZkA7ZKoJko96WlpY4Hw21+SaQu1FvOqyvr6O7uxvV1dVob2/P6dmXGMkcDoeVEgw5NlsmZnajBENOf9LpdLsS91xKyGVqm5ubaK6tL0jdo9Pp4gb9cjnT5OQk+vv7FQ+TioqKggeqrDJ5F/+5XDJ2/vz5tIm1BwE+nw89PT1oa2tDQ0NDUdYpezY5HA4cPXoU4XBYUc3MzMyUzA8oGAzi5s2bcLlcmibKxY7OJoTAbrfDbrcrMe6rq6vKMSWExKlmEu/hYpAxr6X+04EiY3KBfKHlMruzuLiI/v7+JCf8VCiEjNGzLNZfWc7790mINTUSTd2RUKcfMWER0BigiTqSVIqz8/tEZQwAkYINiwDLKIIMjmMgpFJ5FACrRY/m5gpMz6+ifyRBUUQB3VZxiBgt6I3Sg4YyCe8uQiTvDpZAjAK8TYdIhRH6jShEhkkuawJSD2hTqVMIJG8fZQUSWH8EhtVQ0uJS/LYAgWMQqrUA2cjEU4mIMhg/50PEKJ4xCSSVyEAx/qWUQrDGP8jlcxt3jhMTqkqINe8WWDH55UJEqpgzpkM0yxSlbED5ZNI1yTOmyKoYIHfeixUJ/vxDb0d7XTk6OzvBsizOnz+f1Us6VfSjPOAptWqmUAO611rN8yH2N3KdzDrIZUpv//hdef0ukzJG9jcZGxvD6dOnFU8Q+btiGvWqsbi4iFu3bqGtrQ2NjY0Fr89gMGjGZsslGPKAv9hqDEAiqbu6ul4VMdw8z6Onpwc8z+PKlStY9P6iaOsmhMDpdMLpdKKtrQ3BYBAejwderxejo6MwmUxKyVk+BBqjcDE7ROLY2Bjm5+dx6dKlA10yBkC5nktdZmUwGOLizdfX1+H1ehU/IKfTGUeg5UPOysbDFRUVOHbsWFbryDU6W/53Kuj1+rgSyK2tLYWAkpV2MjFjtVrztgiRcUjGvIqQbYdClhjOzs7i3LlzWcvyOJaBxaCDP5x7FOI5lxtzy4V5xWghGNYmnyTPmJiPRIgHExEg6pNPv2BMPfjQ6zmEQMHpGPCiFGHtcpoAo4CNzRBOnq1H7+giWo5WYGx0pTg7BKDcZUFNjROjkyvoG1rQXIaAlnRQzurY2Ng24SHIIKYKIuCtEhFjXArCYDdhm0luEGWR2kg3zYCeJIhfIIgwzWwlLLRTpsQbWPiP5CCXTJGCpRVfLm+fieZIxNCEIquE57SsGKKUQjAxKY9TkgJkl7Dq9UNzaC1SiKkUafIiAHhz9gP7THHd21vhpM8SjwsRpRStVORqrkhLJGqAFYGnHrgHp5or0NnZCY7jcO7cubwIjlSzPvnWSmeDQg3oXms1z4d49aBQz724de1ytHXL2Uacuv1YXr9NR8aIooiBgQF4vd5dM+qllGJiYgIzMzM4e/ZsVubLuSJVbLZajSF/X2gpkc/nQ29vLxobG7Oa8NzPCIfD6OzshMFgUFT0NrZ0ZRUmkwmNjY1obGwEz/OaBJr8v2wINIKdMiVKKQYHB+Hz+XD58uUDX14rJ3OdPn0aVVVVu7bdxNJANYE2Pj4Og8GgnKOysrKs+kKBQAA3btxAVVUV2tvb87pnMqlmcp3YYhhGUx3k8/kwPT0NlmVhNBqVdeejDgoEAodkzH5FrhdhNlLbaDSK3t5e+P1+XLt2LeeTbzMaciZjWIbBdqc3p9+kAwWUgWs4kkoJREEoARVFsMGo8gBWH1NKKMQ0ZEz78Rr0Di+A07HgY6RPa6MbN29Mx7YgDSD1hjwvK0rjBuC11Q44yswYHl+BV0XCMEFeKnFRTEbSXBdFetczLAE1JJd1AQSI+cgINh2MCwGAYbSJGCD9YDbV+JsQhUiTy08sExtJq1Jiog0MQkesYAICREt2s1rJMdtp2pQPEZO0wZ32KmAlzQxloU0gpCCLdivbOmWssyhmJGN4Rw4lSkDGRDKPZwtI6BcnecaIFFFLEQdCBKmJxAQwIvC1996N863V6OzshF6vx9mzZ4tWspBu1iefWmktHNY8H+K1ikI89xKx254x9378rrwH+amirWUfQUEQds2oVxAE3Lp1C+vr6+jo6IDNZivKetNBXYLR2tqqDCZlc9lCYrMXFhYwODiIEydOHHhDWL/fj87OziR1j0O3OyQGx3Fx3h4ygTY9PY2BgQE4HA7lPKVSY6iVMfIY6PLly0WLNt4ryH43+yGZS02gqRVot27dQjQaRXl5uULOaB13v9+Pmzdvorq6WjMBKl8k9p8KLQdPVAdtbGxgYmICfr8fL7zwAhwOh1K2lY06SE55OiRjXiXIpIzZ3t5GZ2cnLBYLrl+/npcc02k2YmljO6ffnHdXYOEXRVTFqK7rYCoyhkr/xwaiO4uLNM7PhFeZpWquQpRkEg6HCUdcVoAAgWBk53u5jCTHBINEtDSVg9WzGJ3wYN6zGf+lECNsYtJnKkopQxRAxKEDiYpgQ4JS7lIshMIRCIbkTiUlUrw1AXDS7cDYqgcRe4rEHAZpVTHpHk875rUUxoVtMBplKvK6I24jKCGg+uw7wSkjs5HAgfCx1KSs15wGCSuhREpPEswpHkvy9ZWw67tSpkQp9BvR5EQxUVJk8WnKAwEgUpZlilJsW5lSqYKhKCoqbfBsBZTPkpQxiDdJLhzZRWwREfij33ozOo7V4ubNmzAajTh79mzJ5OjFrpWWcVjzfIhXE3bDc09zXbtIxthcVrzh/tyNe2VoRVvLkbh2uz2JUC6VUW8kElEMXK9cubJnyUfqwWS+sdlqdc/58+dTpgwdFGxsbKCrqwt1dXVobW2NO+cO3e4/7xMJtFAolKTGkMuZysrKdt6XcrQ1lTx8Ojo6oNfn0E/Zh5iZmcHY2Ni+9LtJVKBtb2/D6/VicXERQ0NDCtHpdrvhcDgUIqa2tjbpOismEokZAAWrZsrKyuB0OpU0Klk1MzExAb1er5QzpVMHvdY89171ZEwqz5iVlRX09vaioaEhb+kXAJyur8LQYvYqF4YQ+G968tqWjHRDokhUAEcoqJa5A6VSBHUM6lIIURAhmJMfxJRS6AjB8ZO1iAoiKAMsrm5jcVUioM61xtdMA0A0n1pzIs2ms3oGE3OrKRdjw7F0osRBMSSjXMoy4I0cSESAzi8ADEFFlQ2exc2U68wG235/Ujw1ABBBIlGsVgPmplYRTaGAoIB2nLV6ARVEII64kc8VoYBuMwItUJaJDcgJ2AAPMRvjXvVvtSLJ1R+JNKW1TM4gO7XK6s8Ec2pCUPl0lw18jQER5v416NfCEA06iA6V2oFK91M6MoYC4FMRTBrQipvXQrnVFEfGJCqECMsUtYorY2x5bJn3nKgB1mfw85/Pwmw252w2WShyrZVO1bZDMuYQr1Xk47mXCrvpGXPXB98Igyn/AWVimZLH40FPTw+ampriBkSlNOrd3t5Gd3c37HY7Tp06tScGyFrIJzZbFEWl/GW31D2lhFwW1NraqundsxdkTCKMRiMaGhrQ0NAQp8bo7++HKIqKGiMSlvqRDEHGsJKDgMnJSUxNTeHSpUv73quNEAKbzQabzYaWlhZEo1F4vV54vV50d3crz5aKigo0NTXtWjlfondMIaoZ2XPPZDKhvr4e9fX1EAQB6+vr8Pl8GBkZQSQSgdPpVMgZtZI4n/7TN77xDXzta1/D0tISzp07h69//eu4cuVKyuWfeuopfOtb38LMzAzcbjfe9a534YknnohTKc3Pz+P3fu/38OMf/xiBQACtra34y7/8S3R0dOTUtkw42HdfBmiVKcks/cTERJIBWz54w/EW/O2vBrJevt1qxcZcgca9iXIF1X1KGcBgYBEKJYycRApGEOMHeUrpCwU1SHHWRgOHaCAiGfAykt9EhCHoHVmMbSvBdFXVEDE2GPStb2S9KyxLcLy9Bt41P5aXNtIa/5KoKBExhCSXuNAEiipGiFACzIfDcDQ7UFNhhx4Em14/FqdXd0p/soBeb4LIxZejMSKFbj0ClmPgLrdiKRgCjabovKZTxSjtT1g+7vuE/yZ8FaoxAxwBIhREpBBzrdNniaYprDqVh4n5BFGSpz+P6jeCTqPgS0BywpL650z8fxPXWWxU2i04AROG/rlXubKYcBQ0EAY1x2YqYwSWmowxmHQoKzNjaUG6D3grl5SolQ6ZSpRkmLl4sk1NljQ2uDCxmv19mA0ycV5EBD77ttfhrktHcOPGDbAsC5Zl8ctf/hIWi0UxsXM4HLveucinVrpQA9/DaOtDHGQUy8R3t5QxDEPwtofuLHAdjPJsmJ6exujoKE6dOhVXVpNo1CuXQRYDsqdKQ0NDXErTfkM2sdkulws+nw+iKOLKlSuvivKXoaGhtD4kZdzekzFqJKoxtra2lHKm0Y15AAADBqFQaE/izYsBSinGx8cxNzd3YAk/nU6Hmpoa1NTUYHNzEzdu3IDD4UAgEMC///u/K2U+brcbVqt1V/tP+YYoaKkqWZZViJe2tjYEAgEliWpsbAxbW1v40Y9+hLvvvhvhcDin/tP3v/99PPLII3j66adx9epVPPXUU7j77rsxPDys6QP7ve99D48++iieeeYZ3HbbbRgZGcEHP/hBEELw5JNPAgDW1tZw++23401vehN+/OMfo6KiAqOjoygrK8vreKbDgSJjcr0AEzsTPM+jv78f6+vruHr1alHcwq+01GVt4ksACLcKU2gAACUkTtVCVceFMgQGLoGMiSXxJKatEEohcICgY8Ca9ahrKMP88gZEjXKcVAirBqGRiHQMInzm82Qy6nD0aCVmF9fQHyN6Mg3wmaioqFOICICVPpOSnGK+KrIqJYG82NgMYWNzJ3nIVG1BfbUTZh2LwFoQC1M+RCOpO55+fyhuQG3RseCX/ACA9tYq8HoGG5va5WoUGp4sWgvF7Wz8D1IdURFAsNGKaLkRBq90/EmUgppzn41UX1Nxxr2Ugohkh0ySwpoKAp+Q8EMEmjn1SYuQosU3bjbpOZyxuxAcXMPEwGTSsWcCYQgxMka+pyKq+9/lskBHKQgBRJEi7M7N9DApFSkVEsgz+XeUALPrW1q/KAhiOqEVBR656xru7jiKmzdvwuFw4NSpU2AYRpG3y7M+AJTZufLy8l2VRudSK83zfN6KHrnm+SB2DA/x6kQpPPeyAVNg2XK2uPK2i6hsKswjQu4zDgwMwOPx4PLly3A6ncr3pfKHAYDZ2VmMjIwcOE8VrdjsxcVFTExMQBAEGI1GTE9P71psdrFBKcXk5CSmp6dx4cKFtOUvJnZvysmygRxVzLIsFhYW4CpzAmtSd+7ll1+GXq9XlE0ul+tAnCdKKYaHh7GysvKqMB7e3NxEZ2cnWlpa0NLSAkAqIZNVM+oyn0zlgcVGriEKPM+nJWAJIbBYLLBYLIopdU9PDwKBAB555BFQSvHwww/jne98J+65556MKXJPPvkkPvKRj+BDH/oQAODpp5/GD3/4QzzzzDN49NFHk5b/5S9/idtvvx3ve9/7AADNzc1473vfi5dffllZ5qtf/SoaGhrwl3/5l8pn8nkpNg4UGQNIJzCbGFkgvkwpEAigq6sLHMfhtttuK9oAQMexuK21Ef86MJ5x2SMWM7bnimDcy0CJrU0CIdAlRlaLFCQqQAQgmjlErRxEIwdRxyjkRRTA7OJ6zk3ZCuwQHNv+QOyziIbZiASH3YSGRhfGp7zJyUgxBUaigkSnY2HigW1OVWLFi6AsGx+3rXVZpLhUgqEoRqd2ysU4px4NNU7YTHqEN0OYn/AiEtp5uIRCPCgnFdaUWXQIzGwDFDh9pg4e3zZWxDRyboLM5q3plDGUSlHkCRAJEGixS+awidvLA3FlKOp1UCmtSvmQZOseot0sgQNEg2oHKQWjXXkV/3vZMybxsyKRMQwhOFddgfVXVjDpm0SL2wqPVjqZSAFRlAizmKQ9qlrOajVgrGsGzWfqML64KZVe5dKOLMc/25vxseby+YtaWETTxLPmC8GQyu8IePiNl3HvtWPKjM6pU6eUgYpa3k4pxcbGBrxer2I2aLfblU7gXs/6yMSMTKYQQhCNRvOKzn6t1Twf4tWFYiljaMrOSnFx7yfyi7NWQ97fjY0NXLt2LS49qFREjJzmubi4iIsXL5Zk1nU3EYlEMDMzo5iOyuVMuxGbXWxQSjE0NASPx5OV6mK/K0vkwX5dXR14GwHWuqHjdHjjG9+ItbU1eDweDA4OJpnL7pVnUTpQSnHr1i2sra3h8uXLBSd97TU2NjYUIqa5uVn53Gg0xpX5rK2twev1KuWBZWVlyj21m8cgUzl4MBiE1WoFz/NZhShwHIdLly7hr/7qr7C0tISTJ0/itttuw3e/+1188pOfxIkTJ/AHf/AH+M3f/M2k30YiEdy8eROf/exn49p355134sUXX9Tc3m233YbvfOc7eOWVV3DlyhVMTEzgRz/6ER544AFlmf/zf/4P7r77brz73e/Gv/3bv6Gurg6f+MQn8JGPfCTfw5Z6/4u+xn0EuTPh8/nQ3d2NmpoaHD9+vOiM7xuOt2RFxtjngUDGpTJDMltVG2eovwO4mMJABCDqGVAjQbjcIJl5FvllsboRVP5tMBoBhMHzIsrtJmyovqussMFdYcPIxAr6BrXjqWVwLIOGGifsRj1Ca0HMDC3Db+YAtSGtFtHjMGFjO2GACiSlNGmB50VMzu541RALh9oj5XDZTBBDPLwLG2DFKBprnFgaWAYoRXWtCSO3FmBodiC0mpqMSesVo26nCqLqAzYoQh3ORAGIHEHgiB2COi1JXiaH1J6UoFCIBiaRICMEqTOvMyMx4YeJ5EjslKA0qb3SBd1kALM/koy1T52oxvC/DWsuSwBJlaJnFDVKOBhV1Ew6HQvKMRhf2kLUzIHmUKKkEJJZYMW7DWKioCCKkTAlQMRWgpkSSrXJGErx4dsu4rfuOIEbN24o6RKpOqWEEDidTjidTsVsUJ71mZycBMdxSgewvLx8z2Z9Jicn4fP5cO7cOQDIqVZaxqFnzCEOMopBxmxvb6Oru6tILUqNxpP1OPuGkwWtQzbqBYCOjo64AWipjHp5nkdfXx+CwSCuXLly4NPXvF4vent7lQElIWTXYrOLDUEQ0N/fr6QM7bf25YrV1VX09PQo52ZxeQKAZOTLsqzy3pXNZdVlZzabTTlPuzlhkgqiKKK/vx/b29vo6Og48CVw6+vr6OrqwpEjR9DU1JRyucTzFAgE4PV6lbQzs9msfL+bKjR1/4lSiv7+fgiCoKR95RqiEAwGQSnF5z73OTz++ONYW1vDv/zLv6ChoUFzea/XC0EQksoHq6qqMDQ0pPmb973vffB6vbjjjjuUkquPfexj+P3f/31lmYmJCXzrW9/CI488gt///d/Hr371K/zu7/4u9Ho9PvCBD+R0jDLhVU3GMAyDtbU1TE9P4/jx4ylPZKG4o70RHMOATzMbfarCjaUXZ4qzwQRlguLlIFIQgWKJFxBy6XKKos0X4QgPq4lFKChAPaS2243Y2AiiscEFk1mPkfEVLK9ql/EwDEFDjROBtRA4QURg2Y/F+XksQiKXRI5JSgZSFDGqgavZrE8iY/IFpcDc4jrml3Z8N9pbKjDXvQAqUDQ1u7E47UPl2SqMz6ylXk82qhgg2b9GflBRCsPqTgkMARA1MAgdcSTFkGsZ3BYMFtrXUD6lSlQimQSTypiYp2CynTiVlTE04bMC9rfKbkFDWIepf55VPisrM2Pm5lTa3xEh5pakKlMiZgMoBZaWNyG4rAAhiOZIjBAhe2IqFIrCHUtUOtVUiZm5OYmIKQYZp4XE9VKKB6+cxfvffAY3btyA2+3G8ePHc+qoqWd9RFFUZn1GRkbiZn3cbveuDVSmpqYUI0C73Z5UK62Ozk7VuZA7SgddNn2IVw/yKVMqxMBXNr+trq3Oex3Z4t6Pv6WgAaLc1oaGBkxO7iRdltKoNxQKoaurC3q9HpcvX973KpFMmJubw/DwME6ePKnpxVjK2OxiIxqNKkaqr4aUoZWVFfT39+PYsWOoq6sDALCx48uR+EGx2lz2yJEjCIfDSorW1NQUOI6LK2fabYNpQRDQ29uLcDj8qjg3MhHT2tqa0xhVXebT1NQEnueVcnBZheZyuRQV2m6om2S10ubmpkJoZxOiIP9bht/vh9lsVj4rKyvD/fffX9S2Pv/88/jyl7+Mb37zm7h69SrGxsbwqU99Cn/0R3+Exx57DIBE+nV0dODLX/4yAODChQvo7+/H008/fUjGZFumJHfsZVa7lNJPu8mI8001uDE5n3IZ41S4uBtVi2MowIQFUJaBqGcRAko3INOAu9yOubk18MLOqLrKbUMVq8eGwGNoLN6wmCEEddUOuCxGRDfDWBz3YnlhAYKJA4kKYPjYjgkiKEMgmlJfpla7CVuxeG1dKtPafGpqIPmYUAYgDEFrkxsLPYsQogIqKm1YXd7EkUsN6BlPb8ac0SsGEhGTlHsTay8bEJPIimCLA9Sosa9K4lIR2BiZ5EjVIcrz8uItzM46RQoms9VS/CYTCM98y5SMOhbN0GH1Fx5M8fErqLToMb6d/n4lvBAjY6T2UAroDBxCvAhPMKrso2DKtURJ5QVFacYOqdtqgsNmwuZ6CCID8IbSzIQkXceU4v4Lp/DBt5zHjRs3UFFRgWPHjhXUgWYYRjF3O3bsGPx+/67P+kxPT2NyclIhYuR2AZlrpdVS3GAwCFEUDz1jDnFgka8yRvbYGB8fx6lTp+C0lbbsxuI0403vvT2v31JKMTMzg5GREZw6dQo1NTWYnJxMImCB4hr1bmxsoLu7GxUVFSVRa+8mZAPV2dnZnMqsihGbXQqEQiF0dnbCbDbjzJkz+ybNKl8sLCwoxsNqI1Mmdi2zJP21ZzAYUFtbi9raWmVc5fF4MDQ0hEgkogz43W53yRUqgiCgu7sbgiDg0qVLB57AXF1dRXd3N9rb21FfX1/QujiOQ1VVFaqqqhQVmtfrxezsrFIOLvefSkF2ykTM+vo6Ll26pJA/+YQo5Fri7Xa7wbIslpfjx2PLy8uortaeDHjsscfwwAMP4MMf/jAA4MyZM/D7/XjooYfwuc99DgzDoKamBidPxisuT5w4gb/7u7/Lum3Z4sCRMdkgFAqhu7sbkUhEyTIvNW5rbUhJxhxzu7Dw4lxRt0cZshNFTIhkYouShsukhMUm3XQyGXO6uRID/zoCKlIcv6qS3PEiTtS4sDy1Bk/XIhIDvqWxf2wvRBEEFFSv00zYkU2Lm4640T8glT1x6uUS1RP5gAJsRERzeyWWB5YQjfCwWgxgRMBZY8fAVPqIcgokpyJpQDOemWEkVcxaMltBWAaIiKBqbyCRxpFz+UCnk9arNCEDiZUrx0UB8OYdxQ8TzYPTSSRfKAWTw1oYQnC+ugK+l5bhXU02066tNWO8czrzimLmuYppLoAwCKgqVlXQk9xKlJBdfLQaZWYTeqaWYacsRB0DWqInupiw3vtOH8ND91zEjRs3UFVVhfb29qK/3Hd71mdmZgYTExO4ePFiWnP3dLXS8v/W1iS13GGZ0iH2E3L13MuVjJFLO9bW1nDlyhU4HA6Eg1kYghWAtzz4BhgtuQ8C5djl5eVldHR0KP1EhmEgCIJyT+fqFZUJy8vLGBgYwNGjR9HY2LjnJR+FQBRFDAwMYH19HZcvX877eZdPbHYpsL29jc7OTkXleZBJMkCaXJiYmMD58+eTjIdlMobLYR+1Jkw8Hg8WFxcxNDQEq9WqnKdiD/hltRIhBBcvXjzwUdwyEaNWKxULahXa0aNH49RNMzMzYBgmrhy80GNJKcXg4CDW1tYylo1lE6Lg8Xhyuvf0ej0uXbqE5557Du94xzuUdT/33HN4+OGHNX8TCASStiETRvI78vbbb8fwcLxdwcjISNpSsnxxsK9mDciSr/LyclRVVWF1dTXzj4qAMw2ppbi2BR6FZyglQF0mssfvci6mSOF5ESebKzDzwtRO2Y08uKcUxiU/RIsZga0UpUQk5ocjikoZkmDUvkQrq+xY3vCDqHaeS5HaIK0r94NEYmSMd8iDSIgHyzGoqLDC693Glo2LUwJpgaYq8UncjpAs76CMtipG+lJKHiJREVQXY539+cvJZdjKLNjySyk8BBRIN2NCiMTWZF1iRCEYd8gJIiD78iT1ZgtgG49VlYMd28ZM56Tm91abAdtT69m1Qx6kiJJ6S3BagARlVsSW5vEqGwAr66NgIzQ+1lqkaSOxLWY9Ip4AwpthrPtjB7NEnUfBuNOOe9qP4Hd/4wpu3LiBmpoatLW1lXxAkTjrI0d0FmvWZ3Z2FuPj47h48SIcDkfWv9Oa9eF5Hn/0R38EvV6PaDQH6dchDrGPoA5AyAayooBhGFy/fl0ZNLOZUvIKACEEb//YW3L+XSQSQXd3N6LRKK5fvx7nByInwOn1+qIb9U5NTWFycjJJpXAQEY1G0dPTA0EQcOXKlaKRJNnEZsuqmWLGMa+traG7uxuNjY04cuTIgSbJKKUYGxvD/Px8nMpTDXkSS5dBGZMKiSlakUgkacAvEzOF+r9FIhF0dnbCYDDg7NmzB16t5PP50NPTg+PHj+9Kclqiuml9fR1erxfj4+Po6+tLKgfP5dqXTa5XV1dz9u/Rmtjy+/34kz/5E0QiuZH4jzzyCD7wgQ+go6MDV65cwVNPPQW/36+kKz344IOoq6vDE088AQC499578eSTT+LChQtKmdJjjz2Ge++9V7m+PvOZz+C2227Dl7/8Zfz2b/82XnnlFXz729/Gt7/97Zzalg1eVWTM/Pw8bt26hba2NjQ1NWFhYaEoaQDZoJwVwRDFQkLBkTIH5n+0WNyNEVkZkjwyJYixerv4IpHNZuvdNkz/2yREFUkRDkg3lN4TABMVYNBnvuRILKJaMHEpB6OykESVpQQ21UA00wA+xfGyWvQI+wMIiFGwANrbqjA8sIC6S/UYm0qfipWtKgaQyZhEPw5oqmJ2Vg6AEImQYQkoy4DE2Ll8CYuo+l5JVOpoIcdLTImzzrE8KQ40fv8IzdyMaqcVdX4O08+m92xqrLBhZHwlu3aIFBCpVEbntAAaRGC6EiU2LEIwMSA8BRulml4xKc9jjMhprnBicHBRUjSVEpRC0Eute1NLE/6f37yOGzduoK6uDkePHt31Tqsc0Wm323H06FFEIhHFBDifWZ+5uTmMjo7mTMSkwuc+9zn8/Oc/x+DgYFws7iEOcZCQizJmbW0NXV1dqKysxMmTJ+NmHFmudAOnjnvOo7olN1JDVj9YrdakGXZKKTiOw8jICKqrq1FRUVEUkkFW4fh8PnR0dKRV3h0EBINBdHV1wWQy4cKFCyUbHGvFZsulqxMTEzAYDAoxU0jpqqxWKka5yF5DVin4fL60cc9yf1nHFOfc6fV61NTUoKamJk7dpPZ/k8mZXMyQw+Ewbt68CavVitOnTx94tZJscn3ixAlNb6VSI5HslE2AvV4vxsbGYDAYlHSmsrKytMdbjhb3er0FGykzDINQKIQHHngAJpMJY2NjOfUt77//fng8Hjz++ONYWlrC+fPn8eyzzyqmvnLfUMbnP/95EELw+c9/HvPz86ioqMC9996LL33pS8oyly9fxt///d/js5/9LP7wD/8QLS0teOqpp/D+978/7/1MBUKz1azuE/A8n9RBEEURw8PDWFhYwLlz5+B2uwEAS0tLmJycxPXr10vWHrlednJyEv99yINRz3rc95eiNizcSO8rkisEIwMKAi4WvSwYGPDWndpJMUtFRrHQ3lyBif4FWDd48NH4c+OusmNx0w/dWhAEQMvxaoxPaxve8mYOECl0gSgoKCLl5pTeN0YDh6Ag4MyxGqVM6dTJWvSPSMQXExHA+aW2RC1cfElPIhJUCgBQW+XA6tQqxJAAkQPOtddgoGsWx25vQc/IUsZjQhlI6VUJIBEeNIGQ4raiEFnAPLJT9uRvLYeO125zuEwHUd4fSsFsRyHa9DB6IiAUEHQEkbLca2nNHAfBK6mWBB1Jq8qQt02ynDgVWYpwuQ6gFGw4f8IoZAe4EMDFSHPTUgjUyGKtNbnDbDHocMrqxPS/z0KIpB9UtLZWYOqXYzm1hbebAKNO814TdASButQvJt1qGFTPgknD2JGIkGRcDQAQRZxqr8FQ74IUTy+DUoTcJaihphTBSga31dfhD97/Bty8eRMNDQ37cvZQPevj9XoRCATgdDqVzkXirM/8/DyGh4dx4cKFgstZRVHEY489hh/84Af42c9+hvb29kJ35xCHKCqi0ahiPp0Jo6OjCIfDOH36dNrl5ubmMDg4iPb29pRlN79hfTBukqZY+MP/+3u4eOeZrJf3er3o7u5GQ0NDXGml2qg3GAxiZWUFHo8Hm5ubsNvtqKysVJQYuSISiaC3txc8z+P8+fMHPvlF9rupqqoq2CesEAiCgNXVVcUEON/Y7NnZWYyOjr4q1EqiKKKvrw9+vx8XL15Me611r87jQ6/8NU7aqvHd23+npO2S1U1erxfr6+uKWbPb7YbD4Uh5DQWDQdy8eRNOpzOJ5D2I8Hg86O3txalTp1J6mewl1PeU1+sFz/NwuVzK5Jb6eqKUYmRkBCsrK+jo6Cg4bSwcDuP9738/VlZW8K//+q+7Yi+yn3DglTGRSAQ9PT0Ih8O4fv16XOJGrjLbXMHzPPr7+7GxsYFr166hM9IdR8ZUGfVYeLG4RAwAqeRmH42BLHoOvJlD1BtOahbHEoWIAYANnz/9ymLPWt6sS2tCHArzMJjjL9+UXrOUphfHyDHOsQd9dYUNkYVN8FEBDKSSmlvds2g5U4ve0SyIGKSIs6YU7FYYfHlCuwUKotpXwcgiWm6EbjmFTE8tvyIEDGGAQOHXeTgSzfmBkK1vDG+WDgjhCys1SvJUSYzUAsAyBOcqK+B7aQkTa1MZ12k06bAxkd7/JxGixZCSiAGAaIoSJcJL5UhcUITAslJalQYoJDNfrUNlNuoxOrgUT8QAJXsmUAboqK5WiBhZxr0fkTjrEwwGlZnU8fFxZdbH7XYjFAphZGSkKEQMpRR//Md/jL/+67/GT3/600Mi5hAHHpn6T+pJsIsXL6K8vDz1uji26GRMfXsNzr/5VNbLT09PY2RkBCdPnozzaEhMSDObzWhpaVGUGPJgf3x8HCaTSVFipBtEyvD7/eju7obFYimpgmS34PF40NfXty/8bliWLSg2W55InZubw8WLFw+8ipHnefT09IDn+axShmTjXh1T+mGg7P/W3NwcZ9bc1dWlxJ8nKln9fj86OzuLEg6wH7CysoK+vj6cPn06KYJ5vyDxntre3obX61U8gWQSrby8HCsrK4rfVqFETCQSwQc+8AEsLCzgueeee80RMcABJ2O2trbQ2dkJm82Ga9euJcnROY4rWZmSLNNkWRbXr1+HXq/HmfoqfP/lPmUZtw9I6VhTYCkRVRMVe/iMaq4tw8wLU0ClPqkZRpMOCPNxn695t6B3mBEJJ5wXccfzQmQA0Zx5VsNhNyGlriuXAb+KUagot4J6AthaC4LGkmkIBewVVsxsBVJvTw0G2udWpGC2w0D5zuwapRQMTyGqVDSBo6605zSuTIenICBSAlWBBr4ir+os0yxoFkKkxmTYHiWSXwxECqZQzojKtc5Uaaf6eByvKgcZ2cLMTW1fGC20NpRh6Oej2W4eosMEakzf0eETSpRkEkb2ySE8jXnCpNoQ1TTzZVmCaJCH1mEslcTxZHMlvvTAG3Hz5k00NTWhpaWlRFsqPkwmExoaGtDQ0KDM+ni9XvT39yMajcLhcMDv98NkMuU9Y00pxVe/+lX8xV/8BX76058mue8f4hAHEenKlNJNgmmui2MQLXKg5Ns+9pasZspFUcTQ0BCWlpbijHoBJCUmJa7PYDCgvr4e9fX1iom4PIiUPTEqKyvhcrmSfru6uoqenh7U19ejtbX1wA8mZQXJqVOn9t1gMjE2Wy69SBWbLZfyyD4XB91oPRKJoKurCxzH4dKlS1kZssoGvvoilSlli0Sz5o2NDYXslP1L7HY75ufnUVdX96q4d5aXl9Hf348zZ84cGPWVOuJc7Qnk9XrR2dkJURRRUVGBjY0N6HS6vJOteJ7Hhz/8YUxMTOBnP/tZWlL/1YwDR8bIN+XS0hL6+vrQ0tKS0rcg32jGTJDro6uqqnDixAnlJXy2YecF1eC0w/fSUurhbJ5xywrigoOS/UZ2g6BprHZiu28F4WAUhI+/EQkoGmodGIuVEClNo0CF24b5+fW4D4lIY144gGBOrThQw2xKPyDeWX8W31OgvMwCdi2MNa+k3tFxLEReun5EpxH+tQyqHnlVqfqHlIL1x6tdSMwDhcTilSN2PaIuA5hImkarvmJjBslqZU1ep17UHvxnhNpIOgV4U8zkNFL4ZUkoUFduw+KiZIkteSQBtWU2VG8xmMngC5OIpqZyDGdZnpTKqDcRAgsp1YhK5BMTpUlmxYwgpuWxNNVDlMKmN2IjFNT+UQni7IlA8RuXW9HZ2YmWlhY0NzcXfRu7BXnWh+d5LCws4MSJE4hGo3GzPrLMPZtZb0AazP3Zn/0Z/vzP/xw/+clPcOZM9iUThzjEbiOXQU2qySy154rWJJgWiu0bY7IZ8Wu/87qMy8kJLOFwGNeuXYsjjdQpaNkY9apNxOVyyJWVFQwODiIajSrPDrfbjZWVFQwNDeH48eNFT0rZbVBKMTo6qiigDoKCxGw2x8Vmy8SMbDAt99kvXryYV+nZfoJsnG2xWHDmzJmsS3nY2PWu28PSH4ZhUFZWhrKyMsW/ZHZ2FtPT06CUwuORFMtutxtOp/NAkjJLS0sYGBjA2bNnUVFRsdfNyRt6vR7V1dXw+/1gWRYnT56E3+/H1NQUBgYG4HA4FNWx1WrN6lzxPI+PfvSjGBgYwM9+9rMDfXwKxYEjY+QXw9TUFM6ePZuWoS9FmVK6+uh6lwMuiwmr/iDq/TpMpSMBCpzGpqrtmi16bGUda1Mc1FU6EBr0IrQlTXcx0fjtnzpdi4Eb2hHBNmuCv4cg7gwkGQJRnx3DakxYLpX9EckgZyEAjAYdjP4ovCtSmpDZrEdQ9TuPd0vTpFVzZSkGxUSkYAIRSREhL6OKSObNOgTb3TEiKh0ZI31H+B0ChWp8rwV2PQyGZaXrh0AhwIigUVolqtxxU9aApW6m1BQK3sRKMdZFkG6YOR3sZiMW5XwyCnCEQPj3Fczwud0DOh2LqGdzJ/krDSjHSERMFtcAb+fA8JDSkVKsmghistu3ensyQbnzASwGPTY2UxAxAAwWPcLFfA5QCt2mACG8gqOnj6GxsbF4694jLC0tYXBwEOfPn1dmYFpaWhTptOwpAQDl5eVK50Jr1odSij//8z/Hf/tv/w3//M//jIsXL+7mrhziECWF1mTWysoKent70dTUlNOMdbETle78ndfDbEsvjff7/bh58yYsFksSaZQrEZMIdTnksWPHsL29jZWVFUxNTaG/vx+EENTX1x/4WV45qnxrayutGex+hk6nU4xlQ6EQbt68CUEQQAjBSy+9pMRmV1RUZCzt2W+QS3nKy8tx4sSJnK5j2a9OvwtlStkiHA5jYWEBbW1tqK2tVfxLenp6ACDOmD9fJcZuYnFxEYODg3FepgcZ4+PjmJ+fj3sWtLa2IhQKKT59ExMT0Ov1yn3lcrk0yzMFQcB/+k//CTdu3MDzzz+/Lz10dhP75y7MEtPT01hYWMC1a9dgs9nSLsuyrFIPXKjxk+waPT8/n7Y++mxDNYYXPJj+17lMK0QhOgHCEpw8Vo3gVhhj3g3AtnsPphq3DcLYGgIbOxHVajKm/VgVbt3UJmIAyUdGgSjGj/MJybqEi2OZOOJBTDW4zTDWJgDYqAhvzM+GZQmqq+wYnV9VBEhZVONIm0o3AShK62H8EYg2A8CL0G2EAbAgIsX2STdEk2zMm6a9se+YSHxZkXLEaGrzV+PSNvhqh+YGeHXlG4WizlH/v0zgADuBS2xI2LmcibSQqGNABKokKJEiCdSO1LoghtX7DVCexpdYZYljrRUY+rfhjMuJBg6iw5zxmqQAHM0WGGwmzC1upV2W8DSzMkYpO6PgwCDgTx/1F4hEAS3D3zzBBkVwFDh75jgaGhqKtt69gpyYce7cuaTnt1o6TSnFxsYGvF4vpqen42Z9QqGQUr/+7W9/G1/+8pfx4x//GFeuXNmjvTrEIUoD9WQWpRQTExOYmJjAmTNncu44M0VWxrz94+njrH0+H7q7u1FfX69p1Ct7xBQjulqW85vNZmxvbyMajaK2thbr6+v4+c9/DqvVqhgAZztjvB8gx38DwJUrVw4cUZGIYDCo2BqcPn0ahJBdi80uBTY3N9HZ2Zl3KQ8TmxA07HKZUirIKUPqRCtZiSa/k9WeQE6nU1Gi7UeScGFhAUNDQ5r9jYMImYi5dOlS0vE2Go1KOacgCFhbW4PX68Xw8LCSpOV2uxEOh9HW1gZRFPGZz3wGL7zwAn72s58dePVgMXDgyJjGxkZUVlZm9WKQZ0IEQSiIjIlGo+jp6UEwGMxYH322oQrh6S1MZZhtz3ZwrwbDEBxtqYAvFMZiIIihnnkAgLnWgk3V2vJZd7awWzgIY6sIrMcXgMtkTHWNA7MjS2m9VQQ5cUkUJdWK3FHKscyCkHhxgcDHD9I1/50Aq8WAwFoQgUAUMp117Fg1Bnvnwdj0kmoHknIlY7VTGlWMtIC0BtYvJemYhj2I1jkBUTJrlTxYpN+TtMoYaTCfWPoigwDggiJ4QkBVJq/cWghMoldP3O+0FTlxe6QiCeTPqUbZDkMhqZzMLNho8arm9CyLgCANEHQFzLbW1jox+ovM5UmixSCZ9abp6FBKUXe2ChPrm1gzMBBXMpezEUFMq2BSjjOlgAAINDPZJGqkd+ULwlPoAhQmq+5VQ8T09/fj7NmzGWeoCCFwOp1wOp1xsz4rKyu47777wDAMzpw5g5deegl///d/j9tuu22X9uIQh9g9yGVKgiCgr68P6+vruHr1al6xzGw2qtIscfEtZ1DXljoSdmZmBsPDwzhx4kRcTHGiUW8xiBgZoVAI3d3dYFkW165dU/qn0WhUMQCempqCXq8vShRzqREIBOKIi4NuPLy5ualYC6jNYBNjs+VzVczY7FJA9iMqpHSYgewZs/fDQNnc9uTJk5pxz+p3cltbG4LBoJL4Mzo6qhhry+VMe32u5ubmMDIygvPnz8Plcu1pW4qBiYkJzM7OZuWvxLKsomCilCIQCMDj8WBgYAD3338/6urq0NTUhKGhIbzwwgtoamrapb3Y39j7uzBHsCybNUMvv0AEQchb0ibLAM1mM65fv56xPvpibTX+9wvPZ15xDmxJQ30ZHFYTZie9GB9YRNTKAQ69lLgCCo5hAFoao2I13E4LDAt+bKwnO/ExUREWqwFCIIxQMJp2PVsbQYmIUZXrUEpz9rwQRRo3nuVTpTWkONZmkx6VHIdJCsSM5XH6dB0GumdhsuixDcSpTTIhpVdMDHLZCbcWhG5pGwwgGe8ygKBjIBrjjIBSr4cmqGK0wDLg/Dx4KwcaIy0MS/5d9XrW23QIgkrETNFAIcTO88kqF8am/OlJDQ0wDIE+GIbAp75nKADRbgJN40tEQRE1EggVJgxvbQEswfE6N4Z9Cyl/I4MING27CY19z2dPZNFMUeRZggDQbQkgANzu3Ade+w0rKysKEZNPTbJ61md4eBhf+cpX8L//9/9GWVkZ7rvvPrzxjW/E2972NrztbW/btylThzgEkJtnjKyMefnll5WgAoPBkPmHGuB0xetq3vvxuzQ/l416FxcXcenSpbhBUCaj3kKwtbWFrq4uuFyupPhdnU6H2tpa1NbWxsXG9vX1gVIKt9uNyspKlJeX7xvCY319Hd3d3aitrUVbW9u+VodkA5/Ph97eXjQ3N6O5uTnl/qjNmgVBUMya+/r6FLNSOUkmG5+kUkF+nx07dqwgRQEbu04N7N4OAxcXF3Hr1q2czG1NJpPiCcTzfNx9JYqiUiKTqsS4lJCNrouR0rgfMDk5iZmZGVy6dClno2tCSFyS1sTEBB599FG88MILoJTi6tWruOuuu/Drv/7ruOeee/adMfhu4sCRMbmAEAKGYfL2jZH9AxoaGuKkrulwvKEypjDIgAwDSFeZBfU1TniXNjE/5sW81ipYguPHatA76wHspZWQuhxmmFeCWF3Z1vyeiYqw2QmWZzczrsu3vAmiipIGAM5pRIRkdeQURCMCOJU6gk8xuNZSmRiNOtQaDZge9QA2PQCCYyeqcatnFgBQfaYGo70LO5YpGZROFIgzVdaEPCPnjwAci2i5FSTGAoXL9dmna4kiGDGh46alBOJYsH4Bgo2AUgo2yGdPeBWBQNFXmMCsBgpfkQpUAARBRJndhNmXpXOVjeeLGieOVWE4TXkSJQRCWWqjXpmEiZQb45RHACCEsnjWiGJmw2QREExcTvcDLVKf+Xy9DUPeNQBAXa2zOCvdI8gdtDNnzhRsDkcpxT/90z/h6aefxt/+7d/i7rvvxujoKH74wx/iH//xH/GDH/wAL7zwQpFafohD7C22t7fB8zzsdnsSyZArmCJ5xtQcqcSlu88lfa426k1ULxfqD5MO8vNFViikW3dibOzGxgZWVlYwOjqKvr6+feFdIpdytra2vio8wuSB/okTJ1BbW5v171iWRWVlJSorK+NKZNSJP+lis0uFhYUFDA4OFiWVR1bGGPZQGaNWkORbysNxXNy5kiPO1SXG6nKmUpKLMzMzGB8fPzBG15kwNTWF6elpXLp0KaMtSCZQSvGNb3wDP/3pT/H888+jvb0dnZ2d+OEPf4hvfvObeOmll/D0008XqeUHDweOjMn1Rson3ppSiunpaSXGL5eHuF7Poa2tCoO3MsyOyyUIqv0xmnQ42lSBkD+MqbEVrC+mJzbMNj3mxj1AfpNVWcNs0MGxFoVnIXV7TrRXYrg7g09ODKEwHzfQZTgGTJkZ2Ix50GRppxMMRWFVmQGHQikUOQljdb2eRaPVjOmhZYCJee6yDCaHl0EpcOxaM3rGl8GpS78yCFEoi/RkCqXQeaTyFUIp+DIbiEFKjRI4At6ahlxJABMRIWZZg09YBs3lDqzcWgQhJD0HWMR3lNmmg3c1UHQljiiI4AURTUYTRkNRwKiDSClMBg7hcGYixF1hw+TLEym/T2fUSylF1Mwg4jIkkTCAdJ9MTXgztoEIMX+fVCdDVonl2mkownjHwhB8/L2341Pd/wQAaG05uO72Ho8Hvb29OH36dFHiJP/hH/4Bn/zkJ/H9738fb33rWwEA7e3taG9vx2c+8xml/OEQhzjomJ2dxeDgIAAUTMQAxUtTevvH7kpqi1q9nMqol1JaVCKGUqoMvPKJek4su0j0LpEHkJWVlRljw4sB9f4U63m515iensb4+HjB5qmJ50ouu0gVm12qwf709DQmJiZw4cKFopS+yNHWxj1Sxqj3p1gKksSI81AopJQzjY+PK6VnbrcbZWVlRVXIyftz8eJFOByOoq13rzA9PY3JycmiETFf+cpX8Mwzz+CnP/0pjh8/DgDo6OhAR0cHvvCFL7zm+08HjozJFbnGW4uiiFu3bsHj8eDy5ct5sZunztRnJmMAgErikKNHKqFjGEyMLGOwezbr7bjcVizdWgY1JjDzRSwLMRt1cK5F4VnWVsQAwPET1Rjuyi5SmAISEaN6YbVdaUbv6FLObfP7w7BY9Kq/Q9oLqo6HTseipcyGqb4FgGFAY4NukVJEIwIaj1ehf0qK01Mn4aRLZMqoihEpjFNrYDel8i7RZADMO+0Ou7KL8pbBRMX0mTkJTZ2bXYPVF4QIOQqaanYY1LtYUHeCUmxzxUlPSgQviCi3mjD+L2NxKp/KShtmZ9cy/t7JEqynIO0oSyCUWZPUQ6IogreyEgmTZob3SJ0LI77FjG0gsrdRivhq8MiZiJGuwQI7gZTic//xjWhrbVY+On0itTfDfoZsBnj69OmiSF//6Z/+CQ899BC+853v4O1vf7vmMntdp36IQ2RCpoGiutTn/Pnz6OzsLNhzDyhOmpLRYsCdD74+7jPZqLeuri7OCwSA4g9TbEWMfIw8Hg8uXbpU8MCLEBLnXaL2qBobG4PZbFYMgEsx2JfDKZaXl4uyP3sNdRR3KfbHbDajqakJTU1NSbHZsl9GuhSZXEEpxfj4OObm5oo60JejrXe7TIlSqpS+lJq4MBqNaGhoQENDQ1yZ4MDAAHiejytnKkSNNjU1hcnJyVcNETMzM6MQS/l4hKlBKcWTTz6Jb37zm3juuedw5swZzeVe6/2nQzJGhXA4jO7ubgiCgOvXr8NoNOa1zVNn6vC330+/jNGsxx1vPo4bvxjD+EDmAZwWZmdWJdPZEqnuLCY9aqIMlpZTD3LrG8owMZAF8QRtIsZebsbITGY1gRY2t0KocO/UMFL1gdAgUjiWQavbgYmeOZCwAGpiwBo4iJBKT2wuE1b4qOJJEjdYFlOTGGCQevAsiDCOecEFeWV1YtlOmwWdhioGmYiMHE94OAohIuy0XaRAJn+RAogUgaHgc/RxyXrdvAi6EEhST9ksmeVhJ09UYyRFeRKlFCCsJqEhGhhQjqQlYgAAmXx8YiC8dGySSt9Eyaw3n9s5k19RNnjL+WZc75BmLAhLQAWK460HL25Q9gjIZ8ZaC88++yw+9KEP4ZlnnsE73/nOIrTwEIfYf5DTcyKRCK5fv66UXxTiuSejGMqYN7//DlgcOyqR2dlZDA0N/f/Z+/I4R8o6/acq99Gdo5P0fZ/T3dPT5wwDgoAoMMxMD4rsIiyroIIuyHrhtYDr8UNh13tVdBFU1lV0BrlW7kEUAZk+p+/7PpK+k85d9f7+SFd17k66k77I8/m4y6QrVW9Sqarv+7zP93m21ajX5XKhvb0dTqcThw8fjkuLirdHldvtDpjsc4qZWKzsc+bMq6urcfs82wmWZdHZ2Ynl5WUcPnw47qoi79hslmWxuLgIk8mEnp4euFyuLU/2CSHo7u7G/Px8zKPFBWst8tJtbFPiiLLp6emIzGBjCf82QbPZDJPJhPHxcXR1dfFJWjqdLqrUs6GhId5TZavExW7A+Pg432q1VWKJEIIf/vCH+M53voMXXngB1dXVsRnkPsSeI2Oifah6xzOGAxcTp1arcfDgwS0x2uUVmaBpKiBqWaOWIytDjUWTBT945CP4/v/7PywvRu+pQa9NpHnDzjiQMTKpCFmsEJMDxpDbJCVLYV1chcsZoSePgPbxiQEARZYSpqnQqptwYBgWy8vr73U6Qsxkice0tTRdi4HWCU8MtNMNSiGGSCqCy+ECQCG5QIfB0Tmf93GguH9TAbsOGWdNORnI+kygvVKeWKkYEK9fdtGqYkLBn7zxHqrYZPW5bkKqfGLwOyKEwJ0cP8M0nVSCrp7htQRtLmN74w4dlVqGiXDqLXrtt8mwPi1KhBAwSjGIgIJwxRXys0nEQowMR0YqBlXGbIGI8ex0s2/0QCuV4EufuIr/t1AoABEQiEL45uxWcCkTBw4ciDp+NxheeeUV3HzzzfjpT3+K66+/PgYjTCCB3Qez2Yzm5mYkJyejtraWb/XZiueeN2KRpnT8do9xLyEEPT09vPJhu4x6rVYrWltbIZPJ0NDQsC0mrkKhEGlpaUhLS/OZ7Hd2doJhGKSkpPAGwNESZtziI03TOHz48LYbncYabrcbbW1tcLlcaGho2LTZ9GZB0zRSUlKQkpKC0tJSWCwWn8k+13qm1+shl8sjUql1dHTAYrGgoaFh04vDIcfLkTGC7Tnv3HU7NzeH+vr6HY2jpigKycnJSE5ORmFhIZ+kNTc3h6GhIT71jGtnCjYfJITwKUOxaOXZDRgfH8fAwABqampiQsQ89NBD+Na3voU//elPaGhoiNEo9yf2HBkTLSLxjJmZmcH58+dRUFCAgoKCLa+iKJVS5ObpMDxk4l/LytJgZmgenTNmAIBIJIRWFwEr7OcrAwB6XRIm7XaejCExlq1KJULkUmJM9MyG3EYgoJCSLMVoGLLGG4SmAnw49FkqjM0EiQGOQlSxal1vOXEzLARiGgxLfE17CVCepUN/81oLGAuAYXGwMhPnBmYAgcfKbMq4vP4WQgLmt0G/5RCqGNrqgrTPFEASsGo/VYwixGR3C94uvB8JRQGEQGBz+b4pBBkTrhUrIhACt5TeertMCIhFAsz8fXL9u/E6zKo5RIvaGtJVMgz0BW+FI4QA4jVSzMX4/E5ZL0UMBSqArOFQmKHFQEdkrXacMoY/D1slYhB9LLzPeAjwn58/6XPfE4sFkEj21uNhYWEBra2tKCsrCxqPGS1ee+013HDDDfjBD36Am266ac+niiSQQLDf8OzsLNrb25Gfn4/CwkKfbaJt8w4FwRbTlA5dVoGcA5lwuVxoa2uDzWYLatQbj7YkYD1hKD09PeIwh1jDf7JvNpthNBoxPDyMjo4OaLVafrK/0cR9dXUVLS0tUKlUqKio2PMtAg6HAy0tLRCLxaivr9/RtCPAc50lJSUhKSkJBQUFPrHZg4ODkEqlfDtTsChmjlhyu92or6+Pi6Ez16Yk3wYyhrN/WFpaQn19/a5TYPknaXGkZ3d3N1wuF39t6XQ6SCQSvnVscnJy2xU+8cLExAT6+/tjYj5MCMEjjzyCr371q3jmmWdw9OjR2AxyH2NvVdtr8BiRRjZxDFdMcBfU8PAwqqqqYhqrVVGZ6UPGGNKSMTe+BKfDDaGQBk1TkZExfkhNS8b07AqgEvOTXlpA+3iIcL4gm1FciEUCFIhlGDsfvnWqtCQV3dH4xAgFAeORZ6rBen1Hm4HV5rtqJ5OKYLE6fV6jAPS3eHnxsB6ihbE6fVtd5BLYuFjuID8viiU+k95QqhjBkg3SkcWAyTURCQBJpKqYKImRELsRLNsD0r0olmy8903wMiwNsLL4KSkq0lPQ1zPIq4AItf6xTTOhzaVLSlIx8Ne+0DsWrP82KRcDIvUUJ4QQuJVehYqAhsDKgEkKLFwFUcxXqLU2OIpFTIgYYGtJStdfdhB5Wb5JBlKpCLpN3J92CouLi2htbUVpaWlUhuuh8Le//Q3XX389HnjgAXzkIx9JEDEJ7Dt41z8HDx4MqiTbTABCMGzVM+bEJ94Hq9WK5uZmSKVSXHDBBT5KjngmJnGJPCUlJcjOzo7ZfrcC75X9oqIi3lR2ZmYGvb29SEpK4tuZ/BNkuHtlVlYWioqK9vy9jSOW1Gp1TMym44FQsdnt7e0ghPjEZrMsi9bWVggEAtTV1cWNWOLSlOKtjGFZlm+F2wnFUrTgfH90Oh0IIbzCiTPXTkpKAk3TsFgs+4aImZycRF9fH2pqamJCxDz22GP40pe+hKeeegqXXHLJxm9KYG+SMdEgVJuS2+1GR0cHlpeXccEFF8RcYlZxMAvPPNXK/1ujUSItQ42x4TmI1laJtLrojkmBQCERgl3xTOY481mxTAR3DAomoZBGiUKBkdbwHjDlFenobhqNaJ/BfGIAIOdAGvq2SMQACGgFk0iEAWQM97pjLXa4oFCP0fYJrJjMHiJrbRdyuRiYD3MwfwKQgu/nIgSiWQska+qngLcrZfz27nCqGGADZUyQ4inE9sJFW5BtN25TohBxqNXaLlm44tiepE6SYfxvY+tJRAC8R2e1OFBZl4OhkXlYvc6/XCHGXG9oYpEQ4iEKuT0y60QVK6BAxL7nSOBk4X+liYQCjI1E7nvEkTFERMeEiAGw6SQltUiAiyqTMT09DZ1Ox09w5HIxcrK2ntiwHVhaWkJLSwtKSkqQmZm55f29/fbb+MAHPoBvfOMbuP322/f8ZCWBBPzhdrtx/vx5rKyshK1/YqaM2YJnTGquHkUX5OKNN95ARkYGSktLfSbc8TLq5ciq8fHxLSfyxBveprJOp5M3AB4eHoZEIuENgO12O7q6ulBaWurjs7NXsby8jJaWFmRmZu4ZYmmj2GyapiGTyVBRURFXhY9gbWFRJoxf3cYwDNra2uB0OuOm8IkngimcOjo6sLS0BABoaWmJuWHzdmNqagq9vb2orq7ecqoVIQSPP/44PvvZz+L06dO47LLLYjTK/Y93BBnjX0zYbDa0tLRAIBDg6NGjcblBVFT6PujUGjkysjQYG56DcG2Cp02JsGdyTeVyoDwDPa3jgNozXiKgUHooCy1zi4B4azcBgYBGuSoZQ03h46lz87Tobw+9DSukfTxSgvnEgAIcEv8oZ7+2oC0oezzv931dKhPDYXcjO0eLJJnn4bNqc0GRIofZ4mlxkXh5uQQ7MkV8d+ujiiEE4rEliIORH/C0kRDZ+u/MmRLeKybsJ4/kayEAGBa0iw08Tiif2U12KRFC4JYHN7+NFfJkcgxaZ30/ut/huprGIJEKUX4gHV09npah/DQVet8ITvoRQgCR0Of78W7VYoKRZUHOWWGmFkOdoVv6AnbBELiSJWAU4pjZPW2mVZEmwP13XQUh5cDIyAg6Ozv5vna5TISSwt0fb+pNxMRictHS0oJTp07hnnvuwZ133rknivsEEogUFEXBZrOhubkZIpFow/onUs+9jbAVZcxF/1CPltYWlJaWIicnh3+dEMIrYoDYGvUyDMMbwTY0NOyp1W+xWIyMjAxkZGTwKgyj0Yjm5mawLAutVguJRAKGYfbk5JEDl5hXVFTk87vYS/COzc7IyMC5c+cglUohEAjwxhtvQKlU8qqZpKSkmD6POGVMvNqU3G43WltbQQhBXV3dnvck4lKgrFYrH/DibdjsdDp92pli7fETD0xPT6OnpwfV1dUxiUt/4okncMcdd+B3v/sdrrzyyhiM8J2DPUnGRNOm5C+zXVxcREtLC1JTU3HgwIG4SRpTdEroDUkwGT0qCY1GwaetiMUCEEKg1kZuYJWUJMEEryRZa6kQ0RgbnQeCpPFEI2ugaQqVWhUG3w4fq63RyLE8uwK3K/RKGSsVgrZ4lAmEpnxUBxyKa7LRMR5agsKpMjYDTnVUVpSKfq+UKrFYCG2KEisLq1ClJqPkkmL0zy7DZln3GhF5jzXY78tLhUMorJMPLIF0YB7CIIoc/q1K6boqRkx5yIvNgqIAlvUjuQLHK5pfDf7wDnHtbPY7Z4UA60+uxRA5qWoMvT4S8DrXpuT9CR12N/raJlBWlQWny43eNwZC75iiPC1K/q+5GbAiGqw08PZIxAIoZWJYbOvnWhyNLIUQMHIR3KrYPqg3k6Z005U1KCv2yO6Liopgt9t5EzuXaxVuxwx6e0Uh+9p3GtyqaFFRUUyImPPnz+PkyZP4/Oc/j89+9rMJIiaBfQen04k33ngDaWlpKCsr2/CajlWbEr1JZYxQIkBqjQa1tbVISVlvpfQ36qUoKmbXq8PhQFtbGwDgyJEje2413xtcy8Xc3BwEAgFKS0uxurqK3t5eOBwOflV/q9G+242pqSl0d3ejoqIiJkbtOw0uQMRb4eMdmz02NuaTBhTKVDYaCNaufbkg9ufd5XLxC941NTV7mvQDAs2HOc8bbw+n1dVVmEwmnuDgiDSdTheXSPqtYnp6Gt3d3Th06FBMiJhnnnkGt912Gx577DEcP348BiN8Z2FPkjHRQCAQwOFwAPAYFHV3d6OkpAQ5OTlxvziKS1N5MkatkUO2prwQiTwFjloTuYlVVqYGvX6KFJamsLrqBJI2b4ZFUUCVQYuBN8O3HYlEAiilQkxML4XdjpGJILQ4131i/CAQCTBrc2x6vBuBZdyoKNBD7HcIZZIUdrMd07NWiNOSMDUa2FZC+7QcBe7bO7GIV8W4WMj7jB4FSggQCiCK9XPk0Io3Vv1sxIz4/z3I9kJzcHIopGeMf1IUtVHENsASArcyvrcRxZILi/4x0EDI75BlCYa7ppCtD0128qa9QUC5GDChWq4oCnl6NTrGPMbVAprG+Gi43jafgwIMYk7EeMYV3ebZmiR85ANHfF6TSqXIzs5GdnY2Dvc5cPRwAebm5nD+/HmwLIuUlBS+l3qnC/fl5WU0NzejsLAwJj4OXV1dOH78OO6880586Utf2nWFUwIJxAKcwWmkEayxa1PaHJFbekk+Ln3vu32SV+Jp1GuxWHz8R/b6JNLtdqO9vR12ux1HjhzhJ5ElJSVYXV2F0Wjk037UajXvM7PbDFY5EEIwMjKCkZER1NTUxGQSudPgPHzy8/ORl5fHvx4qNpszld1qbLZgbREp1m1KTqcTTU1NkMlkW06m3Q3g4sUXFhZCmg9TFAWlUgmlUon8/Hw4nU7eF2hsbAw0TfPEZ0pKyo5/JzMzMzwR401ybxbPPfccPvKRj+CRRx7BtddeG4MRvvPwjiBjXC4Xenp6MDk5GbDCEk+Ulqbhb3/xrMyrNQrQak9BIRILwLIs5AoJpDIR7DZX8B2srfqo1bIAIgZYm3/7SwPWEKm6pDpDh/4gqgN/FBXo0NMWXjlD4FEOsDQFyssU1Wc/dTloH4y8pSNSCAQUVMlSqMQ0Bv4+jsy89WKTpikopSIM9s+AUUkxNRvc7NVbbRW0TWmNEOC+d8ruhqzXCHojwkIp5VU0bjEVvP0lYDAb/J0lgNdu/MdL212e8QYrVIMRGwhCvGzwIyKEeD5LHCeu5Tl6jL80GH6jIGMsLNSBXQmS1MWBDtI+x+2OAhh56FujwMsttzBTi5HuCBLF1oiYjcitzSKaNCUBKPzn5xvDbvOed5fBYFDzfe1ms5kvLLiYTq648DeIjDe4VcSCgoKYyNN7e3tx/PhxfPSjH8V9992XIGIS2NdQqVRRBSDEJtp6c5OPCz5YG0DExMuolyOec3JyYpKqudOw2+1obW2FSCRCQ0ODT5uI9+SxoKCAV0UajUb09/dDoVDwxEys22M2C0IIent7MTs7i/r6+n0RJWwymXD+/HmUlpaG9TuLJjY70shobvFRIYzdwordbkdzczOSkpL2RUoXIQRdXV1YXFxEfX19xK1HYrHYh0hbWlqCyWRCX18fHA4HNBoNT6RtN/E5OzuLzs7OmBExL7/8Mm6++Wb87Gc/wwc/+MEYjPCdiT1JxkTzYKAoCgsLCxCLxQFRiPFG2YH1eFWNRg75mmeIUCjgCwmtTomp8cWQ+6AA2FZ9FQ5JahkWwJn4UggMYQYMKgVmzdaw46vJ1qP/L8Mbfo6Kygx0nRvZcDvPgCmwcjEEQZQiMqUEw7NLke0nGAgBTYCiIgMkIiHAEjhsTqwsWrFoWoV5aQWpUk8ilucGZwEAZGfL0TM1DyY5/I3U7Q6tbgHW0m/gUcXQZidkQ/NBSRvvDjGCNePeNThSYvPg2ygRKdnBwhXiOomUENgooYelCVhx/B62NEXB0hXG5DkEWUQLKCz0TkCfHdxwMZwqBgBcyeGVSxOTi8jQJmFqwQyZIIJbaJyJGAAgVOR9iR890QB9SngPhMw0Nf/f3skdhYWFsNvtmJubw9zcHIaGhiAWi33k0/EswMxmM5qampCfn4/c3Nwt729wcBDHjx/HjTfeiG9+85u7YtKRQAK7BbFSxghF0ZMxJUcKoMtbN5SMl1EvAIyPj6Ovrw/l5eVIT0/f+A27HJzCR6PRRJQw5K2K9G6POXfuHEQi0bbd30OBYRh0dHTAYrHg8OHDu1a5Ew2mpqbQ09ODioqKqJJc/U1lueexd2w2d75UKlXI80XFmIyx2WxoamqCVqvFgQMH9vyzlBDC+0ZFQ8T4g6ZpaLVaaLVan3am2dlZ9Pb28sSnTqeDSqWK6/dmNBrR0dGBqqqqmBiSv/baa/jQhz6EH/7wh/jQhz6058/5TmJPkjGRYnV1FSMjIwCAo0ePxtWZPBgKi9MgEFBgGIJklQxJSRKIxQKIJQL+R6tNCU/GAIDT6eanWRKpEAwh/LyL0FTgHIwQvP9YFX7yuzdD7rM2x4C+14Y2/AwFhTr0tESWnMSNg5UKIXAFtsjkHMpEe99MRLtSJ4mhVcsgl8rgsDoxNjgHxsWAoigMt4VOexrom0V2XgpEYhEIAH2mCqOLtg2JFsDzPfMIsnJIEY+YQrRkg2h8KfSOKAqEYUDRNIhCyisw3GIKzFa8YrygUsuw5K2o8huuw+wI7WQSQhkTwG6Eua9Sdpdn/6qNRrp5pAgoLE4th9ki+ABLSgzofaEVhlBpQEFi1jmwNMAkhS9OVsx2yN0MKnINmBxbCLstCAHcJCBePOaIsD4u0Kvxj8drt3QoqVTqE9O5sLCAubk5dHZ2wu12+7QzxTLGkiNi8vLyfOTcm8XIyAiOHz+Oa6+9Fg8++OCeX8VLIIFIsBXPvc1iM54xV956Ke8LEy+jXpZl0dfXh5mZGdTV1W051nU3YH5+Hu3t7ZtW+Pi3xywsLMBkMqGjowMsy0Kn08FgMCAlJWVbamqXy8UbwTY0NOx4i2wsMDo6isHBwZgYp/o/j7n2GM73yLs9JuB8EUASyYLSBlhdXUVTUxNSU1NRUlKy5yflLMvy5F99fX1M6xiFQgGFQoG8vDy4XC7+fLW0tICiqPDnawswGo04f/48Dh48CL1ev+X9vf7667j++uvx4IMP4sMf/vCeP+c7jX1LxszNzaG1tRVarRY2m23biRgAkEhE0BukMM7aoVB4HiCpGWreZBYAtLrQK9QarQJLC6ueyT08U8+C8gy0jhqB5LVEpSBkTGqSHMevOIhHn2yCzR7YAlWTa0DfnzcmYnQ6JUzjC2CZyAo3rlWCFdEB/sFqvRI9wx6Vg1QqQopGgSSFGCKBAMTNwrxsw9js+sTbbLJhddpX2RPpxS6RirC8ZAORCTCzGKZVxQ/e7WKhVAz1eQZ0PNMWfkcCCnCyIDQFNmlzqhhqg2JZo03C0uQ6EUD5EyzhiBSvfRNCor+JEgKBzQ0SxOA2VqDcLJix8MquYMoYQgFjo0a49cmYtDnh0njixAlNAfRa6pCAXn+NWtvF2n8Ll50QL9i9dk6BYgkUSikUyVIwbhZupxvuVSeMs5Og09aVdjqV51zPLa8natFWxhNhHcfnlGf8GxMJQlD4j8+fjOmxvU0Fy8rKePn05OQkuru7kZyczBcXSqVy0w9si8WCpqYm5ObmIj8/f8vjnpiYwDXXXIOrrroKP/jBDxJETAIJBIG3596W9hOlZ4w+KwUNx6rR2tYaN6PeUH4qexmcse2BAweQkZGx5f1xXhc6nQ5lZWVYWVmB0WjkY5i1Wi0fmx3LCSsHu92OlpYWSKVSVFVV7bjXxlbBxaVPTEygrq4OKlVsV7M2is3m0n70en3M0n64RZKsrCwUFhbu+Uk5y7I4f/48rFZr3OO4RSIR0tLSkJaWBpZlA86XdzvTVro6uHa4gwcPwmDYekrm3//+d1x33XX45je/idtuu23Pn/PdgH1HxhBCMDo6iv7+flRUVEAkEqGnp2dHxkJRFNIy5LBaCSjK8+/0TA3crnUFhsY/UYmbKFMUxAKvHzgFFJVnoOv8JOBlLkoEVMAq/wdP1EIsFuLIoVy8+pZvmkxNfir6z27gwQGPAkdMA/Mr9g23BdYmhBzhJaDBiui18dGAgIIhSwOlk8HSvAUrJiuMJiu8nTYIBUAm9Pr35jKVCIDekTkQYeD3shGWlixh/67WyGGJwO+GUBQohgFRKTwTfwBuKR0zVQwAiLyKW0IIqIDFyzCf3Yu4odwsEEpCHmIXtNkBiIRxbbsRzzvhskfoVeA1jiS1FMZkIaBXYBIA5FGY07EEolUGNP9dUvz/t5kdsJkDJyVlKSq0rnp+ydnJSVhl3etkDENAu1gwW4ydjwkIwb988CJoVPFr0/SXTzscDr6daWRkBEKhkC8stFptxIW1xWLBuXPnkJOTExMiZnp6Gtdccw0uu+wy/PjHP04QMQkkEAKxi7aO7h549cffA5FYBLfbDavVCrlcHtOC32azobW1FRKJJMBPZS+CEIKhoSGMjY2huro6Lr6IFEVBpVJBpVKhuLg4ID0mOTmZ95mJ1LckHLhWK67tZa/fp7lEHpPJhIaGhph8R+HgHZtdXFwMq9Xq0x7DxbWvrKxs2hdoaWkJLS0tyMvLi8mzeafhTcTU1dVtqwqLpmloNBpoNBqUlJTAarXy7Wd9fX2Qy+U+7UyRXg9cBHxlZWVMiJjm5mZce+21uPfee3HHHXckiJgYYU+SMaFOPsuy6Orq4m92arUai4uLMZHZRgtOVnugPBMTY3144403kJqaCq1ehgXT+op/gDKGACAEBw5moKd9gp/MK5NlmJkJbNkgAt/vQkJROH5NFQDg4oYCHzKmOkIiBgDysrXoOx9oGhwSQoHPWNxqL9adIejunolOHLCJ65vQ8Ex8ozAz9YbdwYAWACwvl1j3f5ErxJAYFyFJ9V3JEAhpMH4tUISmAEJ8VTGaKIu9DYgO7xsxxQbpVAv3FfiQMQxICDIm6BDcDOi1ncfrFpypS8ZSX3CT5fAgMJsdQPLmbmtCGxM1wWQ2eQg8mgJmRuZhKFgvgmm7O/7tSRGiLEOHU+89uK3HlEgkyMzMRGZmpk8aRE9PD5xOJ79Kp9PpQq7ScfLn7OxsFBQUbHlMs7OzuOaaa3D48GH8/Oc/3/MrrQkkEC2iKZ5j1aYUjTJGJBHhfR++FGKxGBqNBm+88QaUSiW/4s9NIjeL5eVltLa2wmAwoLS0dM9P8lmWRXd3N+bn57fV2Na73YIj3o1GI4aGhiCVSnnFzGZ8MLhJ/n4xU+baXsxmMxoaGnZEhSWXy5Gbm4vc3FzeF4hafB3nzp3jF0r0ej20Wm1E18TCwgJaW1tRXFwck0TDnQbLsmhra4PD4UB9ff2OE7RyuRw5OTnIycmBy+Xi2wW59jMuTSslJSXkWDkiJlpfolBob2/HyZMncffdd+Mzn/nMnr8udxP2JBkTDA6HAy0tLWBZFkePHuWL+1gZ0EUDb8f/y95Th66ORZSUlMBoNMLpWsbysgXd3d0wGAzQaOX+b4ZYKMDkkK9x6Ufveg++d/+fPP/w+v37kzHvqs3nL5DDVTmQioWwO90oz9RhIEIiJirDXqyRD4LQN2+Bg4FELIDTGcV5iOIaJ1hrjdqEGsZnPwRQJsmwYrbzCiUiAAQUBbXDCuPYPFIzfMmYzLwUjA34mcxSFEiygo/2jrUqBgCEXoRTQIvS2hhCwXt7mmEhEFBwBmlFo4Kok4RmJ8C12cVJGaOyMNgMFaNUS2G2OD3nbjOtV9boV4AnRxeQVqaDWi7BSPMk0nLX+79pN4nbd+SDDT6qmKLxwOdObMNAQsM/DcJ/VVWpVPLFILdKt7q6inPnziEzMzMmRMzc3BxOnDiBqqoq/PKXv9wRIuarX/0q/v3f/93ntdLS0h1TbyaQQDjELto68mvtkg9eAKVGDpZlUVNTw08cjUYjhoeH+Ym+wWBAcnJyVBOCmZkZdHV1oaioCNnZ2Xt+MuFyudDe3g6Xy4XDhw/HrPUkWngT726328cHg6Zpn4n+RvddzmS0pKQEWVlZ2/QJ4geGYdDW1gan07lrPG84XyBBG41LL7006thsru2lrKwsJu1wOw2GYdDe3g6n04m6urodJ2L8IRKJkJqaitTUVJ/2s+HhYXR0dPCx9Dqdjldccd5RBw4cQFpa2pbH0NXVhRMnTuCuu+7CF7/4xR25d+7n+mlfkDFc1KlarQ7ItY+VzDZS+Dv+p6WpUVBg4IsHxq7E88+0AAA6OjowPhGodrn8WCVee66D/3degQ4ZucFlp8SLBKEIwcc+cjH/b6lEhLrKbEyPL+C73/gAPnT1D+AI4iHjjaJiA3qaIzTsxZoCgyu0glyclJsFxRBk5GowMjQXxX4ju9C3qobxh1IhxorZziskWCGNrBQppv4yCQBYmPc1W1YmB65wEBpA8vrqnUO7iRv7RsoYaiMyJvR7vbcvztXD6HRhwWwPOGhA0rXdtX6uNzjGZnEgkijrIKAowGx1eQbNEEAY3eAEdhb0xh7PQZGulMNt9VxXDuuacTW7ZtpLNrnTKBBWBUUIPnPTxUhS7kyRHgzesar5+flwOp18O9PY2BhomoZarcbCwgIyMjJi0oe+sLCAkydPoqioCI899tiOeIhxqKiowEsvvcT/eyfHkkAC4RArMoYOs1jjj6s/fjkIIbxRr1gsRkZGBjIyMsAwDE/MNDc3+3hkqNXqkCv6hBAMDw9jZGQkZgaWOw3OT0UikaC+vn7X3EeEQiE/cfSO9e3p6eEn+gaDATqdLmDSOz4+jv7+/pi1VOw0XC4XT0jtpnPEgaaoqGOzZ2dn0dHRgcrKypioLXYaHFnmdrtRW1u764gYf/i3n9lsNr6dqb+/HzKZDEqlEnNzczhw4EBM0uF6e3tx/PhxfOxjH8O99967oyT2fq2f9uSn8P4hzMzM4Pz58ygoKAgqZxQKhSCE8ORIvBDO8f/w0fVV3YxsLZRJChw4cABlZWXoM4zhD4/28X/Xpylw4kOVWFlcxRt/6YdIKMDXvvePmJqeAEUFhvwQr0lnUboWGo1vH+qHrqlBfq4ONE0jI0uD4QEjQiE1LRlTQ0awIdN2/I4NeFQS3HceJNVJ4GBAAcjM1oYnY/zfu8G1His1jD9USTLIJCJYps1YnFtFVlYyRt8eAnd7lknkADymwCKJAEt+5AwAEKkY1Jp6xCWjwcgiWxWkGALawYKR0hvyHNzfCSFeHifeG0TmGSMVCSANYRbsYyK8ZtoLL/PpWJMxQiENa3fkhB28fnaGtGRML9tAuV0bxn4HPfYmVDEcpgbnYF7zVjIvevxiaAfrOQc7rIypyk3DlRcf2IZBbB7eky2WZTEzM4Oenh5QFIWJiQlYrVbeBHgzK79LS0tobGxEZmYmfve73+34yqRQKIzJSlUCCWwG0RTSsVrMEooiKzXLjhShsDovpFGvQCDwmegvLCzwKSGEEJ+kH25RjmtfX1hYQENDw7a18cQTZrMZLS0tvLHubm218o71LSkpgcVigdFoxOjoKDo7O3mDUr1ej8nJSYyPj6O2tnZfpFrZ7XY0NzdDLpcHLBLvFggo39/NRrHZQqEQLpcLhYWF+4LQZBgGra2tYFkWtbW1e3JiL5PJ+Fh6t9uN0dFRDA8PQyAQoLe3F3Nzc7xqZjNE08DAAI4fP46bbroJ3/jGN3ZcTbhf66e998tbA+dKPjw8jKqqqpAMLXcDZBgmbg8sjuxhWc8quH8hUXZgXcanMyRBoZTw2+Xlr/9NIKDwwX+uw/j4OJL0blAUhZtuO4yhkT4sLy9DZ0iCadYM79mXd5vSh288GjC2kqL17yUzRxuSjJHJxYDTDdtqYCS1z2f1PnqYiGAAoNwE1JowICtbE3a/Gx7LCyxNgRXTMVPDeGNqfBFpSXIszq2irNiA872joMXrl4nNvG5orNEpwfq1XhFCQGQSftzOCLxiaAcLkdkNoZX1ZPhQa+qaMLAs25CTrsb07AoIgqgvwnw1QooCd5ZZFwOpaOPrgvZuT+IOwWwyjSkEDmbqMNA1sPGGQWCaM3vGRxA1GUM7GE9L0SaxvGjlf6vzJjOEOgng4vYXfzaGhLgOJDSN//fpa+J+/FjC4XBgcHAQmZmZ/KqPt+mgQqHgi/dIWhRWVlbw/ve/HykpKTh9+nRcEj+iRX9/PzIyMiCVSnH06FHcf//9yMnJ2elhJZBAAGLlGUNFWHodu+2KiOs076QfTrpvNBrR19cHh8PBG4VPTU0BAI4cObIrrv+tgvOByM/PR15e3o5PjiKF90S/sLCQv7cbjUb09vaCoihkZmZCIBDEtK7YCayurqK5uXnXmw8LNviOvWOzR0dHMTAwAK1Wi9HRUYyOjsYthnk74Ha7+TjpmpqaPTf+YDCbzRgdHeXT1FZWVmAymXjyk1M5ce1MG11jIyMjOH78ON7//vfjgQce2BW/4/1aP+3JX5/b7UZbWxuWl5dxwQUXhF3p8CZj4iE/8/aHodYkf4Fj8Goloihke7UcSaQiyBUSWFcdOHAgHZVVJcjI0aK05ABWl1+EUueE0WhEUlIS9GnKNTLGC2sXk0osQn1tXtixZoQgRCgKyEpLxkDX1MYfeE2e40lJ8v2sPq1Fa6oYDlnZ4d39iT97EOQmwU1rWWl8Vhiy09VwLtsxPGhCTo4Wo2c7wBaoQTHrZMfS3HrikkIpw+ywL7lFpCLP98KQ8KoYQiC0ekgYgdN3wk4RBElH8sXoyDwAoPxgJro6As9bwPfpBe+z5ra7IFZJuTf5Yc0zhmFAh+qEYgHE4HSolFJMvDG2qfcSCnBzq06E+Ch/IoFwdesrv4TynDeWJZDTQtiwfa2RQYk7QvDFD18OhXzn+9Mjhc1mw7lz56DX61FSUgKKonxMIjnviLm5OTQ3N/tMxoIVg6urq/jgBz8ImUyGJ554Ysf8FLxx5MgRPProoygtLcX09DT+/d//HRdffDE6Ojr2xYp9AvsLsWhTstlsmJmd2XA7TZoaF13bsKlj+Ev3LRYLJicn0dvbC0IItFotTCZT3CKYtwsTExPo7e1FeXl5TNoPdhIymQyZmZmYn5+HQqFAVlYWFhcX8fbbb0MkEvEGwOHaz3YjVlZW0NLSgvT0dBQXF+9qUslfGRMKIyMjGB4eRl1dHdRq9bbFZscLHBFD0zSqq6t3pWopWnCm1yUlJcjMzAQAPv2sqKgoQOUkkUh4Mk2j0QRcYxMTEzh27BiOHTuG73//+7viGtzP9dOeJGOsVivcbjeOHj26oeScoqi4+cZ4EzHebUkbISdP5/NvbYoCSqUYA51T0KclAwDEEiEuuCINSUlJKCkpwfz8PNQpoSerxy4v3/C4mdnaoK+Xl6djsHNjIsYTX02DuNnghr1eH78sT4+poXnYrE4IhTTS/YxvI4KXfyxLUx4zXRfZnEHrBigvSsVIzyycTjdSdEqYz4/C5XCDlYo88c9rsK86INMmwbbqgFgigNVsh0Qth8PmXktQknsMfEGCesVQbgKRxQ2hhdm0RwmHtHQVerqmg/8xzNfjdjEQCii4GQKH1QmZJlTcsefLF64EqmI40CwJMJHeDPIVCgxaQrfQhQMBy197FEuCe+iEAOViQbtioF5Z+60SAI4gEdjxRDAypr4oE5ceKdrWcWwFNpsNTU1N0Ol0KC0tDXov5UwH09PTeS+Cubk5vhjUaDQ4e/YsTpw4gezsbFx//fUAgKeffjruMaKR4uqrr+b/u6qqCkeOHEFubi4ef/xx3HrrrTs4sgTeKdjONqWlpSU0NzdDptg4PeaqWy+LuJ0pHCiKgtPpxPT0NHJzc5GRkREQwcz5zMjloZ59uwucEpxr49Foolca7zY4nU60traCoig+XjwnJwcMw/DJMd7tZ9yK/m6eOC8uLqK1tZVXLe12CKnw3yUXmT4+Po66ujokJ3vmJ/7k5+rqKubm5nxis/0N+XcLOB8foVCIQ4cO7erfU6TgiJji4uKQptfeKifva6yzsxNutxtCoRDnz5/HtddeC4qicOzYMVx++eX4r//6r11BxAD7u37ak2SMSqVCfX19xNvHI1Fps0QMAGTn+apEtDolVhdWwSRJIRILeRfsrKwsFBUVgaIoZGVloe5IOf72SqC5roAlOPa+kg2lnZk5gWRMSWkquppGUXthIZr/toFxKk0BoADRBu1JhOALd1+Nr9/zR4yPLkCZJMXSsink9p43Bb5E1lQ4rJgGEdKgXCxi3fZBU0BlURq62jwx3jK5GNL5ZczOeRRIjFQIyq8VSaWVw7bqgM3qiSjXp6kwMTwPViwEJB4Cxi2jfRQ8/q1IsYBCKQU7EyJ3aCP5qUgEC+OE1exAyKBQEsS0N2CbrZ+PLIMKQ6+PbOq9BGstVHIJT4hQQZKhQkG06o7N+VhLniICCsT7+NtQg/iTYTKBAN/812PxP3CMYLfb0dTUBK1Wi7Kysojupf5eBFarFQMDA3jyySfxta99DXq9HjRN4xe/+MWOxIhGCrVajZKSEgwMbK49L4EE4gmuZWQznntTU1Po7OxEcXExjOmBQQXeEIoEuPKWS7cw0nVw6hFOqg/AJ4KZa40ZGBiAQqHwiczeTZNGDizLorOzE0tLS2hoaNhytPdugM1mQ3NzM5RKJSorKwNCN7iJvLcCY2BgAB0dHdBqtbxqZqf9v7zBkUd7KQVKGOaaJoSgr68PMzMzqK+vD/u74xSsubm5cDqdfJrW6OjopmKz4wWXy4Xm5maIxWJUVVXtCyJmeXkZLS0tfEJcJPC/xsxmM15//XU8/PDD+OIXvwiDwYDU1FR86lOf2jVETDDsp/ppT5Ix0SKWZAxn1MuyrI/jfzSQSH0VE8XFBjzx6zdRUpERtJDgkB0iUakkS4Wu7k6gG/xDKiUlJeAiyvBTxmRkqjHW65EP1xyNhIyhAYYJPTlf+x4uqMlFapoKen0yxkcXIBJTWDHPh993sN2xxNPmw6keuIl/KDOZKJGkkCBdreCJGJqmkCWlMXx+lj8MKxWCdfiuDMoV4rW/e8bDJSqxyXL+fQ6tyNOKtMpAZGY8ip4Yoqg4FQNhzJg3+n6kYgEsdmB1xRZSSUIRBJr2+m/DRG+W64/kFTeWo2wt4kBoQOgm4K/uKNqUKDfrMdqNEQh8080AQCQVxb1hifW+nRCCez52BcTivXFrt9vtOHfuHN9bv9nJkFwuR1VVFV566SXcdNNNmJqaQmlpKW688UYAwLFjx/DBD34QJ07sbMS3PywWCwYHB/FP//RPOz2UBBIIANf6F43nHiEEAwMDGB0dRXV1NfR6Pd4Wtod9z0XvPwxNqnpLYyWEoL+/H1NTU6ipqYFWG7j4JJFI+NVh78jskZERSCQSnphRqVS7gphxuVxoa2sDwzA4fPjwnm6x4mA2m9Hc3IzU1NSQKkgOwRQYRqMRk5OT6O7uDkj62SlMTU2hu7t7zyUMiejgtTwhBN3d3Zifn0dDQ0NUCjKxWOyjYI02NjtecDqdaG5uhlQqRVVV1a4mGSLF8vIympubUVhYGDER4w+KopCcnIyrr74a9fX1eP/73w+5XA6tVouLLroIer2eN/C94IILYvwJtob9VD/tjYrdD9E+JGPVprSRUe9mccWJQ3jxyVbIlDQGBgZCFhJZuSmBggdC8NlPHUNmpjogQpBLFtDpdBAKhZDJxdDqlFiYs0ChkMBpsfNR13lFBmj1SVgwmQOOC6zpUQgBRTzfQ1BPF8pjH/LZz14FANCmeG7gEgmNiy66AD+TdcBuCx+tzX0m2smCFdG+x4mhJ2pmWjIYsxND/euERnmuBj0vrUeKExHtacuS+F4mdocnMUcm8ZAwNE2BCAWAVOyZkAspCO0sRHMu3sA4lqBpCmaLPfxGG/wuJSIBpGIhnA43XI7gps20PTwRA4SI1Y4CQaOsWdZD/G0AmqIAf8NpEnmbktDKxE64QuCRWfmZ6UYT6bqpw1Lw+a4uOpCLozX5cT1mrOBwONDU1ASNRrMlIoaDy+XChz/8YUxOTuLll19GSkoKGIbBW2+9haeffhqvv/76jpMxn/vc53DixAnk5uZiamoK9913HwQCAW644YYdHVcCCQRDtJ57brcb58+fx8rKCi644AJ+NV2wwXPkmtvfu6VxMgyD8+fPY3V1FQ0NDRFNzL3bHhmG4VfzudYZvV4Pg8GwY6v5NpsNLS0tkMlkqKmp2Rer+AsLC2hra0NeXt6mzIcVCgXy8/ORn5/Pq5w41YxcLufPWSTm7rHC2NgYBgYGUF1djZSU8N6Iuw3iIG1KnBJrZWUFDQ0NW/J/CRabbTQaQ8ZmxwtOpxNNTU18stV+IGJWVlbQ3NyMgoKCmBjYLiws4OTJkygoKMDjjz8OkUgEu92OV199FU8//TTa2tp2nIzZz/XTniRjokUslDFcWxJZU2fE8mI2pCfj2o8cRH/nTNhCQiYXQ2dIxqRjfSKeoVYie03x4i3bN5vNMBqNGBoaQmdnJy/tTM9SY2HOgovfU4bnH3+b348uTYXsfF1IMgYAwHjiejOztZicXAq6yTVXlEMuF8Nut8Ph9LTQpGfoIJVKoVbLMWMLL1em3Cxop6eVh/VLBuaUMRQhYQ1qN0J5USpGe2fh8FK8VJYY0POnVp/tWKnn8mAlvg8sicTD5jvWiCXGxYBNkoGhWTiSBRC5APFKbNvivHGgPAOdG3n8uMOzQCIBjSSZCGYAlmUzAL84bUIgcGxMamxkNBwOQiENa0+QKGs367kzbXCNEUIgtPuRrCRCgoghENhipJYDQIGADaIYs9tdgDz2xuH8sb0OqRQKcd+dV8btWLEER8SoVCqUl5dvuXB2u9249dZbMTg4iLNnz/JFsUAgwIUXXogLL7wwFsPeMiYmJnDDDTdgfn4eer0e73rXu/Dmm2/ui5jQBPYGornWuFCCSBazuChfgUAQ4OcnEIa+lxfX5aO0oTDiMQU7bmtrK4RCIQ4fPrypoAaBQMCrYjg/KqPRiK6uLjAM4xOZvR2pK8vLy2htbY1IPbJXMDMzg87OzqCq783AW+XkdrsxPz8Po9HIm7tzKvF4kWmcj8/ExATq6uqgUm3CF3GH4a+MYVkW7e3tsNlsqK+vj6kSyz9Ny99QViqV8sSMWq2O2W+eI2IUCgUqKyv3BRHDqcvy8/ORm5u75f0tLS2hsbER2dnZ+O1vf8vfQ6VSKa666ipcddVVWz5GLLCf66c9S8ZQFMUTIxthq/GM3kRMrNQwHLhCIrtQhSuuPgqFIrwcMCs3BZN9k/y///HauoBtONlZcnIyioqKeGnnxMQEIPAQOQcPp6L5LyqYppdB0xRSDEnIyteh7e/DQY9LeXaMjBwtGm88gh8/8HzANpnpKnzs45fCbDajpaUFqelqABNIWmvjUWnkmJkOQcaQtYhhb78N/9O7RWUMTQGVxWnoap3web2oUI/eFwJl1OxaOxkRCUBoip/ki4WeItO6YgMroNHvdMCSrwCjFEO4aA9IR4olFAoxRkc3aPlysxBsoJwRCWiIKM84ZRIp4Hb6fK8cIbYRvIkPwhJQXsoQ8YwZLrUMRBr8NlOZqcNgkChrimVBXASQhH5oFhTp0e+TZOVxz6UAIALPGKEtRl4x60cPGrVO4lxEMyKKH8DXPnkVROH8fXYJuOIoKSkJFRUVW76fMgyD22+/HZ2dnTh79uyufjD/9re/3ekhJJBAVIikfuIMJPV6PcrLy30mPB4lbej3Hv/E5lUxKysraG1tRUpKSswihL39qEpLS7GyssJ7zHR0dCAlJYVXHMejzYLzHiksLEROTs6+IGI49cihQ4eg0+k2fkOUEAqFSE1NRWpqqk9rTFdXF9xut48BcCxSVQkh6Onpgclk2tBPZTdDQq/XZgzDoK2tDS6XC/X19XFJn/WGt6Gs2+3mDWXb2toAICax2dyiD1dr7BcipqmpCbm5uTExiV5ZWcH73/9+6HQ6/OEPf9jVrZD7uX7as2RMNNhKm9JWjHo3wmYKiZz8FLy1RsZIaRrvu6Jiw/d4SzsnBwnmptsgkbuhNghhmgaUKinsdhuy8sM8JCkKEqkI3374w5gcXwi6yYkP1GFxcQHt7e3Iz8+HKkmAP/ymA8lrZIxaFZpoojdSMxCypfYkhVyMLK0ygIjJyFBh5o0eECZQScLK1i8PViyAYE2FYbc64ZaLMKqTwV6qAbwMVEUrboAWxcTYNhhUGiGmJm1htxEu20BtcHjG6QBh1lrGGL/fNEvWzJI3hjd5JnAwPt+ZYNkOycQyLJWpnshvL6iUUkyGiLImoEAxrOechGjzWZ1d9PV7Ifz/2bg1jCUQWmOoXKICvWL4YQUhaGIGAjASz/4vP5SPmordbxrIETFKpTJmRMydd96Jt99+G6+++irS0tJiNNIEEkgA2FhZPD09jY6ODhQXFyM3N5e/pr399UK1a6r0ybjo2sObGpfRaERHRwcKCgp8jhtLUBTFx8Nykdkmk4lvs1Cr1byiJhZxvuPj4+jv70dFRcWe8h4JBc4/aHJyctvUI/6tMZxKfGRkBJ2dndBoNLxqZjPnjGVZdHR0wGw2o6GhYVcbxG8E8VrrGxf1DAB1dXXbov7yhlAo5K+jWMVmc8EAKpUqJrXGboDFYkFTUxNycnKQn7/1dnSLxYLrrrsOcrkcTzzxxK6PJN/PeMeQMdEqY7wLiXgQMZstJLJy1wmTSw8XRj2m3AIDLrvyIOrq6jD9Hjf6215CklqCt956C0th1BQUBXztxzdCkSRFdp4Oa0FHPPKrMmBcWkJb2zzKy8uRnp6OqclFAIAyyXOBq9TRPbQoP0KD8no9GqojI1UFsurEYJ+v4a1KLYNraAb2EFHEjJeig5UIQNndcKUnoUMtgiszNcCXRbhoh4CK3yWVmpaM2ZnVsNtQdhdo+8a+PG6nExqlEoswwem3Pe2I3EuFSy6iXCwEDhas1ymmnQxoBwPpxApsRb691AVKBQbCRFlTAOB0g8gCVx7zC3UY6p8JJJzWSJiN2pQENmZDsioasBSCqmI8g4ndcfxBKAACGsliMb68Rc+F7UCs5cIsy+LTn/40XnvtNZw9exaZmZkxGmkCCexfxMpzz9uo99ChQzAYDD5/8/bXE4qCK/auvOUyiCTRrcATQjA6OoqhoSFUVlb6HDfeUCqVUCqVyM/Ph81m45OZ+vr6kJSUxE8oo/W/8DYfrq2thVqtjs8H2EawLIuuri4+BWonDHb9VeI2mw1Go5GPYE5KSuJ9ZhQKxYbXBqcecTqdaGho2FVpTpuBVCDkE4ZEItGuiHoOZtpsMpkwMzMTcWw2Fwyg0Whi0ga9G8ARMdnZ2SgoKNjy/qxWK66//nrQNI2nnnoqKpPmBGKPPUvGxLNNyb+QiCURs9VCgovFpgjBrf/8rqiPn5mjRXqWBgBQUZMHAMgrzMCll16K0eEJ/O5HrYFvoijc8un3orjc0+crk4thSFNhdq3liBJQmHM4sLg4j8ZrLuPNh3X6JACAMskje1Opo7zYSQT/vQEOFBow3m/yeHd4QSwWQm13YHKNMAoGzjMGbhaUUISVS9ID/GN89rnCAEEM0WKFpGQZZmfDePoQAuFSeNUMB1VSEgRrk2GHzQmsKVooN+vbKrYBODKGdjIB76M5Tx4//5osgwqDYaOsvbyBXIEmwu6FZRCFFNSKH3nI3Q/CkTGEQGiNXb6RUCSAWCqEzRX8/kLiqIoltCfx6v/deTUEcTYK3iq4gk8ul8eMiLn77rvxwgsv4NVXX41J33QCCSQQiGCLWZxh7vLyMo4cOYKkpCT+b8H89YRBDHwFQgGu+uhlUY2FZVl0d3djbm4O9fX1SE5O3sQnig1kMhlycnKQk5MDp9PJEzNDQ0OQyWS8+mIjM1mGYXyUFjuZChQruN1utLe386TFbml9kMlkyM3N5SOYOc+S4eFhSCQSnpgJ5lnicrnQ0tICmqZRX1+/7eqReEAEGufOndvVxrZcbHZeXp7PORsdHYVIJOLbmThvIJvNhqampi0nNO4mrK6uoqmpCZmZmSgs3Ly/Fge73Y4bbrgBTqcTzz333J5ts9tP2Pt3kwgQjTImnka9LMv69JluppDgyJjSLB3f/hMN0jLU/H/nFhkgk4uhT0uGQCBAQVEu1FoFlhZ81RdFB1OQWy6D0WhESkoKBAIBcgr0PBmTUZaCIaMVxUW1PilQYrEQKpUMSbwyZvNkjI9KJgKuIJQ/DOARtBSkyDDw196w+6AASMctEDkBW6E2LBEjWrCBjiMRU1hkwEB/mChrAAKzHdRau9VG6d8Ciua9Veyra2QMS0A7olOQUcyaka6N5YkZzwAIKKdnXxTrS8aozAyWwxE+XgOnXIynBWjtOswr0GG4axLQKgN+B7zBc5h9C+ws6BglXCWrZFBLhBhetoZOr4pjmxIrpHB1QzEqStLjdoxYwOVyoampCVKpNCYFH8uy+Ld/+zc8+eSTePXVV2OySpRAAu8kbGUxK5xRbyg1MR3EwPdoYz1S0jURj5mLeXa73Thy5MiuktSLxWJkZmYiMzMzwExWKBT6TPK9739OpxOtra0AgMOHD+95pQXg8eloaWmBSCTa1aSFWCxGRkYGMjIywDAMFhYWYDQaec8STn2RkpLCEzEymQwHDx7ccfVILGCz2WBZXEZSelGAz9Nuhfc5Y1mW95nhvIHUajVWVlag0+n2FRFz7tw5ZGRkxISIcTgcuOmmm7C0tIQXX3xxRwntBNaxO++SMYZAIIDdvkEUMOLrD+NyudDe3g6Xy7WlQkKukEAqE+OjN1+05THRNIWSykzo0tYvxqx8nQ8Zk1tkwJceuA4mkwl9fX1wOBzQ6XTQGjzjF4ppzFo88cJJSYErOjp9Em/gq9ZER8ZQgEftQFF8C4rn9fBFpFwqQo4+OSgRAwAVhTr0PB9o2OsPySILam1Fj3aHV1uIzGzcVDEURcFqDR4/zcPNgPZut6IQlrSiCAHr9hTYNqsDFGSbat+h4GkLEthZXxLFub4vb3KkLEeHsZeGwu/Um4QDPO1K0rUidXkFJJj3EFl/X7g2pVipYjKyNHCazBibMwNJIa5lQgA6fkVbil6Bz3308rjtPxbgFDESiQRVVVVbLvgIIfj617+O3/72tzh79iyKi4tjNNIEEkggGLzblJaXl9Hc3AydThdgiBmufhIGMRY/fvsVEY/BarWipaUFCoUC1dXVu3aCDwSayXKT/PPnz4MQwhMzMpkMbW1tUCqVqKys3BcTfKvViubmZt6nYy9M8AHPb5wjXwghWFpa8ql5ASA5ORllZWX75jw1NTUhSSLbs34qNE1Dp9NBp9OhrKwMJpMJnZ2doCgKMzMzsNls2xKbHU9w5ykjIwNFRUVbPk9OpxP//M//jOnpabz00kv7oh1yv2D3PtE2QDQ/ykjalOJJxHCFhFwuj8lKwbsvKsbBytiYdZZWZUGXum6qlp2vQ0fTKAAgvzQV//6jG6FMlkGj0fAGdkajESKpC0qVCO+6Og9PvuwxFBYF6QvX6ZM2r4wBeIlHpMqYNH0SaAeDgd7ZoH8/ECTCOhQor0IinNpCtGiPqyrmQEUGujaIshYu2XyVMAI6bLy1bdUKrPESLEMgZgjc4QincMe2rXnMkLXWIopab1ECeLWOUEjD3hvc/NkXfkwSSwA3i9xiA0baRoA0TXCD5A3alGgHE55UixAlpamYaJ+A3eYCow8j74yTiTMACAQ0vv7pY7u6iOJMAcViMQ4dOhQTIuZb3/oWfvGLX+CVV17BgQMHYjTSBBJIIBQ4ZfHMzAzOnz+PoqIi5OXl+dx7ODVMqPpJ4EfGFBzKRdkFkRGpi4uLaGtrQ3p6OkpKSnb1Pc8f3hNGbpLPRWY7nU7I5XI+TnuvT/KXl5fR0tKCjIwMFBcX76nz5A2KoqDRaKDRaJCWlsYbzrMsi7/+9a9QqVR8C9pe9NngvEfS09ORwTB79jx5w2q1oqenB5mZmSguLobD4YDJZILJZMLAwADkcjlPzKhUqj3xma1WK86dO4fU1NSYEDEulwsf/ehHMTw8jFdeeQUpKSkbvymBbcOeJWOiwUZtShsVEltBPAqJ22+7dOsDW0PZwSyotOsPlOwCTyysUiVDzdEiKL1aoSiKQlJSEliWRUauAp+651LoDRq8/KYRllUXhocHkZkmgMFg4B9SOkMSb+CrjtLAFwCUCjEsNpePMiYUGVNWYMDkoAk2W3AD27y8FAy9fD7yg3udq5BkTJxVMXKFGONj4QkMyub0IT8AgNA0KIQmY9xON1aXrGsbE7BmB6hNkoRCy/q1laZSYHrF6tvutPbdVWbqMPhiYJT1RqAAwOUCZbGA1ST5vu4N4vU6SwJahISrW0tQoiigojwD3X8bAAjAykQh0568xxNraJJFuPWfqqAQu+F0OneltN3tdvMS/VgpYr773e/ixz/+MV5++WUcPHgwRiNNIIF3HqJpUxIIBJibm8PQ0FBQo15uIQsI7a/n36Z0ze1XRFQLTU1Nobu7G6WlpcjK2v1pceHATfKdTicmJiaQm5sLgUDAp/xotVp+kr9bPFYixdzcHNrb21FYWLhv/LsWFxfR2tqKvLw8nny02+38JL+/vx8KhYJXOoUyk91NWFlZQXNzM28CK+837fSQtgyOXPJWj0ilUmRnZyM7O5tvGzSZTHxLIBdzvpXY7HiC871JTU2NybzR7Xbj9ttvR1dXF1555RXo9foYjTSBWGH3/QrjgHBpAPEy6gXiV0iIxbE7bSWVmT5FWXa+DjRNQZeqQklFRsD2JpMJ58+fx4HyIj4FqrI8C2++PYy0VD0WFhYwMDAAhULheUAlizdt4KtTy5BdkYm3z434KGN82pfgaVuqKklHV9tESDGC3pCExZYhMM4IvYMAn8l8KEWFaCG+qpiCAgM6OiZDb8CGMO2lqbC+MTKJFHbiaX1ixQKQLTyQBF7fjVouxfSKFZSfMiZZIcHkm+M+7yOEBL/egr1EgJHBeSBNvfZm/y2IjyKGYohPrDTlYkFHGNcdDBKJEPmZanS/PsAfnpVvQILEWBkjoCn88w0X4KorSnziVVUqFb/qI5fLd7wg5BQxAoEgJukMhBD86Ec/wn/+53/ihRdeQHV1dWwGmkACCYQFwzBYXFyE3W7HBRdcsKFRbyh4K2OStEpcfN0FYY9LCMHg4CDGx8dRXV29L1ZxCSEYGxvD4OAgDh48yJNahYWFsFqtMBqNmJqaQk9PD6++4NqZdjO4OpdL0dwP4OrckpISn9rde5Lvcrl4b6CmpiYIBAKeTNNoNLuuRWtpaQktLS3Iz89HXl4eAEAm2H0LOdGAI2I4Y9tgtY9326B3C9rAwAA6Ojqg0Wiijs2OJ2w2G86dOwe9Xh8TIoZhGNx5551oamrC2bNnkZaWFqORJhBL7FkyJpofaDBlTDyNevdSISFX+q7AZOXrUFGXh8U5Mypqc3z+Nj4+jv7+flRUVCA1NZV/vawkDW++PYyMjDSUl6XD5XJhbm4ORqMRZsssmpvfXmOi9aBpCuwG0cMAIKIo/MePb8Iv//ctz6TW/y1rTINMKkKeQYXOEP4wAKBQSCCcWcDiQvhYaB/4/b6CKmM4VUycfEEMhiT09EyH3UZgtgf3SKEoD5kU4rt2Oxmsrtg9hKQydg8g6dp34a2MoRiCwqQkDJj9VmEsqyBKvzhJQkISSJRQ6Gm9CmIGCfi1svl9buGqe9Mp09oUBRQE6G8eWx+mWAgSIq6VH8IGf48GpUUGfO3LJ3jT7qSkJBQUFMBut/PpAoODg5BKpT5y3O0uCBmG4RMnqqurY0LE/OxnP8P999+PP/3pT2hoaIjRSBNIIIFwsNvtaGlpAcMw/Mo/h1BGvaEg8Lpnv+8j74ZEFnoS6J8utB+SPggh6O3txezsLOrq6qBSqXz+LpfLeQUGp74wGo3o7+/no3wNBgOUSuWOk+0cCCEYGRnByMjIrq9zo8H09DS6urpQWVnpU+f6QyQSIS0tDWlpaWBZFouLizAajejs7ATDMHzKj06n23H1xfz8PNra2lBcXIzs7Gz+dYVw75IxZrOZj3qO1NjWuwWtpKQkZGz2Tl1rdrsdTU1N0Ol0KC0t3fLxWZbFpz/9afzlL3/Bq6++iszMzBiNNIFYY8+SMdHA3zMm2kIiGuz1QkKlUeCrP7zB5zshhKC/vx9TU1Oora0NMH06UOphWsViz8RLJBIhPT0d6enp0GmzIJayawZ27ZDKBLCubmCiSgi+cO8JaHRKT2sTCa7wSNMrIXAS9PfMhNyVUEgjTUgwOhylHJP2J2Pgo8YBANG8HXQcDVpVGgVmTZbQ5ISLAW1xBP0boQAqDBnjtLtgW3V41B1BzBU3C8Ha4Xw8Y1iCkTdGAzd2OAGBAJBHtvJHAaDNdrAaeaDqxMvAlzsm90/KzULg2JwqJicvBeaxeUwtWH1eZxQRFDExuK1IxEL86ycvx6XvKgn6d6lUiqysLGRlZYFhGF6OyyVCcAXhdshxOSKGoqiYETGPPvoo7rvvPjzzzDM4evRojEaaQALvbGxU86ysrKCpqQkpKSkwGAwwm8383zbjr8fdC2iawtUffU/I7RwOB1pbW0HT9L5JF+JiwFdXV3H48OENlS7+6gtuYWtkZAQSiYRXzOyk94U3uVRfX+9D1O1ljI2NYWBgIGpyiaZppKSkICUlBWVlZVhZWYHJZMLQ0BA6Ojqg1Wp3TH1hMpnQ3t6OAwcOICPDV+2+V8kYrt0qNzcX+fn5m97PRrHZ3DnbDqWT3W7HuXPnoNVqUVZWFhMi5u6778aLL76Is2fPIicnZ+M3JbBjeEeQMd7KmHga9e6XQsL7O2EYBp2dnVhZWUFDQ0NQV/LiIgNomoJYFPhzysjSgqIo6PV6sCyL3z82AuvqYtjjnzhehYZ3FQEAVCpZUOVHfV0uelsmYNsgZag0U4XeVzrDbhMMuaWpGPIK4KKw1voiXPtuWALRavy8YgoK9ejvC25CDAAgBIIla+j5PkWB0FTIvy+ZzCA0BaKIbW86WWsDo52+hJtEJIDb5adOc7o844uQjAHWzIDtTiDIb82boPH+zQitzKZ4kQMH0jHUNAL32mfi9kiENIgk/rfOI3W5+PJnr464LZGTSRsMBhBCsLy8zCtmzp8/H9eCkGEYtLa2ghCC2tramBAxjz32GL74xS/iqaeewiWXXBKjkSaQQALhwBn1FhYWIj8/H+Pj43z9tFl/PU4Zc+R4HfTZwSe5ZrMZra2t0Gg0eyZqdyP414QikSiq93svbHFku9Fo5NWH3P1+O9tiuJrQbDZHRC7tBXBq9omJiaDKpWhAURRUKhVUKhWKior4FjROfZGcnOyT8hNPQm1mZgadnZ0hVT57kYzhEt28261igVCx2Z2dnXC73T5Kp2iv443AKWI0Gk1MIrlZlsVXvvIVPPXUUzh79iwKCgpiNNIE4oV3FBkTT6Pe/VhIOJ1O3vAqHLkklYiQn6sLmqbk/R3TNA1tihITY6HJmOy0JNz8ifWJl0YlD2hRKqvKRJJIuCERUxlFchIHQ6YGSrkQAx0TQJGvzw/lJiBrV0w8vWIoioKdU5aEiKimrU7Q4fxvKAS0WnljddkGNlkWdpvNwGH1mCdTDt+xSSRCrPqfL5cLxOUCpdNGvH8KgGDVCSY5yPXlrZbh2spYAoEtOuNemqZwoDQNPW8M+u6e9hyfjZTA2uRXm6SU4N8+fzWqKjbvM0VRFNRqNdRqNYqLi2G1WmEymTA7O+sjx9Xr9Vs2HuSIGJZlUVNTExMi5vHHH8dnP/tZnD59GpdddtmW9pdAAglsDEIIhoaGMDQ0hKqqKn7yxnnusSy7oVFvKAjWyPNrQsRZcx4deXl5yM/P3zWtOFvB6uoqWlpaYhbz7E22B2uL4e7nOp0ubslMLpcLbW1tYBgGDQ0Ne3bB0RuEEPT09MBkMqG+vj7manbvFjSn08kbAA8NDfFtxfFQOk1OTqK3txeHDh2CTqcLus1eI2M4IqagoCCuRtH+sdlms5lXzHR2dkKtVvv49G0FDocDTU1NUKvVKC8v3/JvgBCCr33ta3j88cdx9uxZFBdHllqXwM5iz5Ix0XrGuN1uuN1uUBQVcyJmPxYSXBy3UqlEZWXlhg/3spJUvk0pHNRhTHylQgr/8NEDeO2116BWqz3mdXKhj8rh1D/W4yO3XIyf/vCVsMcpKdKj57m2DcfDQaaQIL/EgJ6/D2LWxQDywAk3bXeDlQriroo5UJ6Orq4wXjEMC8GKPfTfgXXPmBBgxQIQaWzZfQBYnF8BJAS0X6x20N+Gyw04/QiaCDxvKQLQ1iCJWX5tSgAgtEbnFSOXi5GpUwYQMRxYigIrjc9tkwJw1RXluPO2y2J+D5HL5cjNzUVubi4vfeeKC6FQ6CPHjaaQZxiGL85ra2tj0gr1xBNP4I477sDvfvc7XHnllVveXwIJJOAL//sL1169uLiII0eOIDk5mf8bVz9xRAxFUVHfnwRCGrkVWai8uMzndUIIxsfHMTAwgPLy8n1jLskl8WRlZcUkltYfwdpijEYjb0rKtZfp9fqYreJzHkJSqTQmpPtuAMuyPrYC8Vb5iMViZGZmIjMz00fp1NraCoqioNPpYDAYoNVqt/T9erdbabWhF7uUe4iM4QyICwsLt7XlhqIoJCcnIzk5GYWFhQGJWluJzeaIGJVKFTMi5v7778ejjz6KV155BWVlZRu/KYFdgT1LxkQKQggEAgGEQiHeeOMNpKamwmAwIDk5OSY//P1YSCwtLaG1tTWqOO6ykrSgbUr+CJWoRBGCb373H1FUmg673Q6j0Qij0YihwRlQhIAWUrjr8+/FpZdVAACSVaHbLLKyNJj4S1fEaTZl1VmY7JlG5+t96y/6r2IRAlm3Ca50JViJKG6qGJlMhImJJc8hgaDKGMFKCNNeb1CeeOtgiLVprzdWzU7QCDyuOJiZrcsFuNyediVxdAUj5WI8sdJcohbra/xLsQQgBEJr5KoYQ2oyBFYHhtpDm0ETqShiNRGJ4v6Sqk/C179yAtlZkauENgtv6Tu3wmoymdDd3Q2Xy4WUlBR+hTXcyifLsmhvb4fb7Y4ZEfPMM8/gtttuw2OPPYbjx49veX8JJJBAeDgcDjQ3NwMAjh496hOrTAiBVCqFxWLB22+/zSszgrUrh4NAKMDx29/rU0uwLIve3l4YjcagXnR7FVxryHbFcfu3xayursJoNGJsbAxdXV3QaDQ8MbPZ9lSLxYKWlhZotVocOHBgXyi/uYUEp9O5Iyoff6UTl/LT29sLp9PJP4ejJdSGh4cxMjISUbuVUrg3ItQXFxfR0tISYEC8E4gkNluv10Or1YatiZxOJ5qampCUlISKioqYzEe/853v4Cc/+QlefvllVFZWbml/CWwvKEIinLHuMrAsC5cryOq4FzijXi41iWOh5+bmIBQK+RuhWq2O+kLwLiQOHTq0bwqJ2dlZdHZ2oqioKCr2eXJqCSkpCkgl4R8av//fv+OxR14PeP2WWy9C4z8cDnh9ZnYRn7/rd/jIxw+BECtkMhkMBgNazpnw2C/eDNheo5WDHjdheWZ5wzHnFBrAOByY6As0ACZJcrC56+QatbwKwaIFAOBOUYLVhjGso6hNxxpXHszio6wJACKgQHslOVEON4RzoU19OTAqmadFxxyooGFkIpA4kTEAABUNYbvvd6qvSMOscd1smBACcr7bY86ckwlKs1Y0sCwoNwu4N/Z5IRQ8BsAAWKkQtH3dp8apkYCViyA2b2AWvYaCIj3m+maxGkZxxNAAo0sKqzjy2V5IgYjCF600TeHGDzbgQx8M/O1vNwghsFgs/KqP2Wz2ic32noCxLMsXsrW1tTFZfX3uuefwT//0T3jkkUdw/fXXb3l/scC3vvUtfOlLX8Jdd92F733vezs9nAQSiAkYhoHb7eaNMDUaTYAC1ttfz+1280ay8/PzUCgUMBgMSE1Njcj3wjQxj2StEpI1xanL5cL58+fhcDhQXV29b3xHRkZGMDw8jKqqqpCtIdsJm83GL2wtLy/zfiXREGrc4lxWVlbI+OC9BpfL5ZP6t9NpR97wfg4bjUZYLBZeKa7X60NeK4QQDAwMYHJyEnV1dRGZKi877VCJdz7OORwWFhbQ2toaEDO+28CyLO/TZzKZYLfbeZ8+nU7nQ4RyRIxCoUBlZeWWyU1CCH74wx/igQcewPPPP79rUicT9VPk2D13oCix0QMhmFEvlzXPsixPzLS1tYGiqKiM0LwLif1iYAYAo6OjGBwcRGVlJQwGQ1TvzcxQIxJeL1ib0gUNeUGJGAAw6FX4xa8/BpFIwLPQHkItsI1HIhUiyWzF1AZEjEqrQGqGCn1vD4Ucs7+qhFpZT9OhXNF5kEQKnS4Jvb2hk6E2NO313hTBrxFCI+amvf4QuwD/7CK5VALAK/nJzfCKH2K1rZMxUYAiAGGJhxzxO40UQyBcjew8VZRnoO+tIbBM+MQlIhVFTMR4BhH+z4X5Onz9KyehCdO6t52gKApJSUkbxmanpKRgbGwMDocDdXV1MSFiXnnlFdx888146KGH8MEPfjAGn2brePvtt/HQQw+hqqpqp4eSQAIxBUVRmJ2dRXt7OwoKClBQUBCgWvH215NIJHx7hdvt5ieKIyMjkEqlfP0USnGsz1o37bXZbHy7S0NDw66aCG8WLMv6+I54t3ntJGQyGd+eyvmVGI1GDA4OQi6X8+ctlG+Y0WhER0fHrlAkxApcu5VMJsPBgwd3XbuV/3PYZrPxE/y+vj6eCPX2e+PSrYxGY8igjWDY7W1KXCR3WVlZQBLUbgNN00Fjs6enp9HT04OkpCTo9Xqo1Wr09PTElIh56KGH8K1vfQt/+tOfdg0Rk6ifosPefwoGwUZGvTRN86u93kZoHR0dIITwKwcpKSkBF8p+LCS8Ywq34iQfyYqJSu1LXNXW5+KL3zgVcnuapvmOIaFQyBNqLocSzz21HpdMUUCqiMVU51TIfQlENEoqszDUOoLeibnwA/WedJutoL0m6pR7czHJG0GrU2Bu3rL+gt/XSa86A7xYQoKiPMoRbxACVhF7015/KGgaZr/XpFI/Ashb1Wa3A9NGwOUGpVUD0ciFWRag6IAIbzEBmA1auYRCGqUFevT8bWDDwxBEYdy7AcQiAe742KV47+UHYrK/eCFYbDaX5AEABoMBCwsLW47Nfu2113DDDTfghz/8IW688cZdsfJqsVhw44034uc//zm+8Y1v7PRwEkggppiZmUF7ezsOHjzo015NCOEXsoDgRr1CodAn4YdTzDQ1NUEkEvGKmWD+CZzKIi0tDSUlJfui3cXtdqO9vR12u31XL855+5V4K53OnTvHnzdvpfjExAT6+vpQUVERNIlnL8JqtaK5uZk3S90Lvz+ZTIacnBzk5OQE+L1x8ctWqxWrq6tR+94IdvHnn5ub4yO509PTd3o4USNYbPbs7CwGBwd54mZxcXFLSWiEEDzyyCP46le/imeffRZHjx6N8afYHBL1U/TY+0yCFyIpJPzhb4S2tLQEo9GInp4ePs4sNTUVKSkpfGLSfiokGIbB+fPn+Rv5Vp3BN4K3Z4xCLsYn77piU5MvtdqX+S/NUWHgle6Q2xdWpGN5egldr/dGdgCvc0svr/r8KR7KmPwCHfp6g0RZc18Nw0KwYot8h0HSlOJl2suBpikUF+qx3D8dQMYECEqc62QMJRACZs93TNwMqCi4GAoedQzlp3BKEomw5A59npKSpdApJOj5+3BEx2GlIo9HTRQIIMMA1FZl494vHINkg3a+3QaBQACdToeZmRnI5XIUFxdjaWlpy7HZf/vb33D99dfjwQcfxIc//OFdQcQAwL/8y7/gmmuuwRVXXJEoJhLYd9DpdLjgggt8Whm81cScSe9G16NAIAiqOOYMSTliRq1WY3Z2Fl1dXSguLt5WA854wm63o7W1FSKRCA0NDTGPvI0XhEIh0tLSkJaWFqAUBzxE/OrqKqqrq5GSEjyKfK/BbDajubmZr993y7MmGgSLOu/r64PdbodAIMDg4CCvXt3LC8VcKMp+8eIUi8XQ6/UYGxuDTqdDVlYW5ubmthSbTQjBr3/9a3zpS1/CU089hYsvvjjOnyJyJOqn6LFnr1b/GylXSHBtJ5tx/KcoykdmxjnUczc7QgjS09NRVFS0L4gYp9PJ980ePnx4WwoJrk1JKKRxx11XQJ+6OTlvUvI6+19RYkBviAjrJI0ECrkQA28PRXcAjj2w2ALVKGEm+ZsBRVFwucIrXgTLNlDR2NCspSkRrBEWcTTtlSvEKMjWYLZjHIMvdQQfjv8LnDJGIQPlRcxsKg6akLVzsv5my5IV4hQ5nI7Ac5WRqYZrfhVjY2ESq7x3D4BRbE3Oq5CL8aXPXIW66r05CeFSJ1ZXV1FfX88XF8XFxVhdXeVXfaKJzf773/+O6667Dt/85jdx22237Zri+Le//S2am5vx9ttv7/RQEkggLhCJRGGJmM3UN6EUx+fPn4fb7QYhBPn5+bva9yEacKa2Go1mz6gsgsH7vDEMg/b2diwuLkIkEqGtrY1P+NHpdHt2gs+lW3ER07vlWbNVTE5OQigU4uKLL+bbmQYHB9HR0eGzQOJtyr3bwd0zKisr940iy+Vyobm5GRKJBIcOHeKvuc3GZhNC8Pjjj+Nzn/scTp8+jcsuu2ybP1FoJOqnzWFv3ln9EItCwh+cQz3XA80xmisrK/jzn/8MrVaL1NTUmEYHbidWV1fR0tKC5OTkmPQtRgpOGXPs6oO48LLSTe8nWeUhYwoLdOh/sT3g71K5GAUlqeh5exArm1GyrH0f/qoYAKBYFmKRAM4YKWTKDqSjuzsEMUABIoYFZQtvVh34PmrtfwAIwMrFgDC2vdGpqcnQKUQYeWsQPV2hE4gABLQREZfHWJcSiADWSyWziXFQAOBmfdKVWJYgw5CMsfFFn21LSlMxcX4SDqszcEchQMQCIFgaVEQDA654dyk+c8fmFGC7AYQQdHZ2wmKx8ESMNzg5LudLwKUL+Mdmq9Vqvphvbm7Gtddei3vuuQd33HHHrvluxsfHcdddd+HFF1/cdPJIAgnsJQTz19sqOMWxWq2Gy+XC4uIitFotpqamMDY25tMKvts8OyLB/Pw82tvbkZOTE+C3s1fBqaRtNhsuvPBCSCQSmM1mT6rl0BAfmc2du+1OH9osOJXFbjeAjQZutxttbW1gGIb3bZNIJFCr1fwCiclkwtTUFHp6epCcnMz7zESbhLadmJ2dRUdHBw4ePBi1b+VuhdvtRktLC8RiMU/EcPCPzbbZbHwbmn9sdlJSEn+vfOKJJ3DHHXfg8ccfx5VXXrlTHy0Aifpp89izaUqAR9nBpSXFspDgwDAMurq6sLS0hOrqan4liYsOnJ2dhcVigVar5ftt98IDilslyMrKQlFR0bYXEj/8j+dx5+e2fgO586O/xGLTIGx+7TvFBzMwMzCLFW//lSjBpqeAyCQQGJd9I5MB3PnA9TjzSg+GBk3B3xxFmpJUKoREKsbycmALEqE9aUqS8WVQG5jL+sOtUwIUBcG8BQABo1XGxCuGooCCAj3oFSuGm0Yi/pzlFxShY3Ce/zc7OgEwTIDqSJqXAQcEEaUp+YPQlE97WVlDHrq8DJErKzLQ/beBALPfjeDSyEEk0fPW2iwVvvrl48jP3flUjc2CI2JWVlZQV1cX1Qqbd2z27Owsbr31VpSVleHIkSP46U9/irvvvhtf/OIXd9VE5o9//COuvfZanwkiwzA8ye9wOPbk5DGBBLxBCIHT6YwLEcPB6XTyca+HDh2CRCIBIYRXHM/OzsLpdO455cXU1BS6u7tx4MCBXW8qGim4dCGKolBdXR10gZGre00mE1ZWVqBSqfi6d7f65ExPT6Orq2vfqSxaWlogEAhw6NChDa8Zh8PBGwAvLCxAJpPxE/xgvk47BS4SvqqqCnq9fqeHExO43W40NzdDKBTi0KFDUdUO3rHZPT09uOuuu3DxxRejrKwMP/rRj/Cb3/wGp06dit/gN4FE/bR57Gkyxm63b2shEQw2mw2zs7MwGo1YWVnhI+gMBsOuZAa5G15paemOrRJw52ur+OIH/wv9zSM+r6lSlMjITEbXm4Nb2jebqQdlc4J2rkcjCwU0vvrrj+NAQz6+8bWn8NabIVqfoiBjvKOs/UFogAhpSIYXoiImCABG7yEOBQurYOXiLXvFiMQClBTosNg/C+OQMer3lzUUoGtkXaXC9g+DEghBuX2jp1Mq87FgcQMME11bFtY4Fi91zIG6HHT2e8aarpVDpZCg/3zw7zoUWCHtIbY2gaee+dSuKXQ2A0IIT0bX19dvSerMsizeeust/PrXv8bzzz+P2dlZvOtd78LJkydx8uRJlJSUxHDkm4fZbMbo6KjPax/5yEdQVlaGL3zhC6isrNyhkSWQQOzAsiwcDgfvr7eZtu5wsFgsaG1tRXJyMioqKoIW4FyEL1c/2Ww2pKSk8Cv4u01xTAjB0NAQxsbGUFVVtW+8VLhQCi7dJZLJkt1u55OZFhcXoVQq+bo3kqjz7cDY2BgGBgZw6NChfXOunE4n3+5SVVUV9cTWe4JvMpn4dhmDwQCtVrtjrXbT09Po7u7eNZHwsQCniOHi07dCQjidTrz88sv41a9+hbNnz8Jut+N973sfTp48iePHj+8aUjhRP20eu38ZIgRee+013H///WhsbMQ111wDrVYbs31HUkhwkMlkfB+q3W6H0WjkfWY4aaDBYIi7Me5GIIRgZGQEw8PDOHTo0I7e8GJ1w88tMgSQMUevPgi31bFlMgYuNygvIkauEOM/n/5XGLI9D3W9YevRlSkpCvT1hY6ylkgEsDNr0c0bJAP5wKsOolxuEOnmZamEAjRSAdiZJfT2R+axEgyMn88OJZGAsjuCbLf5pCrOzBcCzxdgWVpTG9ldmB1ZgJECyuty0dU0GnonfmA36xVDRZYutltBCEF3dzeWlpaiVsQEA03T0Gq1eO6553DLLbfgE5/4BJ599lk89dRT+Ld/+zeUlpaiubl5x1dNkpKSAgoGhUKBlJSURCGRwL7Bww8/jJdeegmNjY1473vfG9PWBa6FJzs7G4WFhSHvg94RvkVFRbzyYmxsDF1dXbtKccyyLLq7uzE/P4/6+nofv529DLPZjJaWFt6/ItJnllQqRXZ2NrKzs+FyuXhiZnh4OKKo83iCI83Gx8e3lA6622C329Hc3AylUrlpawHvRFKWZfnAku7ubrhcrk0ZyW4VXCvVfiLNGIaJGREDeAyAxWIxXn75Zfz0pz9FXV0dnn76afzqV7/CJz/5SVx//fX4zW9+E6PRbx6J+mnz2LNkTGFhIS688EI89NBD+NSnPoVLLrkEjY2NOHHiBPR6/aYfAJEWEsEglUr5CDqn08kTMwMDAz4rB0rl5lbaNwuWZdHT0wOTyYT6+nokJ2+dSNgNKKnLxkuP/93ntQuvqcZQy8iW902t2ECteayoNFLc+7+3ICVDzf/dYNigGItAHaPTJ2N+ITQZo5aLMWN2gNAUqKjImLXfLCFgZZt7oBKaAiuiIFhchWXEPxsperi9iC3iZgCXO+h2NocbAA3e7CZaEOL5H0VhenwelFAAysnwf+ppn4RILIDLubHfD6EpT4pShPiH6xtw5skWuBzuPWvmCKwTMQsLC6ivr4+Jwm9gYADHjx/HTTfdhG984xugaRq33347br/9dp783mkiJoEE3ik4evQoent7cc899+DjH/843ve+96GxsRFXXXXVloiG8fFx9PX1baqFR6FQID8/H/n5+bzimJuo7aTi2OVyob29HS6XC4cPH96ViufNYGFhAW1tbcjNzUV+fv6ma2aRSISMjAxkZGTwUecmk4kn1znlxVYifCMFIQS9vb0wGo2or6/f9lo7XrDZbGhqauLNomPl66TVaqHValFaWsobyY6MjKCzsxMajYY/d/H6zU9OTqK3txfV1dUxXVDfSXBEDNfyF4u65rXXXsOHPvQh/OhHP8KNN94IiqJw4MAB3H333TCZTBgbG4vByBPYSezpNiXAc/MdHBzEH/7wB5w5cwYtLS248MIL0djYiJMnTyI9PT3iG9dWColw8F45mJ+fh0wm4yMflUplXFcO3G43b8pWU1Oza3t7o8Xy8jJe//ObePjTL/GvaVOT8dBfv4LWV3vwjRt/vOl9E4kIUMhBO93IL0vFRx+8BvPzc7Db7byMur9vGf/xwPPBd+BFhoRCbp4Oo6PzIf9emKfF/KwZCwwD8eQy6AjIA378NAUmRQnabAMBQJIiO+cEABFSYEUCAASiyUUIzPaIjxsOuuxkGO2ehxKZnAG1GiKmuyAbFAuAYQPiqiMF164kkYvgsLsDOJ3cghSMDYTw+/GCWykBq9xYESIR0fjaN9+P8opM3HjTz7CyaINIIsSZM/+yqfHvJAgh6Onp4VeAY1GEjYyM4KqrrkJjYyO+//3v72miKoEE9hNYlkVzczNOnz6NM2fOYGxsDFdccQUaGxtx7NixiD0lCCHo6+vD9PQ0Dh06BI1GE7MxeiuOl5aWeMVxampq3OsZu92OlpYWvi1kL3jaRILZ2Vm+XT0zMzMux/BO1DIajWBZNq7GzSzL8v5mtbW1+6bWXV1dRVNTEwwGA0pLS7dFacQlM3HXHLeYrNfrYzZnGR8fR39/P2pqamJ6v9hJMAyD1tZWsCyLmpqamNwvXn/9dXzgAx/Af/zHf+BjH/vYnlZcJxAae56M8QYhBKOjo3xh8dZbb+HIkSM4efIkGhsbkZ2dHfSHHM9Cwh9utxtzc3MwGo2Ym5uDWCzmC4tYSzrtdjtaW1shEolQVVW163qwN4u5uTm0t7ejsLAQ37v9Dxhba/U5dvNFuPW+UzCOz+P2w/dFvV8uBprNTgVtseGiy8vw2R/d7PkbIbyM2mg0YqDfiN/9bxjPGM+bQvwdyM5Owfj4QtA/SyRCZFLA8IoNjFIC0cwKBLbgSpKgn0NAg1HJIJhdAlFIwKrCS9AJ5fGmYUW0pyXK6YZ4bB60I/JjbgRNWhLmGSGI0wWMTAT1wCEiAajcLFBudktkDOAhpIoqMzHQFdhalZmfhKnB5fDvpwCXPmk94jwEMtOT8J0f3AS53COjv/2Tv8bk6AJkSgke/93tmx7/ToBbVeQUdLEoZsfHx3HllVfiqquuwo9//OMEEZNAArsUhBB0dHTg97//Pc6cOYP+/n5cfvnlPq3gweoT7wWf6urquLZkeyuOFxYW+EliampqzFNiuBYenU6HsrKyfXPv4rxUDh48uG1GqYQQLC8v8+fO4XD4GDdvtTZlGAZtbW1wOp2ora3d8ba2WMFsNqO5uRkZGRk7ErYBeK45Tu00NzcHiUTik5C4metibGwMg4ODqKmpgVqtjv2gdwDcb5BhmJgRMX//+9/R2NiIb37zm/iXf/mXBBGzj7GvyBhvEEIwOTmJM2fO4MyZM3j99ddRXV2NU6dOobGxkZdlLi0t4Vvf+hZOnDiBmpqabfV2YRgG8/PzvEO9QCDgCwu1Wr2lC89isaClpYWXNe6XQoJLMqioqEBaWhoe/tqT+L9f/hUA8O0nPoWiqmwQQnBDwWfgtEcXB00UMsDpAluSjasuyscn/v39IbednprHxz/66+B/3ICMOVCeETrKGsDBQj0EFjtaxxfAKiUQGS0QrEYRxSykQQgBbXeClYnBaoJLdQkNsCIBiJDix0xZHRCPLUSd3rQRtKnJmGMFICMToJzBzwvJTvN4ycSAjKFoCurUZCyYAhO1Cg+kYqgzvP8NIxeDSQ6jCiEEV1xZirvuutrn5S995TQ6Wieg0srx2K8/tqmx7wQ4QpqTd8eCiJmensaVV16JSy65BD//+c8TbUgJJLBHwCnkOMVxR0cHLrnkEpw6dcqnFXxwcBC/+tWvcPLkyW1f8Imn4phb8MnPz0deXt6+mAQRQjAwMIDJyUnU1NTsmJcKZ9zM1b0WiwUajYZvQ4vWnyySJKi9iOXlZbS0tPBtZLsBDMNgYWGBP3cAeFItUrXT6OgohoaG9hURw7IsWltb4Xa7UVtbGxMiprm5GSdOnMA999yDT3/60/viHpRAaOxbMsYbhBDMzs7iiSeewJkzZ/Dqq6+ioqIC733ve3H69GloNBo8/fTTO3pjYFmWv8kZjUZQFAW9Xo/U1NSoe225XuCcnBwUFBTsi4uYMyAeGRnxSTJ4+6VOfOu2R3HJqVrc9Z838Nt/5or7MdIZeXIOoSiQTD0EAhruJDn++38+GtaklxCCD5z6EVyuIO1DYcgYiUQIuUKCxUVr0P2mp6mwdG4Y2kwNJhkGrFwM4dwqhOZAs9uQY2NZUGspGaxECDZl/XMQeOKyiYgGEfr+pujFVYiml6JOMYoEimQpVlgKGJsKrk4DQA4WgbY6QTEEYNnofHKCoKgyA/09swGvaw1JWJwJrYwhAFx6pSeZKQgENHDHnRfj6EUVUCh8C8fvfv9FvPJCF/TpyfjFf39kS+PfLhBC0N/fj5mZGdTX18eEkJ6dncXVV1+N+vp6/PKXv0wQMQkksEcRrBX86NGjOHLkCB5++GFcdtllePTRR3f0Gg+mOE5NTd2UiezExAR6e3tRXl6O9PT0OI56+8CyLLq6urC4uIja2tqYq4i2ApvNxte9y8vLUQVfOBwONDc3QyaT4eDBg/vmObO4uIjW1lYUFhYiJydnp4cTFMHUTikpKbxqJpg6iQsRqa2t3TfGyizL+qiyYkEGtre349ixY7j77rvxhS98YV/M4RIIj3cEGeMNQgjm5+fx/e9/Hw8++CAyMjKgUChw8uRJnDp1CgcOHNhxFYm3y7nRaATDMPzDSavVhn3gcMqRsrKyuPUCbze49onZ2VnU1tb6GAyumm345Lvvx/ee/xw0+nXS4T9v/wVef7I58mMopBDmGECkErhcDH59+hNITg6vDvj4rY9gejrIpD4MGRMuyhoACpMloB1ujA7Nw5GeBCIVQbhohXApMu8WQghYuRiCFQ/ZQ4QCMAaVh+wQebUi+b4JwtkVCOcDVSSxgkBIw7m4DMod3PuGGLQgGTrQK3YPCRMDMgYAiqqy0B+kVUmlkmFlcdVrAGvHoigwEiEYTfAiUJ0swle/cQ0KC3OD/v13v38bjz36N2Tnp+DHP7ppy+OPN7jV0unp6ZgRMXNzczh27BgqKirwP//zP/vGZyGBBN7p4FrB77vvPvzP//wPiouLoVarecVxqFbw7YS/4lgoFPL1UzjFMUc6jY+P49ChQ/vGUNTtdqO9vR0OhwO1tbVbTsaLJxwOB692WlhYgEKh8Am+8D53VqsVzc3NUKvV+0r9zamy4unnE2twbfzcuTObzVCpVLxHkFwux/DwMEZHR1FbW7tvQkRYlvW5tmJBxHR1deHqq6/GHXfcgXvvvXfH76cJbA/ecVUyRVF47bXX8N3vfhff+MY3cOutt+Lpp5/G6dOn8Z3vfAc5OTk4efIkrr32WlRVVe3IDd7f5Zxjn3t6evj4udTUVOh0Op6Y4eL8xsbGUF1dva8i4jo6OmCxWHD48OGA9glFkgyf//E/+xAxAJBZlBrVcZLzUlFSk4vmtfYVaQQpOnpDcnAyJgQUCiF6e0O3x5QXGzDwYifKGgoAzPOkDoniN0hkIp5sIQDcGhlYMQ0iotdJIm8wLEQTixBYYmPUGwpuh8uToBSqENap43LchZllSKRCj5GvFwyZKl8yhhsHACYpSLFKCA6UqfDxT16M/PzskMfLzfEU8HLZ7u9Z5yYfU1NTMSNiFhYWcOLECRQXF+Oxxx5LEDEJJLDP8Nvf/hZnzpzB6dOnUVdXx7eCf+UrX0FNTU1AK/h2g2v3NhgMvOJ4dnYWbW1toCiK/5u34pgzf11aWkJDQ8O+SeFxOp1oaWmBUChEQ0PDrr8fSyQSZGVlISsrCy6Xi/cqefvttyESifhzJxAI0NLSgrS0NJSUlOybCavRaERHRwcOHDiwp1RZFEVBqVRCqVQiPz8fdrsdJpMJJpMJAwMDEAqFYBgG5eXl+yYWnmVZnD9/Hna7HXV1dTEhYnp7e3H8+HF8/OMfTxAx7zC845QxTzzxBG6++WY89thjaGxs9PnbysoKnn32WZw+fRrPPfccDAYDT8zU1dXtOPNOCIHZbIbRaMTs7Czsdjt0Oh30ej3m5+exuLiImpqafXOzc7lcaGtrA8uyqK6ujsqU7a9/PIfvfOLRiLYtOVyIwRUXfvK/t+Fr9/wR42MLeOL5T2/4vu995wW8/FJX4B9CKGOKSgwY6A+e4qNQSCCZWYRr1QlKLIbd5oQjSwUiEoA2OyCeCyQO/EEogElRgrI6QC9b4UpLBhtG3UM53RDF2Kg3FFjLKuAK4RWjUoLke9LL6GWbp00qRsoYACivy0Vn24TPaxU12ehqGvUahOdYjEQARutXiLME15zIxuEL8mE2m8GyrE+ftHeBazKt4JYPP4Kahlx87aunYjL+eGFwcBATExOor6+PiWx9aWkJJ06cQHp6Ok6fPr2rV2ATSCCB6PH1r38dDz30EJ5++mnU1NTwr3Ot4H/84x9x+vRp/PnPf0Z5eTkaGxtx6tSpXTFh9lYcz87OghACvV4PrVaLiYkJvs7YL/ctTjmSnJyMysrKHa9ftwJvrxKj0Qi3243k5GQUFBQgJSVlT382DtPT0+ju7kZlZSUMBsNODycm4FqgJyYmoFarsby8vO1x5/EAR8TYbLaYETEDAwO4+uqrccMNN+CBBx7Yk99LApvHO46MWV1dxeDgIKqqqjbc7k9/+hPOnDmDZ599FiqVim9lOnLkyI73pnKywOnpaYyPj4NhGGg0GqSnp4fs19xL4CIlpVIpqqqqov6+hzsm8Nn3fmvD7SouPoCutnGUvacS3/zxTXjwm8+i+e/D+N8n79jwvf/z2Bv47W/eCvxDEDImJ1eL8fHFkAFL+ToJJs9NoLQ2F73npwAA9hw1IKBBrzohNm7cQsQkSUCkYlCrdjAKEVhlaANaatUB8XjsjXqDgbgZELM59FjK88GIPQ8zesnmSVqKIRkjENDQZ2sxPbHIv5ZfkoqRHi+V0tqJcemUICKv35qLgdRsxU+evh06nQ6EEKysrPASeKvVCq1Wy8c+isVinDz+A7zr0hJ84fO+5r67CRwRU1dXF5NV4JWVFZw6dQoqlQpPPvlkTCKxE0gggd2FsbExCASCsO0ThBAsLCzwxMzLL7+M4uJifmFrN7SCc34Xk5OTmJ72PAc4819vxfFexcrKyr5UjphMJrS3tyMrKwuEEJhMJrjd7pCLI3sFExMT6Ovrw6FDh/aNqp1rgZ6amuLrDO+4c5PJBIZhkJKSwp+7vWC+zLIsOjo6sLq6irq6upjMtUZGRnDVVVfh1KlT+N73vrfj98cEth977661RSgUig2JGG676667Dtdddx1sNhteeOEFnDlzBtdffz2kUilOnDiBa6+9FhdeeOGO3PwpioJQKMTc3BzUajWKioowPz+PiYkJdHd3b8mdfqfBJUFptdpNF24ZhQZQFIVQXCNFUyi/+AC6mkYhTJLiE3dfBQDIyU1BZ/tE0Pf4wxDG4Nf3YJ7/E4qIycpUYfKNQQDA3NyS1/vW2o0EGxdShKZApJ6HAquQgFWGfkAIFlchnFoKGi8dDxCbLfQf5RIworXrx+cLouBpGto6GIaFWESDAgFZ+9Qzk4u+hyAes2OeiCEEmQYljM3jUKYmQ6fTeUZFUVCpVFCpVCguLub7pKenp9HT04Pk5GTQQhpS6e4t5oeHhzE+Po76+vqYEDEWiwXXXXcd5HI5zpw5kyBiEkhgnyISM1GKopCSkoJbb70Vt9xyC5aXl/HUU0/h9OnT+O53v4vs7Gw0NjbuaCs4RVGgKApzc3PIzMxERkYGjEYjBgYG0NHRwU/u9Xr9npvcz8/Po62tDQUFBcjLy9vp4cQM09PT6Orq4pM0AaC0tBQrKyswmUwYHBxER0dHwOLIbod3upBGo9np4cQE/qEAnPKWpmmkpKQgJSUFZWVl/LkbHh7mzx1nALwb6whCCDo7O2GxWFBfXx+T39f4+DiOHTuGY8eOJYiYdzDeccqYrcLpdOKll17CmTNn8OSTT4KiKBw/fhynTp3CJZdcsm03f7PZjJaWFuh0OpSVlflcwP7u9CqViidmYhFZG08sLS2htbUVWVlZKCws3NKKzm0N98I0sRDwukgqQn5tAfrXSJf3fugobv/3UwCAN/7aj1/+/C/46S9v2XD/8/MWfPHzv8eMfzKPnzKm7EA6enpmgu6DpinkiAWY6pmGLkODubUoZgLAke/xH6GcDCST4b1pCAUwOk97GiOmQcRByABCIJxZhnBh45anWIG43CCW0KoeWVUhVul1AkSwvOZdwxJQbGxVO2W1OehuXzdP1huUmJtZ8RyaYeE2JHnIGJZAsGTFnd84gp9/5U0YclLwg8dv33D/nPngpz/9JKoOaXD5Zbl8UahSqXbF6iRnoldXVxeTdkar1YrrrrsOLMvi//7v//aN10ICCSQQe/i3guv1er6Vqb6+ftsmIiaTCefPn+fTarh7M6c4np2dhdFoxOrqKr9yvxcm9xxhsZ+SoACPImtgYGBD5cjq6ipf+5rNZqjVav7c7bbalxCC4eFhjI2N7StTW0II+vr6YDQaUVdXF7EXndVq5Q2Al5eXkZSUxJ87hUKx4/UTR8SsrKygrq4uJovc09PTuPLKK/Hud78bP/vZz/a8Ii+BzSNBxmwBLpcLr732Gn7/+9/jySefhMPhwPHjx9HY2IjLL788booUzm09Pz8feXl5YW9SDoeDfzgtLi7yN7jU1NSYGHbGEpx5WXFxMbKzQ5ukRoqv3fBfWJ4zo+6KSvzxv16E28VAqVYgpSAVY33rkccP/vFTKKjwyK4nJxbx4Nefwfce+qeIjjE3Z8aXv3ga01NL6y96kTEisQByhRjLIdKQKksM6HuhEwBQfqQQXc1jnrdSFBx5a6skDAvp2FLQ93MQiGk4VJ7Vh6BkDMNCNL4AwWrkEdmxALtiBpjgCUppBQZMKVXr6U4sgWBl7XsiJOYtVIpkKSAUwLJ2jJKKDPSf9xByjEQIRi0D3CyE8xZIRAL85E+fxMMPnsXy4iq+/pPIfg8A8NGPP4rj1xzC0QsyeBM7mqb5FZ+NEtHiBS4aPlZEjN1uxz/8wz9gdXUVzz333L4pJhNIIIH4I1QreGNjIy644IK43SPHx8fR39+PiooKpKaGN/r3n9zvVsUxl3I1NDS071pdhoaGMD4+jurqaqjV6ojfa7fb+XYY79pXr9fv+KIBpxyZnp6OWavwbgAhBD09PZibm0N9ff2mCTCn08nXTvPz85BIJPy5C5eIFi/Eg4iZmZnB1VdfjcOHD+PRRx9NEDHvcCTImBiBYRj89a9/xR/+8Af88Y9/hNls5nsAr7jiipgRH5OTk+jp6dnUygd3gzMajZifn+djA1NTU3eceeZ6ZiMpkCLF1OAs0gs87Urdfx/Ef997GlY7C+PkundIWm4K/uulu/l/MwyLr37xNL7+4AcjPs7wsAmf+pf/WX/Bi4wpKtFhoH8+6PtUKhkwbIRtxQ6KpqBOVWNxbk0ZI6DgyNHw+5GMLIZsK9JmaWASUWAtLlDwmNAS0foKo8DNQjBsAu2Mv1GvN4jTBbIaXIVDC2lIirKxKvV6qMWZjAGAsupsdHd4PHkqanPQdW4EhALcWgUolxsyhxsumxuGTBV+8sxdmJ1axBO/egO3f/FYxMf43N2P49TJGrzrXcWej+VlHGkymeByufjVVp1Oty190lyhXldXFxPSxOFw4MYbb4TJZMKLL74YVZGcQAIJJOANm82GF198EadPn8bTTz8NiUTCe/RddNFFMWkV4ibAU1NTUU/suTH6K45TU1NhMBh2tKWCUyLMzMygpqZm35DihBD09vbCaDSitrZ2S4SF0+nE3NwcX/tKpVKeVEtOTt7W2tebsKitrY2Jef5uACEE3d3dWFhYQF1dXcyUSFxUPUfOAOANgLdjYYsQgq6uLiwtLaG+vj4mRIzJZMKxY8dw8ODBROpkAgASZExcwLIs3nzzTZ6YMZlMeN/73odTp07hyiuv3NRDhYuhHR8fx6FDh6DVarc0Ri420Gg0Ym5ujn84paamIikpadseTv6R3PHsmT33Sjfuv+1Rn9c+8InL8aHPXOnz2qM/fw0f/tglUe375ht/hsVFq+cfa9+dRCIAyxK4XMFJhQsOZqL56VYAQEFFJob6jPzfWBENZ5aa/7dkZMGTMuSHpHQFVjVyWG0MBFY3KJb4kDHJQhr/+snL8cBtv4zq82wVhBCQFTMQotWo4qISnF9mAKHXg5RhITA7uB3EhYyhKCCnNA0jAyYUHkjHYMcEGLkYtMsN2uYCaE8E+KELC3Hvf90IALCs2KAMk0rlj28/+Cccv6YKFeWBJpeEEFgsFp6YsVgs0Gg0vGomHlLqsbExDA4Oora2FiqVasv7czqduPnmmzE+Po6XXnpp36zCJpBAAjsPp9OJl19+GadPn+Zbwa+55hpce+21m24FZxgGHR0dMJvNqKmp2fIEOJjimCNmtlNxzJmJrqysoLa2dtepnTcLLmp8eXk5phN7AHC73Zifn+drX+8odLVaHddWOZZl+Yl9rD/XToIjLBYXF1FfXx83cpJlWSwvL/P1k8Ph4BNldTpdzNsIvQmmWH2uhYUFHDt2DEVFRfjd7363J0yLE4g/EmRMnMGyLJqamnD69GmcOXMGExMTuOKKK3Dq1ClcffXVEU2OuAfT0tISampqYi5pZBiGJ2ZMJhNEIhFPzMTT64JlWZ8Vgu2Qan72xPd8UnS+88y/IrfUV2H0+mt9uOiSkqj2++C3/w+v/bnP84+17+vKq7Lw/HPBzYCTRAKUpyah5a/9AICSujz0efmZsGIBnJnrvw3J+BIoty85kXkgHbMCwLLWekTb3KAZAkYqABHSyE1R4Ls/+ie4nW7cXP/vUX2erYI4nCBWa9C/peUbMGt1g0nX+f7BzUJgiS8ZAwBpWRoYTWYok2VYXLGBCGkIV2zIrdBhtHcRIMCpD1+If7rrik3t/1ePvYH3XnEA6WnqDbe12Wz8is/i4iKUSiW/6qNUKrd87Y2Pj2NgYCBmRIzb7cYtt9yC3t5evPLKK9Dr9VveZwIJJJBAMLjdbvz5z3/2aQW/5pprcOrUKVx22WURTY6cTidaW1sBANXV1TGfsAVTHHPETDxrGrfbjba2NrjdbtTU1Ox6P5tIwTAM2tvb4XA4UFNTE9d2MJZl+chsk8nEx53r9XqkpKTEVHXBxSFzKTy7qc1tK+BaeDjibLtUYtzCFnftWSwWqNVqvn7aKtHFKZjm5+djRsQsLS3hxIkTSE9Px5kzZ/bNNZvA1pEgY7YRLMuivb2dJ2YGBgbwnve8B42Njbjmmmug0WgCJl8ul8vngRvvGzjDMPzDyWg0xm3VgGEYnD9/HlarFbW1tdt2A//rM6347qf/FwCQVWjA95/7bMA2JqMZekN0nhrP/ek8/uuHL3v+QVEQCil85nPvxQPfeiHI1gT3feU42v/ci2cfewOKJCmcLOByrnurMFIhXOnrcmPx5DJor7/nVmdj1OGE3e7iX6MdDGgXC0ZKo6JQhfcez+Z73L986mFYzcF9a2INjypmBQgSTU0JaGSVZGCUCAGp34PIzUBgcXI7iWvsdllNNjqG58DKRBBNL4MC8POXPoVPHvsJXE43vvDdf8DhS0s3te+zf+7FRUcLIRZHJz31VqvNz8/zpCjXJx3ttTcxMYH+/n7U1NTEpI3I7XbjtttuQ1tbG1555RU+0SKBBBJIIN7wbwVfWVnB1VdfHbYV3Gq1oqWlBUqlEpWVlXFvafBXHMtkMr5+iqXi2OFwoLm5GRKJBFVVVfumzcHlcvkQZ9upGuDizrna1+l0xqydmGEYtLW1wel0ora2dt9MwrmFYrPZvOMEk91u54mZxcVFKBQKnpiJ9trjWuRMJtOWvG+8sbKygsbGRqjVajz55JO7Mi0qgZ1DgozZIXDytz/84Q84c+YMurq68O53vxuNjY04ceIEdDod+vr68MlPfhL33HMPLr744m03eGJZFouLi5idneVXDbjCQqvVbpqY4VaqKIra9gcuw7D41JX/AcIS3HpvI+ouLYvJfqemlnDbRx/lVTEf/di7kJdvwL99+UzAtgeLDfh/37sBz//u7/jZ157CgYZ8dLd6FDQU5QlhYuQiuFLXCSHR9AoEdo/nS8HhfPQvWeBy+RrjUi4GAgeLq48fxO13vdenx/2/v/gC5sbNMfmsG4HY7SC24MRPxUWl6GybAFsU2MIDFwPBavzJGJamwOgUIEIBKKsTonkLrrzxMD7+hWvw4csehHnJhl//9QuQKzZXWCwuWqHRbE0uzpGinGqGEOIjx93oXsB5MNXW1saEiGEYBnfccQfeeOMNnD17FpmZQc5fAgkkkMA2wL8V3Gg04sorr0RjYyOuuuoqKJVKvPzyy3jggQfwne98B+Xl5dvuiccpjmdnZzE3NwexWMzXT1tRHK+urqK5uRkajQbl5eX7JgqXI5ikUimqqqp21NDUu52YS9XyjsyOhnRwu91obW0FIWTb6914gmuR45Q+u4lg4khRk8mEubk5iEQiXvGk0WjCXjPxIGIsFgve//73QywW49lnn9037WkJxA4JMmYXgBCCgYEBnphpbW1FXV0dent7cdlll+GRRx7Z8Rs4IQRLS0t85CPDMDzrHI2c02azoaWlBQqFYltWqoJhcsiE1GwthKLYHdvlcuGfb/oZzBY3UlIUePRXH0Vf7ww++5nf+Wyn1yvxve/dgGS1HB1/H8J9H/kFMovTMTkyB0OmGvWXlOD//vfvYBRiuAzrEmfRrBkCqwvF7ypE18wymCBEhdDFAA4W33/on5BX4Ns+8u1/+RX+/mJXzD5vKBBCQJZX+Fhvb6Tl67Ewa4YjQw+iDPIw2gYyhpEIwWjkfIKTcHYZcqkYj/3lC6AoCv/S+COYppbw+Nv/FvNjbxbcih236mO3230KQ/8iaHJyEr29vaipqYmJBxPLsvjXf/1XvPLKK3j11VeRk5Oz5X1uFj/5yU/wk5/8BCMjIwCAiooK3Hvvvbj66qt3bEwJJJDAziFYK/iRI0fw5ptv4q677sI999yz47G4HLnOLWx5K46DKaJDYWlpCa2trcjKykJhYeGOf65YwWq1orm5GWq1elcSTFarlSdmVlZWoFKp+Po3nE+Py+VCS0sLBAIBqqur901iDtdyZbVadx0R4w//VjSWZaHT6fi5i7eqbLOx3OFgtVrxgQ98AADw7LPP7lhyVqJ22t1IkDG7DIQQPPzww7jjjjtQWlqKjo4OXHDBBWhsbERjYyOysrJ2/AFMCMHKygpPzDidTv7mptPpQkpmzWYzmpubYTAYUFZWtuOfI1ZwOBxoaWnBk08MoKNjHt9+4DqUV2RidHQed3zyMZ9tj5an48sPXg8AWDSZce+HH8bU5DIA4DPfvg76dBW+dPPDcCdJ4NatmwwK51ZRXpeL82NzwXgOqEQCfPkrJ/CVz/0eP/3lLUjPUPv8/VcP/B+e/O/XYvvBg4C12QB7YHw2JaCRXZqBsUET2LKc9cQp722cbtDW9bYruJmQCVLRggBgVFKwcjF/bMrhQl6yDJeerMb7P3wRAOBLH34EM2PzeOSVz8XoyLEHF7dqMpl8CkO9Xo/l5WX09PSgurp6yybfgKeQufvuu/Hss8/i7NmzKCgoiMEn2DyefvppCAQCFBcXgxCCX/7yl3jwwQfR0tKCioqKHR1bAgkksLNgWRZf+MIX8IMf/AAVFRXo7OzE5ZdfjsbGRhw/fjwq4iOeY9yM4thkMuH8+fMoKiraUUI81uDqwrS0NJSUlOz4+dkIDoeDXxhZWFjgU0n9fd6cTieampogk8lw8ODBfUXEtLW1weFw7LmWK27uwtVPVquVX9jS6XQYGxvDzMwM6uvrY0LE2Gw2/MM//AOsViuee+65HU06S9ROuxv7o9F0H+GnP/0pPv/5z+Oxxx7DBz7wAUxOTuLMmTM4ffo0vvzlL6O2thanTp1CY2Mj8vLyduTBRVEUVCoVVCoViouLYbFYMDs7i6GhIXR2dvJ9tnq9nlf0LCwsoK2tDbm5ucjPz9/1D9xIYbPZ0NTUBJVKhQuOVsDt7kN5haeFQybzVTOVFOqxOr3E/1ujT4I+S4upyWWUVWfjoisrQAiB1pAEo5cXDACcuLYGT/ylL+gYqooM+PdvvB+0gAZNU5BKA1VUhqytT8w3AmHZoEQMAJRfUISut4ZAMvRBiRjPDuIzLpYCGJ0SxE8JJba58JVHb4UhU82/lmJIgsuxvRHg0UKhUCA/Px/5+fk+heHAwAAIIUhLS4NAIAAhZEvXGcuy+MpXvoKnnnpqVxAxAHDixAmff3/zm9/ET37yE7z55puJgiKBBN7BYBgGn/nMZ/Db3/4Wf/3rX1FfX8+3gj/00EP41Kc+hUsuuQSnTp3iW8F3og6haRopKSlISUkBIQSLi4swGo3o6uoKqTjm1I4VFRVITU3d9jHHC0tLS2hpadlTdaFEIkFWVhaysrJ8PIJGRkYgkUj4NrT+/n6oVCpUVFTsOqXPZsGZKzudTtTV1e24Yj9a+M9dVldXYTKZMDU1he7ublAUhezsbMRCo+BwOHDTTTdhZWUFL7zwwo5Hzidqp92NBBmzi/DGG2/gnnvuwQsvvIALL7wQAJCVlYVPfepTuPPOOzEzM4M//vGPOH36NO677z5UVlbyxExxcfGOETNJSUlISkpCUVER32c7NjaGrq4uaLVaSKVSTE9Po6ysbF95TVgsFl7pU1paCl3KMi6+pJj/u0Qi8vpvIZa7p5BW4fv5b7jzPbj4GhOKKz2vUxSFI5eX4annOvhtjh7Mws13vAdnz09gaWk9oUgooFGZqcHX7r8OgrWI6BSdEhJJ4GWdmr0NZIw9uE+MIVeH/tZxEABEFSZSNEQM9lbASgRwaxR8WxIPF4M7v3QMqVm+bTyGDDWEwr1TOHGFoUAgwOLiIvLy8mCz2dDc3AyapnlSNFqPJ0IIvva1r+Hxxx/H2bNnUVxcvPGbthkMw+D3v/89VldXcfTo0Z0eTgIJJLCDePjhh/HCCy/gzTffRH5+PgCgvLwc9957L+655x6+FfxXv/oVPv3pT+PCCy/EqVOncPLkSaSlpe1Y/aTVaqHValFaWsorjvv6+njFMQDMzc3FTO24WzA3N4f29nYUFxcjOzt7p4ezKYhEIqSnpyM9PR0Mw2B+fh7T09MYHR0FTdMQCARYWFjYksfibgFnQux2u1FbW7vniJhgUCgUkMvlcLvdsFqtyMnJwfLyMt58801IpVK+forW48npdOLmm2/GzMwMXn755Zj49sUSidpp9yHRprTLMD8/j5SUlLDbEEIwPz+PJ598En/4wx/wyiuvoKSkBI2NjTh16hQOHDiwK1YYrFYr+vr6YDKZAIBP9jEYDHveSXx5eRktLS3Izs5GQUFB0O/b6XTjA9f+FwCgqkiPnpe6cNU/HsHH7jkRsK03Ot4ewZe/8DiYZCned1Ex7vq8p6fz8dPn8MvH3gAASMVCNOTrMNU5hR88fRf/3q987nF87dvXQSDwffBPDZtw55X/uaXPHA6EYUBWAg2CKZpC9oEsjPdOg6SowKaH+G27GdAWp29bUgzalFz6QEUMAGTIxXjo958IeP3FM82wW504cdMFWzzy9mFmZgZdXV2oqqrii3dOCs+pZtxut48BcLhCihCC+++/Hz/72c/wyiuvoLKycrs+SkQ4f/48jh49CrvdDqVSid/85jc4duzYTg8rgQQS2EG43W4+3jYcCCEYGRnB6dOn8cQTT+Ctt97CkSNHdl0ruNlsRldXFywWCwBAp9MhNTV1y8k+uwHT09Po6upCRUXFvkrl816g0+v1/POXYRifVv691rLEMAxaW1vBsixqamr2TXoXAAwODmJiYgJ1dXW8n4vb7cb8/DwfoEDTNN8KrtVqw54/l8uFW265BX19fXjllVeg1+tDbrvdSNROuxcJMmaPgzPWfeqpp3D69Gm8+OKLyM3NxcmTJ3Httdfi4MGDO8LIc6bEk5OTfCQ3Z4C2tLSE5ORkGAwGpKam7jln8fn5ebS1tUXUu93bMwOKBh6+5wkMdEziH+94Dz74icvCvodhWNzQ+ENcdbwKt9y+vu3qqgMf/vijUMpE+Mwn34MDB7PQ9rcB1L17PYL5J99/CZ+464qAfbqcbnyo6h6wQeKmYwF2dRVwugJer7ioBF1vDQEAmJIcIEjcM+Vwg7K5AogXuVQImyV421MkIADcwcgYhsWv/udj0KQEGql1t45BKhUhvyx908fdTszOzqKjowNVVVUhH/pcYc/1Sa+urvLEqF6v9yFGCSH4zne+g+9973t4+eWXUV1dvU2fJHI4nU6MjY1heXkZf/jDH/Df//3f+POf/4zy8vKdHloCCSSwh0AI8WkFf/3111FbW8sTMzvVOsMwDJ9UU1NTA4Zh+PrJYrFAq9UiNTU1qIH7bsf4+Dj6+/t9Fg/2A8xmM5qamgLMlb19SjgDfu/I7N1+/rzToPYbETM0NISxsTHU19eHNNZlWRZLS0t8/eRyuUJGnrvdbtx2221oa2vD2bNnd11LYaJ22r1IkDH7DCsrK3jmmWdw+vRpPPfcc0hLS+OJmdra2m0hZliWRVdXFxYXF1FbWwuFwrc1xel08g+mhYUFKJVKpKamwmAwBGy72zAzM4POzk6Ul5cjPT3yCftbL3fhgU/9Brfd14j3Xd+w4fajQybkFgROrs++2oNDlZnQ6pKCvAv4v6dacexkddC/feLyb8M4sRjxmCNFKFVMaq4Oi3OrcDlcIEoZ2Dy/74sQUHa3h4zxe296tgYKmQiDXdObH5eQBqOWgxX7kjGHD2Xjnv/3/qDvcdhdEEuEO74yGgmMRiPOnz8flogJBqvVyq/4LC0t4fXXX+cd/9944w08+OCDeP7559HQsPHvdDfgiiuuQGFhIR566KGdHkoCCSSwR0EI8WkF//Of/4zKykpecbxdreAul8tn8uuvgvFP9lGr1Twxs5sVx4QQfvJbU1Oz61o3tgLO+yYvL49vkQsFi8UCk8mE2dlZWCwWaDQa3idot50/t9uNlpYW0DS9r9KgAGB4eBijo6Ooq6tDUlLwetof3pHnJpMJFosFTz31FNLS0vCBD3wA3/ve9/DGG2/g1VdfRUZGRpw/wdaRqJ12D/YPxZkAACA5ORkf+tCH8KEPfQgWiwV/+tOfcObMGT5J4OTJk2hsbMSRI0ficmN1u928wdfhw4chkUgCthGLxT4GaNyDaWhoCDKZjCdmvJ3pdwMmJibQ19cX9eQXAA5ffgD5ZenQ6CKLtQtGxADAZZeWhX9fXuiVpswCQ3zIGJst4DWKpiBNksE1ueTZxuAXsUwI6FUnKHegT4xEKsIDv7wF//2tP22RjBEERGxTAD71hatCvkcSxPx4N4JL1jh48GDUv0W5XI7c3Fzk5ubC6XRidnYWjz32GH72s5+Boihce+21sNvtYBhmTxRfLMvC4di8giqBBBJIgKIopKen4xOf+ARuv/12n1bw+++/HyUlJfzCVrxawe12O5qbmyGXy0Mm8MjlcuTl5SEvLw92ux1GoxEzMzPo7e1FcnIyXz/tJsUxIQS9vb2YnZ1FQ0PDjsX7xgMLCwtobW2N2PtGqVRCqVQiPz8fNpuNb2Xq6+tDUlIS38q/0wuT+zWWGwBGRkaiJmIAX4/MwsJC2Gw2tLa24o9//CO+9a1vQSQS4ZOf/CRMJhPS09N31fwlGBK10+5BQhnzDoHNZsMLL7yA06dP45lnnoFUKsXJkydx6tQpXHjhhTGRHjqdTrS0tEAoFOLQoUNR79PtdmNubg6zs7OYm5uDRCLhC4vk5OQdu7FxPeYjIyOorq6GRqPZ+E1B8NZLXdAaklFclRXjEa7DYrZDmRR8deXR+5/B04/8NabHI243iNkS8HpmhQFTvQuebUQisCVZ6ylKLOvxhwnSMkVRwP/77w+jtCoTz/7mLfziwec3PTYmSQoiEoCVrv8OL764GHd/cW/3yJpMJrS3t6OysjImMlhCCB555BHcd999+NznPoeBgQE89dRTADwO/J/5zGd2jW/Ml770JVx99dXIycmB2WzGb37zG3z729/G888/j/e+9707PbwEEkhgn8G7FfzMmTN44YUXkJOTg8bGxpi2gnN+IzqdDmVlZVHvk0vWm52dxeLi4q5RHLMsi87OTiwvL6Ourm5XkURbBbcoUlZWtmUlhNPp9InMlslkPDGTlJS0rfWvy+VCc3MzRCIRDh06tO+ImOHhYfz/9u47rsnr+wP4J+y9IQiiIAqIIlOo21pUFIS4+tVqtbXTOjuttXZpq1btsHXU/lq1Vq3KdGtFFMXJEBRlCYgKJCAzzIz7+8PmURQXBhLwvF8vXy9JHp7cQMhzcu495/r6+iplhyO5XI5PPvkER44cwXvvvYfTp0/j0KFDsLa2RlhYGBYsWPBUq+hbC8VO6o1Wxjwn9PX1uTrohoYGxMbGIiIiAlOnToWGhgbGjBkDgUCAwYMHt6g5XG1tLZKTk59pKz8tLS3Y2trC1taW60wvFAqRnJwMLS0t7sJkZmbWZhcmxhiysrJQXFwMPz+/p8qi38//pZ5orH+wr4oyPSwRA9xZGaNsGo0NkN13m3VnC5Rev1u2xGwt7iZipHJo1DSA10wKmMcDps8PhGufOztLKXaYagkGAFoadxI+cjnw3+vR18+xxedUB4odKJSZiPn777+xcOFC7NmzBy++eKdHkUwmw5kzZxAdHY36h+ySpQoikQjTpk1DUVERTE1N0adPHwomCCGthsfjwdzcHNOnT8f06dO5UvDIyEgEBgaCz+dzpUy+vr4tin3Ky8tx8eLFZ9ri+f4tlxWJmWvXrsHQ0JCLn9pyxbFiK+T6+nr07du32ZXS7ZWiX5uyrsU6Ojqwt7eHvb0910BWJBIhMTER2traXCmTmZlZq7YbkEgkSEpKgq6uLjw9Pdv9LlD3un79utITMYsWLcKePXu4XSc/+OAD1NfXIzY2FjExMWrzmqfYSb3RypjnnEQiwYkTJxAeHo7o6GhIJBKEhIQgLCwML7744hO9kVRVVSElJQW2trZwcXFR+oVeLpdzF6aSkhLweDwusDA3N2+1i4Wi901FRQV8fHxgYGDQKo/TVq4m5uPzVzYo7XymJroovy5schtPg4euvRxw/WohAIDxeJD37ApoaIDXKAWv9sFGvQpjp/fHq7PvNixubJDi1YHLIW2mlOlxmKYG5Kb64FXXQWaqD/Zf4+B1G16FQxts890aFI2j3d3dlbIDBWMMu3btwpw5cxAZGYkRI0YoYZSEEPJ8uLcUfP/+/S0qBRcKhUhPT4eLiws6d1b+qtn7Vxwrtuxt7RXHit43AODl5dXud4C6V2FhITIyMlpUJvy05HI5ysrKuPiXMcYlZh63s8/TamxsRFJSEvT19dGnT58OlYgpKCjAtWvX4OPjA1NT02c+H2MMX3/9NbZu3Yq4uDi4uT26hQAhj9LhkjFr167FypUrUVxcDE9PT/zyyy/w9/dX9bDaBalUilOnTnGJGbFYjFGjRkEgECAwMLDZ5aWKD4jdunWDo6Njq49R0dlcKBRCJBI1uTBZWloq7eIhk8lw6dIl1NXVwcfHR22y28+iqqwGr7+wRCnnsrQ1gXZjAwqvNU3GuPd3wdXzudzXchsLMBuzO7slNdOoV8HJ1RqvzhkGr4DuTW7/+JXfkXv16fvGyHW1wHQ0oVFRC5mVEeT6OjA01MWOne+ofR1vcxR/Zz179lTaktfIyEi888472LVrF4KDg5VyTkJI+0XxU8s1Vwo+ZswYjB079qGl4IqdhdriQz2AJiuOS0tLuRXHfD4fpqamSrs2NjQ0IDk5GXp6eujTp0+HKnNR/M68vLxgYdG2EzuKkjlFA2eJRNJky+xnaTegSMQo+hV1pETMjRs3kJOTo9REzLJly7Bx40YcO3ZMbcq4SfvVoZIxO3fuxLRp07BhwwYEBATgp59+wu7du5GZmQkbG+WXaHRkMpkMZ8+eRUREBKKiolBaWoqRI0dCIBBgxIgRMDIywvr163HgwAFs3LhRJTWRjDFUVlZyiRmpVNrkwtTSAEAikSA1NRVyubzZ3Qzas+n+X0Nc8WDD3adhbmMMe74B0uIzm9xu1dkC4sp6NNQ1gulog1kYg5kZg9cgg4bk/mKmu6w7GaO8tAaCt3uiu7sdN2tnYGCA377djyPhSU89RrmhDng1DeDJ5HAZ5obL2SJ4+3TBN0vGPvW5VE3RIFAZdekKe/fuxYwZM7Bt2zYIBAKlnJMQ0n5R/KQ895aCx8TEQENDAyEhIRg7diwGDx4MTU1NvP/++5DJZFi6dKlKdhZ62IpjPp//TKUwdXV1SEpKgpmZGdzd3TvUh3pFvxF12A1KsbOPUChESUkJampqYGFhwcVPT7NldkNDA5KSkmBkZITevXt3qN+ZYuMNHx8fpfzOGGNYvXo1fv75Zxw7dgyenp7PPkjy3OtQyZiAgAD07dsXv/76K4A7FxsHBwfMmTMHn376qYpH137J5XIkJiZyiZkbN27Ax8cHKSkp+PXXXzFp0iRVDxGMMVRVVXEzBvX19Vxixtra+olnDBobG5GcnAwdHZ0O17isuLgYS2dsRlFORYvPYWphCJ2GGsjkPJQW3t2ZicfjwdGjC/Kv3AIDIHfrAh5PA3Pmv4SzcVlIOpXT7PkMjfWgZ6CD28IqrNz6GnSNGNfAztDQENcvV2P3urNPPU65tgY06iWw7WKBkFnDsO7XY5g02R9TpvZr6VNXidZIxBw8eBDTpk3D5s2bMXHiRKWckxDSvlH81DruLwVvbGyEk5MTbty4gd27d6Nv376qHiLkcjnKy8u5+KmlK46rq6uRnJzcaiXrqsIYw7Vr13Dz5k34+Pgopd+Ist2/5bmpqSmXmHlU0+T6+nokJSXB1NS0wyXPbt26hczMTHh7e7d44417McawZs0arFy5EkeOHIGfn58SRklIB0rGNDY2wsDAAOHh4U1meqdPn46KigrExMSobnAdiFQqxWuvvYbo6Gi4uroiPT0dL730EkJDQxESEtKmzXUfRjFjoLgw1dbWwsLCAnw+H9bW1g9d6VJXV4fk5GSYmJi0uAmxulJclFIPFOPUnrQWncPIzAD60jpY2Jrh6oW8JvfdW57E9HSg1d0O3/7wP7h4OKCspBrzJ22EuKppI1geD3BytUVuRjEAYEPMLNjYmQG4E8CWlpbicnIONnwe91TjZDwAUhk0NHhYt38ebgqr8OXiaHz5VSj8+jq16LmrQnl5OVJSUuDq6gp7+5Y3M75XbGwsJk+ejN9++w2vvPKKyv9WCSGqR/FT26iqqsKIESNw7do1mJubQyQSPbYUvK3dXwqjWHHM5/NhaWn50AmqiooKpKSkPFMTYnV07yYOvr6+7WJb7vr6em5nJsXOWtbW1uDz+TA0NOR+N4pEjGIVU0f5nQF3+/ooMxGzYcMGLFmyBIcOHcILL7yghFESckeH2U2ptLQUMpnsga7mfD4fGRkZKhpVx9LQ0IDp06cjKSkJaWlpcHJywpUrVxAeHo5169Zhzpw5GDp0KMLCwhASEgIrKyuVvLnzeDwYGxvD2NgYzs7OqKmpgUgkQkFBAa5cuQJzc3MuMaPoBaPYVtLGxgaurq4d6qKkaFzm7e2NmuuXW5SMMTDWgxEaUXLzNupqm+4IZWVvjtzLN7mvPQa44IOfp8DM8s7OUxbWxhjzSgB2bDjR5PvcvbsgPbmA+1pX/26STFtbG506dQJ/lC02f5eA+trGJxonYwzMQAca1TKMnTEINvbmkP73u3Rxffamt21FEdi6uLgoLRETHx+PV155Bb/++islYgghHIqfWl9JSQlCQkJgYGCAnJwcGBkZcaXgn376KVcKHhYWhpEjR6rsQ79i9yhzc3O4uLhwK46zsrLQ0NDAJWbu7VGi2OWvR48ecHBwUMm4WwNjDFevXsXt27fRt2/fdrOJg56eHhwcHODg4MDtrFVSUoL8/Hzo6urCxsYGpqamyMrKgqWlJXr27Nmh4gFFIsbLy0tpiZg///wTX3/9Nfbv30+JGKJ0HSYZQ1rfypUrkZubi4SEBK6GvFevXujVqxe++OILZGdnIzw8HJs2bcL8+fMxcOBAhIWFITQ0FHw+X2Vv9oaGhnBycoKTkxPq6uogEom4N2tTU1OYmJigsLAQXbp0Qbdu3TrURSkvLw/Xr1/nGpe1ZHtrPUMdmOkxFKTeQO+hvXHl3DXuPh6PBxMrU9wW3knG9Bzjh6kfjOQSMQoefo7YgbvJGDfPzshIvdnkGF29B1csaWjw0M3NFlfuSdpwGLu7ZbbiJl0tMA0NWHUyxZS5LwEAbGyM0bmzOUxMVD/r+CQqKyuRkpKCHj16KG13jYSEBLz88stYvXo1pk+f3qFe44QQou7ee+89dO3aFVu3buUmgQYMGIABAwZg1apVXCn4kiVL8M4772D48OEICwvDqFGjlNJ0tCV4PB5MTU1hamqK7t27cyuOc3NzkZ6eDgsLC+jp6eHWrVvo3bu3Unb5UxdyuRzp6emoqqpC3759oaenp+ohtYi2tjbs7OxgZ2fHNXAuKirC9evXoaGhAR6Ph7KyslbdmbQtFRUVISMjA56enkppsMwYw9atW/HZZ59h7969GDRokBJGSUhT7f8v7z+Khq1CYdPdXYRCYYe6QKjSRx99hGPHjjXbzI/H48HFxQWfffYZLly4gMzMTIwePRq7du2Ci4sLRo4cibVr1+LmzZtQZWWcvr4+unbtCn9/fwwcOBDGxsYoKCiAVCrF7du3cf36ddTW1qpsfMrCGEN2djYKCgrg6+vLBXOdnZ9uxwZdPW1YG2uhIDUfRuaGyEu/1eT+nv26Iy/9JjR1tOAyNgA3yuvQ3eXBv7fuvTpB756VL8PHeuO9xcFNcinNJWMAwMb+ITMbjN35p/gSADPQBU8ux6e/3O1jpKWliQEDezzBs1W9yspKJCcnw9nZWWkzjOfPn8eECRPw3Xff4a233nruEzFyuVyl70GEqBuKn1rfb7/9hn/++afZnRk1NDTg7++PFStWICMjA6dPn4aHhwdWrVoFJycnTJgwAVu3bkV5ebnK3rsUK46dnZ3Rv39/vPDCC+DxeLh5886kSmFhIW7evInGxidbxarO5HI50tLSIBaL4efn124TMffT1NSEkZERqqqq0LlzZ675bHp6Ok6cOIHLly9DJBJBJnv4hgvqrLi4GFevXoWnpycsLS2f+XyMMezcuRMfffQRIiIiMHTo0GcfZDtH8VPr6DDJGB0dHfj6+iI2Npa7TS6XIzY2Fv36ta+mnepKT0/viZbO8ng8dOvWDR9//DESEhKQl5eHiRMnYt++fXB3d8ewYcPw888/Iz8/X6V/1JWVldyMzpAhQ2Bvb4+ysjKcPn0aZ8+eRW5uLsRiscrG11KMMWRmZqKoqAh+fn4wNr67SsXa3rxJOdCjaOtooZO1HvKS76yEcfRyQp34bt8XQ3NDvPr5WLzy6Ri4je+Hq9kl8PZ1hIbGgx/2tbQ04drnzioPDU0e/AZ0x4vBffDuwtHg8QAdXa2HJgms+I9olnfP60duqANo8GDbxQTXCq4gMTERBQUFqK+vx5Ahrk/0nFWpqqqKS8R06dJFKedMTk7G2LFj8eWXX2LWrFnPdSJGkWRVzAZeunQJW7ZswV9//dXkAwQFGuR5Q/FT67OwsHiilQcaGhrw8vLC0qVLkZ6ejqSkJAQEBGD9+vVwcnKCQCDApk2bUFJSorL3KsYYhEIhysvL0bdvXwwYMACWlpYoLCxEfHx8k2tveyOTyXDx4kU0NDTAz8+v2eRZe1VTU4PExETY2NjAzc0NVlZW6NmzJwYNGgQfHx/o6uoiOzsbx48fx8WLF1FYWAiJRPL4E6sBoVCI9PR09OnTRymJGACIiorCnDlzsHPnTowYMUIp52yvKH5qXR2mgS9wZ2vG6dOn47fffoO/vz9++ukn7Nq1CxkZGQ/UQpO2xxhDcXExoqKiEBERgfj4eHh4eCAsLAwCgQDdu3dvsw+Liu3uPDw8YG3ddLWIonmsUCjE7du3oa+vz3WlNzY2VusPtIwxXLlyBeXl5fD19W22IeCnE9YiO+3GI8+jqaWJrg7GyEq4CgAw72SG2joZJA3S/w7QxLTPBRg7ezgAoKK8Ft8v2YugEE8MHubW7DnD/zyF7etPwN3bAUs3TuNujz90GVt+jsUfB+c1+33/RiZjw5J9D96hmL3R0ADT1oT8vzKkme8HYvBLLlwDu4qKChgbG3O/Q0NDw0c+d1WoqqpCUlISunXrhq5duyrlnGlpaRg9ejQ++eQTLFiwQK1ft63tn3/+wa5du7B69Wo4OTnh3LlzGDp0KHr16oXk5GQMGTIEy5YtQ0BAwHP9cyLPL4qf1JtitWt4eDiioqJw8eJFDBgwAAKBoE1Lwe9taOvj49Nksge42zxWKBSioqICJiYm3JbZ6tCg+FGkUilSUlLA4/Hg5eX1xLtwtgeKRIydnd0jY23GGNdnUSQSQSwWw9zcnNuZVB1XCQmFQly+fBl9+vR5IJ5vqb1792LGjBnYtm1bk6bmzyOKn1pfh0rGAMCvv/6KlStXori4GF5eXlizZg0CAgJUPSxyH8YYSktLER0djYiICMTFxcHV1RWhoaEQCASt1lCMMYb8/Hzk5+c/UXMvRfmSUChEaWkpdHR0uA/1pqamavXGI5fLcfnyZYjFYvj4+Dz0orllxQHs+SP+oefR0NRANyczZMSnc7e5D+6FjAu5/x2ggc4utvjl5BdNvk8mk0MqlUFXt/mVN1dTb2DRm39h+ryXEDa1aQO07PRb6NGr+Ua1KQk5WDp7+4N3/JeMYTwe5OaGgOadWccvl4+Hd19H7rDGxkYuMVNWVsYl16ytrWFiYqLy32F1dTWSkpLg6OgIR0dHpZzzypUrGDVqFObMmYPFixer/Dmq2tmzZ9G/f39MmDABS5YsweLFi+Hv749Zs2ahvLwcI0aMgImJCZYvX46BAwdCQ0MDjLHn/udGni8UP7UPijgmIiICkZGROH/+PF544QWEhYUhLCwM9vb2rfLeJZfLceXKFVRUVMDHx+exDW0bGxu5D/VlZWUwMjLi4id125WosbERKSkp0NbWhqen50N3jWqPxGIxkpKSYG9vD2dn56d6bSj6LIpEIlRWVnLJNWtra7WY2BKJRLh06ZJSEzEHDx7EtGnTsHnzZkycOFEp52zPKH5qfR0uGUPaH8YYysvLsWfPHkRERODff/+Fk5MTQkNDMXbsWPTu3VspjcUUM0tFRUXNzug8jqL5mUgkQklJCTQ1NbnAwtzcXKVvPDKZDGlpaWhoaICPjw90dHQeeuyFY1ew/N2/mr2Px+Ohh6slrhy7xN1m242PUpEYcpkcAKClp4MN57+BZSezpxqjVCrDqy+uxg/b30QnhydvrHYt4xY+mfxH0xsZA+R3xsM0NSC3uBvY/fLna3Do2vwyVUVyTSQSobS0FFpaWrC2toaNjQ3MzMzavIGdIhGj2A5UGTIzMzFq1Ci88cYbWLp06XN/QZTJZNDU1ERqaioGDBiA4OBgAMCiRYvQp08fAHe2ER82bBi0tLSwcuVKDBo0qEMF44SQjokxhps3byIyMhKRkZFISEiAr68vl5hxdHRUyjVAEWPU19dzJS1PQ7Grj0gkarLimM/nw8jISKXXqYaGBiQnJ8PAwAAeHh4dopGtgiLGcHBweOYNKhTJtZKSEty+fRsGBgYqXTVeUlKCtLQ0eHh4NNvLsiViY2MxefJkbNy4EZMnT6b4ieKnNkHJGKJ2KisrsW/fPkRGRuLQoUPo1KkTl5jx9vZu0YVSLpfj6tWrKCsrg6+v7zNvUSiXy1FWVsbNGPB4PFhbW4PP57d5V3qpVIrU1FTIZDJ4e3tDW/vRPWFqqurwmv83kMsf/NN3c+fj8tGLTW6bu+FNRKw5gqK8EoDHw9TPwzB+blCLxvp/qw7jzY9GPvHxDQ0NOJNwDr98eLzpHfcmYwDIrYy5nZX+2Te3SbPgh7n3d6iov1ckZiwsLFr9YiIWi5GYmMjt4qUMOTk5GDVqFCZPnozvv/++QwWVLSWXy8Hj8bg652HDhuH27dvYu3cvgoODuRkcsViM4cOHQyQS4Y8//qBmfYSQduVRpeBhYWHo0aNHiz5cSiQSXLx4EQDg5eX12BjjcaRSKUpLS7lJEcWKYz6f3+arVevq6pCcnAxTU1O4u7t3qGumog+dMmMMhft/h9ra2lxixszMrNV/h4pETO/evZVWRhkfH4+JEyfil19+oV0n/0PxU9ugZAxRa2KxGAcOHEBkZCQOHDgACwsLjBkzBgKBAP7+/k/0gVkmk+HSpUuoq6uDt7e30mteFSt7FIkZmUzGJWZa+0O9RCJBSkoKNDU14enp+cQ1zh+G/oz8jKImt/X06IRLh5Ob3GbjxMfGtBXITsnHZ8Er0fOFHlgS9X6Lx1tdUQtjsydLhNXX1yMpKQmmpqZYMXM/asUNd++Uy5s07zXraYfbpWIYG+tha/Sspx4XYwwVFRXc71AikcDKygrW1tawtrZWeu24Ytlw586d4ezsrJRz5ufnIygoCAKBAD/99FOHCipbSjGrU1ZWhvr6etjZ2SE3Nxf9+vWDu7s7NmzYAFfXu82d6+rqMGrUKKxfvx49e/ZU4cgJIaTlFKXgMTExCA8Px7Fjx+Dq6sr16HvSUnDFqhE9PT306dNH6fHMw1Yc8/n8Vv9QX1tbi6SkJFhZWcHNza1DffhW7Mzo5OSktPLnh5HJZE0mtgBwpUyWlpZKj0VKS0uRmpqKXr16KW23t4SEBIwfPx6rVq2iXSf/Q/FT26FkTCtYtmwZIiMjkZGRAX19ffTv3x8rVqxo8qKtr6/Hhx9+iH/++QcNDQ0YOXIk1q1bR43yHqG2thZHjhxBREQE9u3bBwMDA4SGhiIsLAz9+/dv9gOzVCrFxYsXIZfLn2jVyLNijKGyspL7UN/Y2AgrKyvw+Xxu+1BlaWxsRHJyMnR1dZ86SPpjyR4c2Hqa+7qnpx0uHUxqepCmJmZ8Nwlhs+50kd//RxwCRnvBqtOj++woQ319PRITE2Fubg53d3d88L/fUJAtunvAPcmYgaM9UKuliQtncuHU3Ro//jbtIWd9MowxiMVi7ndYU1MDCwsLLrh41t0VFI30WlK//TA3btzAyJEjERQUhHXr1lEiBncDidzcXEybNg1+fn747LPPYGNjg/z8fPj7+8PDwwO//vprs4GDXC6nnyMhbYziJ+W7txQ8MjISR44cgaOjI8LCwh5ZCl5XV4ekpCSYmZm1yaqRtlxxrJgQ6dSpU4tXDKkrRSJGmRsCPKmHTWzZ2NjAysrqmSe2bt++jdTUVLi7uystEXPu3DkIBAJ8++23z/2ukwoUP7UtSsa0gqCgIEyaNAl9+/aFVCrFZ599hsuXL+PKlStcw6uZM2di//792Lx5M0xNTTF79mxoaGggISFBxaNvH+rr6xEbG4uIiAjs2bMHmpqaCAkJwdixYzFo0CBoa2vj1q1bmDdvHubPn49+/fq1eQ0jYwzV1dXcRamurq7JRelZEkP19fVITk6GkZFRi3rqnDl8CavmbAMA+A91wentJx44xtrJFv93aUWLx9hSigDQwsKCm737ds52JJ/KuXvQf8kYQxM9bDrxCbb9mYDIfy7Av78zPlsiUOp4amtrud9hVVUVTE1NueW4T7s7xJPuaPA0ioqKMHLkSAwZMgQbN26kWt17FBUVwdvbGxMnTsS7776LXr16cUHCzZs30bdvX7i7u+PHH3/k6p8JIapD8VPru78U3NbWllsx4+PjAw0NDVy4cAE//vgjPv/881bbUOFR5HJ5kw/1crmcKyN+1tUWivIdZfRRUTcVFRVISUmBs7MzunTpotKxNBcD3zux9ajehs0pKyvDxYsX0bNnT3Tq1EkpY0xOTsaYMWPwxRdfYP78+R3qtfCsKH5qO5SMaQMlJSWwsbHBiRMnMHjwYFRWVsLa2hrbt2/HhAkTAAAZGRno2bMnzpw5gxdeeOExZyT3kkgkOH78OMLDwxETEwOJRILAwECcOnUKPXv2xK5du9RiOz7FaguhUIiamhpYWlq26KKkSFYoVo205OJRWSbGjBeWwqmHNVbEzMfLNm9D2ijl7udpaOCDP9/F4Altu5PGw5YN//btfhwJv2flzn87KX31x2vw6OuEY4fTseb7Qxgt8MLbc15qtfE1NDRwS3EVu0MoAsTHNSGsra1FYmIibG1tlTYTV1xcjFGjRsHf3x+bN2+mRMw9GGNYuHAhMjIyEBERwf1sZP+9djQ1NVFcXAxXV1f06tUL//77r1rsDkEIuYvip9YlFotx8OBBRERE4MCBAzA3N8fgwYMRHR2NV199VS16j9274lgoFEJI9G1/AABDmUlEQVQikXDX3addcaxIVrRF+U5bKy8vR0pKCnr06AEHBwdVD+cBii2zS0pKUFVVBTMzMy4GftzEliIR4+bmBjs7O6WMJy0tDaNHj8aCBQvwySefUCLmHhQ/tS3lNkIgzaqsrAQAWFjc2UEmKSmJSxgouLm5oUuXLhRMtIC2tjaGDx+O4cOHY+3atfj7778xe/ZsWFlZITExEbNnz4ZAIMBLL7301CsZlMnIyAhGRkbo1q0bt9ri5s2buHr1KszNzbnVFo8qg6mpqUFSUhJsbGzg6ura4ouHqYURQqb1w2uLQsHj8dDZpRPyL9/g7u/Z31UliZjExETw+Xy4uLg0eW5WtiYPHO8/zA0efe/sQNS5652/LWubB49TJl1dXTg4OMDBwQESiYRrYJefnw9dXd2HbnuuSDLx+XylJWJKSkowZswYeHt7Y9OmTSpNxDxJaUFb4/F4yMzMhIGBAfezYYxx/y8pKYGtrS1ycnJw9epVCiQIUUMUP7UuIyMjTJw4ERMnTkRtbS1WrFiB7777Dt26dUNMTAzkcjkEAsFDS8HbAo/Hg5mZGczMzNCjRw9utUVOTg4uX77MrTh+XH83RYmLi4sLOnfu3IbPoPUpkhXq/NwMDQ3h5OQEJycn1NfXc7trZWVlNdn23NDQsEmMVF5ejosXL8LV1VVpiZgrV64gJCQE8+fPV3kihuInQsmYViaXyzF//nwMGDAAvXv3BnBnNltHRwdmZmZNjuXz+SguLlbBKDuO5ORkfPTRR/jwww+xePFinD17FhEREfjkk09QVlaGkSNHQiAQYMSIESp98zAwMICjoyMcHR1RX18PkUiE4uJiZGZmPrQMprq6GsnJyUorcXn98zDu/117deaSMRpampj6xbhnOvfTUiSZHrZqxMrW9O4XjEHfUBcfrHyZu6mzgyIZ83TblT8LbW1tdOrUCZ06dWrShDAlJQUaGhrc71BPTw/JycmwsbF5IMnUUmVlZRgzZgxcXV2xdetWlQXJCidOnMCsWbOalBaMGDGiSWlBW1B09pfL5ZDL5bCxseH6/hgYGHD3lZeX48cff8TUqVPh7u4Oa2tr7nsJIeqB4qe2tXfvXqxatQp//fUXxo4dy5WCT506tdlScFXg8XgwMTGBiYkJnJ2dUVNTA6FQiPz8fKSnpz90xXFJSQkuXbqk1BIXdaFIMilz1Uhr09PTazKxpUjM5OXlQU9Pj4uf5HI5UlJS4OLiAnt7e6U8dkZGBoKDg/Huu+9i8eLFKr/uU/xEKBnTymbNmoXLly/j1KlTqh5Kh5eVlYXAwEAsXboUc+fOBQAMHDgQAwcOxOrVq3HhwgVERETgq6++wttvv43hw4cjLCwMo0aNgolJ666oeBQ9PT106dIFXbp04cpgRCIRsrOzYWxsDBsbGxgYGODKlStwdHSEk5OT0sfg2NsBJ3aeAQAMe2UgevV3UfpjPIyikd6jkkxW/Lu/H20dLXywcgK0te+uBDEw1IWllRGsWnllzMModoBQBA+K3bUuX76MxsZGGBgYwMzMDHK5/JlXsFRUVCAsLAxdu3bFP//8o7Kg+F6HDh1q8vXmzZthY2ODpKQkDB48uNUfX9FsTvHa0dDQgIaGBgIDA/G///0Pf/zxB958800YGBhAQ0MDx44dQ0REBF5++W5CjwIJQtQLxU9t58CBA3jzzTcRHh6OUaNGAQCCg4MRHBzcpBT8zTffhEQiQUhICAQCAYYOHfrMTe1bisfjcSuOnZ2dUVtbC6FQ+MCKY+BOfOjh4cF93VGUlpYiLS2tXSeZtLW1YWdnBzs7O8hkMpSWlqKkpARJSUmQyWQwNzeHvr6+UprC5uTkICQkBNOmTcM333yjFtd9ip8IJWNa0ezZs7Fv3z7Ex8c3WTZoa2uLxsZGVFRUNJndEQqFSusO/jzq0aMHDh06hP79+z9wn4aGBgICAhAQEIDly5cjNTUV4eHh+P777zFz5kwEBgYiNDQUwcHBrb6d4qPcWwbT2NiIkpIS3Lp1C5WVldDR0YFcLodYLH5gGeez6trrzutTQ4OHCR8GK+28j6NIxDxuZyGrTndXxgwN9YTPwAeTRZ27WLTpypiH0dDQgKWlJQwNDVFaWgpra2sYGBhwS6rvnbl72kRKVVUVxo4dC2tra+zevfupG+C1lftLC1qTVCqFlpYWGhsbsWzZMlRWVkJTUxPvv/8+Jk6ciJs3b2L+/PlIS0vjZku///57rF27Fl5eXq0+PkLI06P4qW29+OKLOHHiBHx8fB647/5S8FOnTiE8PBxz5syBWCzG6NGj1aIU3MDAgCuDqaurg0gkwvXr11FXVwdDQ0PU1dWhrq5OpWNUppKSEqSlpSl1i2dV09TUBJ/Ph56eHkQiERwcHMAYw+XLlx9o4vy0E1t5eXkICQnBhAkTsGLFCpX3QnoYip+eP9TAtxUwxjBnzhxERUXh+PHj6NGjR5P7FQ3oduzYgfHjxwMAMjMz4ebmRjXPbYwxhvT0dISHhyMqKgpXr17F0KFDIRAIEBISAktLS5VmfBWzHt27d4e2tjZEIhFKS0u5ZZx8Ph/GxsbPPMbSW2V4w/V99B3thc93va+k0T9adXU1kpKS4ODgAGdn50ceK5HIMDngW9h1tYLPgG547aOgB475c/1xvPbOEGhoqD5Dr9ia+94doRhjXAM7kUgEsVjMzdxZW1s/tsm0WCzGuHHjoKuri3379qltQCmXyxEaGoqKioo2m9GWyWTo06cPzMzMYGhoCKFQiIKCAmzevBlhYWHYtWsX/vnnH+Tk5MDV1RVjx47FK6+8AgC0vJYQNULxU/shk8lw5swZREREICoqCrdv30ZQUBDCwsIwcuRIlfeRKCgowLVr1+Du7o7GxkaIRCKUl5dzK475fD4MDAxUOsaWEolEuHTpEnr37t3htnRXbM19745Q9zZxFolEaGhoeKrdSQsKChAUFISgoCCsW7dObRMxFD89nygZ0wree+89bN++HTExMU0aMJmamnIfoGbOnIkDBw5g8+bNMDExwZw5cwAAp0+fVsmYyZ03laysLERERCAyMhKpqakYOHAgBAIBxowZAz6f36ZvOoqL7f2zHoplnIqu9Nra2lxgcX/j2KfxSueZ+HTbXPQZ0lNZT+GhFFtLdu3a9YnLrt4O+gk6WjyMe3Mwhgm8H7g/+XwefPyVX8L1tBoaGpCYmAgzM7NH7nalmLkTiUSorKyEiYkJN+tzfxBbW1vLffDYv38/jIyMWv15tNTMmTNx8OBBnDp1qs0aCc6bNw8XLlzAqVOnIJfLoaWlhTfeeAO7d+/GiRMn4O3tjZqaGmhra0MqlXIBOAUShKgXip/aJ7lcjsTERG5iq7CwUKWl4Hl5ecjPz4ePjw9MTe+urFWsOBaJRLh9+zYMDQ25+EnZK45bi1AoxOXLlztk2VVVVRWSkpLQrVs3dO3atdljGGPc7qQlJSUQi8WwsLDg4qf7y+YKCwsRFBSEIUOGYOPGjWq96yTFT88nSsa0goe9ODdt2oTXXnsNwJ2Z8w8//BA7duxAQ0MDRo4ciXXr1nWYpYbtHWMMeXl5XGLmwoUL6NevH8LCwhAWFgY7O7tWfRMqKirC1atX0bt370debOVyOdc4tqSkpEnjWDMzs6fK/m/8cCveXv2qMob/SIpZj6fdWjLij5Po5dMVbt5dmr2/oUECXV3V9k9RJGJMTU3Rq1evJ36N3B8g6urqYs+ePQgLC4O3tzcmT56M2tpaHDp0SKX9jR5n9uzZiImJQXx8fKv0NnqYKVOmwMLCAr/88gu37Ba40/NALBbj6NGj0NLSosCBEDVH8VP7J5fLuVLwqKgo5Obm4qWXXkJYWFirl4IzxpCTk4PCwkL4+PjA2Pjhpcv37oioWHHM5/NhY2OjlBXHraG4uBjp6eno06cPrK2tVT0cpaqurkZiYuJTx4b3T2w1NDTg3LlzePnll2FlZYVRo0YhICBA5btOPg7FT88vSsYQ8hiMMdy4cQORkZGIjIzE6dOn4efnxyVmunbtqtQ3qZs3byIrKwuenp6wtLR84u+7t3GsSCQCYwzW1tbg8/mwsLB4bGKmprIWhqatu2S3oqICKSkpj5z1aK8aGxuRmJgIExOTp0rE3E8qlSIrKwsLFy5EQkIC9PX1oa2tjT/++ANBQUFqGUw8rrSgtb3yyivIycnB+fPnAdwJsrW1tfHrr7/i999/x5kzZ9rtcnRCCGmv2rIUnDGGzMxMiEQi+Pr6PlWZVHMrjhWJmWdZcaxMikm6Pn36wMrKStXDUSpF2frTrJZuTkNDAxISEvDtt98iMTGRKwXfvHkzfH191eL3eD+KnwglYwh5CowxFBUVISoqChERETh58iT69OnDJWaedcvp69evIzc3F97e3g9s3fm046yoqOASM1Kp9JkanymDIhFzbx1wR6FIxBgbG6N3795KueA3NjZi6tSpuHXrFjw8PLiO+6GhoRg7dixGjRqlNnXPT1JaoAyKrv/3O3HiBN59912EhoZi+fLl3M9/06ZN2LBhA/bv36/y/k+EEPI8a81ScMYYrly5gvLycvj6+j7TdUcmk6GsrIyLn+7dLfFpVxwrS2FhITIyMp56kq49EIvFSExMfOZEzL1u376N0NBQGBkZgc/n49ChQ7CxscHYsWMxYcIE9OvXTymPowwUPxFKxhDSQowxlJaWcomZuLg4uLm5ISwsDAKBAG5ubk/85sUYQ25uLm7cuAEfHx+llqIwxlBVVQWRSAShUIjGxsYmjc8USxJbU3l5OVJSUuDi4tJmdbBtpbGxEUlJSTA0NETv3r2VEqhJJBLMmDEDWVlZOHbsGKytrSGTyZCQkICoqCgkJiYiPj5ebS6OT1Ja8KzuDSTWrFmD69evo0+fPvD09ISXlxeWL1+O8PBwODo64u2330ZRURE+/PBDfPDBB/jss8+UMgZCCCHP7lGl4KGhobC3t3/i65tcLsfly5chFovh4+Pz2Gb4T0Ox4lgoFKKkpASMMS4x8yQrjpXh1q1byMzM7NCJmCfZyOFJVVRUICQkBPb29oiIiICOjg7q6+tx9OhRREZGQiqV4q+//lLKYykDxU+EkjHPueXLl2PhwoWYN28efvrpJwB367H/+eefJvXYHa1juzIxxlBeXo6YmBhERkbi33//hZOTE8LCwjB27Fj06tXroRdtxhiys7NRVFQEX1/fVm3Oqmh8JhQKIRKJUFdX90xbLT+JsrIyXLx4Ea6urrC3t1f6+VVJIpEgKSkJ+vr68PDwUEpgJpVK8c477yA1NRVxcXH0d4emjeJCQkJw6dIlODk5IS8vDzY2NpgzZw6mTZuGrVu3YsOGDcjIyICTkxOCg4Px9ddfP3AOQgh5VhQ/KcezlILLZDKkpaWhoaEBPj4+0NHRadVxVlRUcPGTTCZr9RXHirJ1Ly+vNtnquC3V1NQgMTERnTt3VloipqqqCmFhYTA3N0d0dLRSE3PtFcVP6o+SMc+xCxcu4OWXX4aJiQlefPFFLpiYOXMm9u/fj82bN8PU1BSzZ8+GhoYGEhISVDvgdqSyshJ79+5FZGQkDh8+jE6dOnGJGS8vL+5Du0wmw/bt2+Ho6AhfX982r8tUbLUsFAq5jvSKWR9lBDW3b99Gamoq3NzcYGdnp4QRq4/WSMTIZDLMmjULZ8+exfHjxzvcz+xJPezCf+TIEXz33Xf4+++/0blzZ2RmZmLDhg2Ii4vDokWLMHHiRAB3trE0NDTkZhHlcrnalHQRQto/ip9ax72l4JGRkYiPj39oKXhFRQX27t0LV1dXeHt7t8pk0qPGWVVVxSVmWmPF8Y0bN5CTkwMvLy+Ym5srYdTqQ5GIsbe3h7Ozs1I+6IvFYowdOxZ6enrYt2+fUkt82hOKn9ofSsY8pxTLOdetW4elS5fCy8sLP/30EyorK2FtbY3t27djwoQJAICMjAz07NkTZ86cwQsvvKDikbc/YrEYBw4cQEREBA4cOABLS0uEhoYiJCQEP/30Ey5duoQzZ86ofNajrq6OCyyqqqpgZmbGJWZaMrtQWlqKtLQ09OzZE506dWqFEauORCJBcnIydHV10adPH6VcqORyOebPn4+4uDjExcV1uL46T+reQGLHjh3w9/eHs7MzZs6ciZs3b8LS0hKbN2/mjs/JycGnn34KbW1t7Nix44Fz0IwOIUSZKH5qG/eWgkdGRuLYsWNcKfjQoUMxb948mJqa4uDBg21Sbv2ocSp7xbGy+geqo5qaGiQlJaFTp07P3GdRoba2FuPHjwcA7N+/v1VXmKszip/aJ0p1PadmzZqF4OBgBAYGNrk9KSkJEomkye1ubm7o0qULzpw509bD7BCMjIzw8ssvY+fOnRAKhfjxxx9RUlICgUCApKQkBAUFIT09HVKpVKXj1NfXh6OjI/z9/TFw4EDY2NhAJBLh1KlTOH/+PPLz81FXV/dE5yopKUFaWhrc3d07bCJGR0dHqYmYTz75BEePHsXRo0ef20QMcLd+esmSJZg9ezb3d2Fra4v9+/fj3LlzKCgo4I7v3r07BAIBIiMjcf369SbnuP//hBDyrCh+ahs8Hg/W1tZ4++23cfDgQRQXF+P999/H+fPnERwcDIlEgv79++Pq1auQy+UqHaexsTG6d++O/v37IyAgACYmJigoKMCJEyeQnJyMmzdvorGx8YnOl5+fj9zcXPj4+HS4RExtbS2SkpJga2urtERMXV0dJk2aBIlEgr179z63iRiA4qf2ipIxz6F//vkHycnJWLZs2QP3FRcXQ0dH54ELAJ/PR3FxcRuNsOMyMDBAUFAQKisr0bNnT/z444+QSqV45ZVX4OLigrlz5yIuLg4SiUSl49TT00OXLl3g5+eHwYMHw87ODmVlZUhISMDZs2eRl5eHmpqaZr9XJBIhLS0NvXr1gq2tbRuPvHVJpVKkpKRAW1sbnp6eSkvELFq0CHv27MHRo0eVtptAe7Zy5Up89913iIyM5HYX+PLLL/HXX38hMzMTv/32G4RCIXe8lZWV0gI7Qgh5GIqfVIPH48HCwgIjR47EzZs3MWrUKCxcuBDXrl3DsGHD4OXlhcWLFyMpKUmliRngzgRct27d8MILL6B///6wsLBAYWEh4uPjkZiYiIKCAtTX1zf7vXl5ecjPz4evry9MTU3beOStq66uDklJSeDz+ejRo4dSrtcNDQ2YOnUqqqqqcODAAaVuftFeUfzU/qhuXR9RiRs3bmDevHn4999/qbGVClRXV2PMmDGQyWSIi4uDiYkJXnnlFUgkEsTFxSE8PBwzZsyATCZDcHAwxo4di6FDh7ZqU7rH0dHRQefOndG5c2dIJBKUlJRAJBIhNzcX+vr6sLGxAZ/Ph5GREUQiES5fvgwPDw/Y2NiobMytQZGI0dLSUloihjGGb775Brt27UJcXBy6d++uhJG2bz///DO+/PJL7N27F0OGDOFu37RpE15//XVUV1dj1qxZuHnzJkaMGAFLS0u8//778PHxea5XFBFCWhfFT6p1/fp1vPTSSxg0aBD+7//+D5qamnjttdealIKPHj2aKwUXCATo27dvqzTWfVIGBgZwdHSEo6Mj6uvrue2ys7KyYGJiwsVP+vr6uHbtGm7cuAFfX18YGxurbMytoa6uDomJibC2toaLi4tSPvg3NjZi2rRpKC4uRmxsbIdbRdQSFD+1T9Qz5jkTHR2NsWPHNrk4yWQy8Hg8aGho4PDhwwgMDER5eXmTN7auXbti/vz5eP/991Uw6o6joKAAX3zxBdauXQtDQ8Nmj5FKpTh58iTCw8MRHR2NmpoaBAcHIywsDIGBgWoTBEqlUpSWlkIkEqG0tBSampqQSCTo3r37I3c/aI8UiRgNDQ14eXkpJbhjjGHZsmXYuHEj4uLi0KtXLyWMtH07cOAAQkJCsGnTJkyfPp27fcCAATA3N8fu3buhr6+PzZs3Y8aMGQDulAwYGxvju+++A0DN5gghrYPiJ9VKSEhAVFQUvv/++4e+x9fW1uLw4cOIiIjA/v37YWhoiDFjxkAgEKBfv34q7S1zr8bGRi4xU1ZWBm1tbUil0g45kaVYEWNpaQk3NzelxIYSiQQzZsxAVlYW4uLiYGVlpYSRtm8UP7VflIx5zlRXV3N1gQqvv/463NzcsGDBAjg4OMDa2ho7duzgmmFlZmbCzc2NGtCpgEwmw+nTpxEREYGoqCiUl5cjKCgIAoEAw4cPf2hCp63dunULGRkZMDU1RXV1NbS0tLjmv2ZmZu06MSOTyZCSkgIej6fURMzq1auxZs0axMbGwtPTUwkjbf/27duHzz//HL6+vli2bBlsbGwQFBSEqqoqREdHw8bGhgsWoqKiMH78eCxevBjvv/8+zMzMKJAghLQaip/al/r6ehw9ehSRkZGIiYmBlpYWxowZg7Fjx2LgwIFtuvvSwzDGkJmZicLCQpiYmKCyshL6+vrg8/mwsbGBkZFRu46f6uvrkZiYCAsLC/Ts2VMpz0UqleKdd95Bamoq4uLiaNv4/1D81H5RMoZg6NCh3G4AwJ2tGQ8cOIDNmzfDxMQEc+bMAQCcPn1ahaMkcrkcFy5cQHh4OKKiolBUVIQRI0YgLCwMo0aNUtmy1sLCQmRkZMDT0xOWlpaQy+UoKyuDUChESUkJeDwel5gxNzdvV2/2MpkMFy9eBGMM3t7eSkvErFmzBitXrsSRI0fg5+enhJF2HIcPH8a3334LR0dHXL9+HY2NjYiIiICdnR3X2b+2thbl5eVITU1FSEgI3n//fXz44YfP7VbghBDVoPipfVCUgkdERCA6OhoymQwhISEQCAQqKwVnjCE7OxtFRUXw8/ODoaEht+JYKBSitLQUurq6XGLGxMSkXSVmWiMRI5PJMGvWLJw9exbHjx+na/59KH5qn9RjvR5RKz/++CM0NDQwfvx4NDQ0YOTIkVi3bp2qh/Xc09DQQEBAAAICArBixQpcvHgR4eHhWL58OWbOnInAwECEhoYiODgYpqambXLRvnXrFjIzM+Hl5cVtza2hoQErKytYWVlBLpejoqKC6yXDGIO1tTVsbGxgaWmp1okZRSJGLpcrNRGzYcMGrFixAocOHaJETDNGjhwJTU1NLFq0CJmZmdizZw/s7Owgk8mgqamJ+vp6DBw4EA4ODoiJiUF4eDgmTJgAS0tLfPbZZ6oePiHkOUbxk3rS1tbGiBEjMGLECKxdu5YrBZ81a5ZKSsEZY8jKyoJQKOQSMQCgpaUFW1tb2NraQiaT4fbt2xAKhUhOTm5XK44bGhqQlJQEc3NzpSVi5HI55s+fj4SEBMTFxVHyoBkUP7VPtDKGkHaOMYbLly9zK2YyMzMxdOhQCAQChISEwMLColUu2jdu3EB2dja8vb1hbm7+ROOsrKyEUCiESCSCVCqFlZUVbGxsYGVlpdIme/eTyWRITU2FTCaDt7e3UurMGWP4888/sWjRIhw4cAADBw5Uwkg7rpMnT+Lzzz8Hn8/HF198gd69e6OhoQHDhg2DVCrFuXPnuGOPHz8OLy8vauBHCCHkiT2sFDwsLAwjRoxolVJwRWlSSUkJfH19YWBg8NjvkcvluH37NkQikdqvOFYkYkxMTNCrVy+lJWI+/vhjHDx4EHFxcbTr5GNQ/NS+UDKGkA5EcZGPiIhAZGQk0tLSMGjQIAgEAowZMwY2NjZKuTAWFBTg2rVr8Pb2btEbOGMM1dXVXGKmvr6eS8xYW1urtMmeXC5HamoqJBIJfHx8lJaI2bp1Kz7++GPs3bsXQ4cOffaBPgdOnz6NRYsWwdLSEp9//jkWLlzIzRICd5ogamtrq/UMISGEEPXXFqXgjDFcvXoVZWVl8PX1hb6+fovGWVFRwcVP6rTiuLGxEYmJiTA2Nkbv3r2Vloj57LPPEBkZiePHj9Ouk0+I4qf2g5IxhHRQjDHk5uZyiZnExET0798fYWFhCA0NhZ2dXYvehK9fv47c3Fz4+PjA1NRUKeOsqanhAouamhpYWlpysz5t2WRPkYhpbGyEj4+PUh6bMYadO3di7ty5iIyMxIgRI5Qw0ufHuXPn8Pnnn+P48ePo2bMn0tLSANxp4qcuO2MQQgjpOORyOVcKHhUVhfz8fLz00ksICwtrcSk4YwxXrlxBeXk5/Pz8lFIOpU4rjhsbG5GUlARDQ0P07t1bKUkhxhi++uor/P333zh+/DhcXV2VMNLnB8VP7QMlYwh5DjDGUFBQgMjISERGRuLMmTPo27cvwsLCEBYWhi5dujxRYJGfn4+8vDylJWKaU1NTw235WF1dDXNzcy4xo6ur2yqPCdwJvtLS0lBfXw9fX1+lJYEiIyPxzjvvYNeuXQgODlbKOZ8358+fx+bNm7F27VrweDwKJAghhLQJxhjS09MRHh6OyMhIZGRk4MUXX4RAIEBwcDAsLS0fGz8pzlFZWQlfX99W6UvDGENVVRUXP7XliuPWSsQsW7YMGzduRFxcHHr16qWEkT5/KH5Sf5SMIWrp1q1bWLBgAQ4ePIja2lp0794dmzZt4hqeMsbw5Zdf4vfff0dFRQUGDBiA9evXo0ePHioeufpjjKGwsBBRUVGIiIjAqVOn4OnpySVmnJ2dmw0s8vLycP36dfj4+MDExKRNxlpXV8cFFpWVlTA1NeV2FlBmMCOXy3Hp0iXU1dUpNRGzd+9ezJgxA9u2bYNAIFDKOZ93FEgQQsjDUfzUehSNdyMiIhAREfFEpeByuRzp6emorq6Gr69vq04q3TtOsVjMxU+1tbWwsLAAn8+HtbW1UlccSyQSJCUlQV9fHx4eHkpLxKxevRo///wzjh07Bk9PTyWMlFD8pJ4oGUPUTnl5Oby9vfHiiy9i5syZsLa2RnZ2NpydneHs7AwAWLFiBZYtW4YtW7bAyckJixcvxqVLl3DlypU26YTfUTDGIBKJEB0djYiICG4pY1hYGAQCAbck9OOPP4aOjg4WLlyosi20GxoauMCivLwcxsbGXGLmSRrgPYxcLsfly5dRU1MDX19fpW1xefDgQUybNg2bN2/GxIkTlXJOQggh5GEofmo7T1IK3tjYiKlTp2L48OGYPn16myRimqNYcSwUCiEWi2Fubs4lZp5lTIpEjJ6eHvr06aO0RMyaNWuwcuVKHDlyhHadJB0eJWOI2vn000+RkJCAkydPNns/Ywx2dnb48MMP8dFHHwEAKisrwefzsXnzZkyaNKkth9thMMZQVlaGmJgYREZG4ujRo3BycoKDgwPOnz+P6Oho+Pv7q3qYAO4siS0pKYFIJMLt27dhaGjIJWaMjIye+DytlYiJjY3F5MmTsXHjRkyePJkapBFCCGl1FD+pRnOl4L6+vpDJZCgrK8Phw4dhb2+v6mECuLviWCgUoqqqqsUrjiUSCZKTk6GjowNPT0+lJWI2bNiAJUuW4NChQ3jhhRee+ZyEqDtKxhC14+7ujpEjR+LmzZs4ceIE7O3t8d577+Gtt94CAOTm5sLZ2RkpKSnw8vLivm/IkCHw8vLCzz//rKKRdyzl5eV49dVXERsby82eCAQChIWFwcvLS222UpRIJCgtLYVIJEJpaSn09fW5HjPGxsYPTYQotgSvrq6Gn5+f0hIx8fHxmDhxIn799VdMmzZN5YmY+Ph4rFy5EklJSSgqKkJUVBSVTBFCSAdE8ZPqMcaQl5cHgUCAmzdvQiaToXv37lz89LBScFWor69HSUkJhEIhKioqYGJiwsVPj1pxLJVKkZycDG1tbaUmYv78808sWrQIBw4cwMCBA5/5nM+CYifSVtTj0xQh98jNzeXqlw8fPoyZM2di7ty52LJlCwCguLgYAMDn85t8H5/P5+4jz4YxhqVLl+LixYtITU1Feno6li5diuvXr2PUqFHw8PDAwoULce7cOcjlcpWOVVtbG506dYKnpyeGDh0KZ2dn1NbWIjExEQkJCcjKykJFRQXuzTsrmukpariVlYhJSEjAyy+/jB9++EEtEjHAneXJnp6eWLt2raqHQgghpBVR/KR6DQ0NmDNnDnR1dZGTk4OsrCy8/fbbOHXqFPz8/NCvXz8sX74cV69eharnw/X09ODg4AA/Pz8MHjwY9vb2KCsrw+nTp3H27Fnk5uZCLBY3+R5FIkZLS0uppUlbt27FZ599hj179qg8EQNQ7ETaDq2MeQ4xxsAYU5uVDffT0dGBn58fTp8+zd02d+5cXLhwAWfOnMHp06cxYMAAFBYWolOnTtwxL7/8Mng8Hnbu3KmKYXcoS5Yswe+//464uDiuzlyhtrYWhw4dQkREBPbv3w9jY2OMGTMGAoEA/fr1a9OtFB9FsTxYKBSipKQEmpqa3K4CRUVFqKyshJ+fn9JquM+dOweBQIBvv/0Ws2bNUotEzP14PB7N7hBCSAtR/EQeZ8KECbh58yYOHToEMzMz7nbGGMrLyxETE4OIiAgcPXoU3bp143r09erVS21eV4oVx0KhELdv3+ZWHFtaWiI7Oxuamprw8vJSSrzHGMPOnTsxd+5cREZGYsSIEUp4BspFsRNpTerxV0/aRG5uLoRCIXg8ntq84TenU6dOcHd3b3Jbz549UVBQAACwtbUFAAiFwibHCIVC7j7ybKZMmYITJ048kIgBAAMDA4wbNw7btm1DcXEx1q1bh9raWkyaNAkuLi6YN28ejh8/DolEooKR36WpqQlra2v07t0bQ4YMQa9evSCXy3Hx4kUUFRXBxMQE1dXVSlnZk5ycjHHjxuGrr75S20QMIYSQlqH4iTypBQsW4MiRI00SMcCdD/QWFhZ4/fXXsW/fPgiFQnz22WfcVtne3t748ssvkZycrDYrjr28vDBkyBA4OzujpqYGiYmJqK6uhpGREaqrq5WysicyMhJz587Frl271DIRQ0hrU98rClEKuVyOM2fOIDAwEBMnTsQLL7yA7t27Y+nSpSgsLAQAlS+TvN+AAQOQmZnZ5LasrCx07doVAODk5ARbW1vExsZy91dVVeHcuXPo169fm461o+rWrRucnJwee5yenh7GjBmDzZs3o7i4GJs3bwYAvPbaa+jevTvee+89/Pvvv2hsbGzlET+ahoYGLCwsANyZOfTw8IC2tjauXLmCEydO4PLlyxCJRJDJZE997tTUVISGhuLTTz/F/PnzKRFDCCEdAMVPpCX69u0LExOTxx5namqKqVOnIioqCkKhUG1LwbW0tGBlZQWJRAIzMzP06tULEokEKSkpOHnyJDIyMlBWVtaiv4U9e/bg3XffxbZt2zB69OhWGD0h6o/KlDq4devW4fPPP4ebmxveeOMN+Pj4IDk5GXv37oWmpiZ++OEH7iLdpUsX7Nq1S+Xdyy9cuID+/fvj66+/xssvv4zz58/jrbfewsaNGzFlyhQAd7ZmXL58eZOtGdPS0mhrRjUhlUoRHx+P8PBwREdHo66uDsHBwRAIBBg2bFib/44YY8jIyMDt27fh5+fHPT5jDJWVldyW2Y2NjbCysgKfz4elpSW0tLQeed709HSMGjUKc+fOxeLFi9U+EUNLbQkh5MlQ/ERUQd1KwWUyGS5evAi5XA4fHx/u8eVyOcrKyrj4icfjwdraGnw+H+bm5o9dQXbgwAFMnz4dW7ZswYQJE9riqbQYxU6kNVEypgOrqqpCjx490L9/f2zcuBHW1tZgjIHH46GkpASvv/46vL29sWTJEkRERGDy5Mk4d+4cvL29VT107Nu3DwsXLkR2djacnJzwwQcfcLsBAHc+RH/55ZfYuHEjKioqMHDgQKxbtw4uLi4qHDVpjkwmQ0JCAiIiIhAVFYXKykoEBQUhLCwMI0aMeGTHfmVgjCEzMxMlJSXw8/ODvr7+Q48Ti8UQCoUQiUSoq6uDpaUl+Hw+rKysoK2t3eT4jIwMjBo1Cm+99RaWLFmi9okYgAIKQgh5EhQ/EXVQX1+Pf//9F5GRkYiJiYGOjg7GjBmDsWPHYsCAAQ/EJcp2byLG29v7oRNUin44isSMTCbjEjMWFhYPJJCOHj2KV155Bb///jsmT57cqs9BGSh2Iq2JkjEd2ObNm/HOO+9g//79CAwMfOD+mpoaVFVVoVOnThg5ciS0tLQQHR0NbW1tSKVSaGhoqHVtNGl/5HI5zp8/j/DwcG5p7vDhwyEQCBAUFARjY2OlPh5jDFlZWRCJRI9MxDRHLBZzgYVYLEZJSQmKi4sxadIk1NTUICgoCFOmTMGKFSvazd8JBRSEEPJ4FD8RddPY2Ii4uDhEREQgOjoacrkcISEhEAgEGDp0qNJ2hVSQyWRITU2FVCqFj4/PY1cKKzS34jgzMxN6enoYP348UlJSMHHiRKxdu1Ztdp18HIqdSGuiZEwHNm3aNKSmpuLgwYOws7Nrcp9ihkdBT08PGzZsgJOTE3r37g1LS8sHznf/9xDyLORyOVJSUhAeHo7IyEgUFBQgMDAQYWFhGD16NExNTZ/p9cYYQ3Z2NoRCIXx9fZ9pBU5tbS2ioqLwww8/IDs7G1ZWVnBzc8Pff/+Nzp07t/i8bUEsFiMnJwcA4O3tjR9++AEvvvgiLCws0KVLFxWPjhBC1A/FT0SdSaVSnDx5Ert3725SCh4aGorAwMBnLjeTy+VITU2FRCJ5qkTM/RhjqK6uxoYNG/Dnn39CKBTC0NAQ48aNw6pVqx5ocqxOKHYibYWSMR1YQEAArKyssG3btmbf8GQyGTQ1NXH06FGMGDECo0ePhra2Ng4ePAiBQID169fD3Ny87QdOnjuMMVy+fBm7d+9GVFQUsrKy8OKLL0IgECA4OBgWFhZPFcgyxpCTk4OioiL4+fkprRSqoKAA48aNg42NDeRyOU6fPo2AgACMHz8e48eP5/oHqJPjx4/jxRdffOD26dOncw2XCSGE3EXxE2kvZDIZTp8+za04ftZScEUiprGxET4+PkorhTp79izefPNNuLu748aNG8jIyEBgYCDGjRuHsLAwWFlZKeVxlIViJ9JWaA1lB8UYg7+/P65cufLQzLMiD7dhwwZ06dIFU6ZMwe7du3HkyBEkJycjJiYGwJ2lkWfPnkVxcfED55BKpa32HNSZTCbD4sWL4eTkBH19fTg7O2PJkiVNuskzxvDFF1+gU6dO0NfXR2BgILKzs1U4avXF4/Hg4eGBb775Bmlpabh48SIGDhyIjRs3olu3bggNDcUff/wBkUj02I79ikRMYWHhM6+IuVdhYSFCQkLQv39/xMbGIj4+Hjdv3sSrr76KgwcP4ssvv1TK4yjb0KFDwRh74B8FE4QQ8iCKn1oXxU/KpampiUGDBuHnn39Gfn4+Dh8+DAcHByxevBiOjo7ca7O6uvqx55LL5UhLS0NDQ4NSEzHJyckYP3485syZg7179yI1NRXp6ekYPHgwfvvtN2zatEkpj6NMFDuRNsNIh3X48GGmoaHBfvzxRyaRSB56nL6+Plu9enWT25ycnNiiRYsYY4zl5OQwV1dXNn369Ec+nlwuZ3K5/JnH3R58++23zNLSku3bt4/l5eWx3bt3MyMjI/bzzz9zxyxfvpyZmpqy6OholpqaykJDQ5mTkxOrq6tT4cjbF7lczrKzs9ny5cuZv78/09LSYoMGDWKrV69m2dnZTCwWs5qamib/zp8/zw4cOMBEItED97X037Vr15iLiwubOnUqk0qlDx0rIYSQ9o/ip9ZD8VPbkMlkLDExkX366afMxcWF6enpsZCQEPb777+zW7duPRA/lZWVsfj4eBYbG8sqKiqUFj+dPn2amZubs+XLlz/0Nf68vPYJaQ4lYzq4VatWsT59+rClS5ey3NxcVlxczA4cOMCKiooYY3cCDj09PZacnMx9j1QqZcbGxmzTpk2MMcZiY2OZjY0Ni4+P544pKipi48aNY4cOHWr2cSUSCZPJZK33xFQsODiYzZgxo8lt48aNY1OmTGGM3bmw2NraspUrV3L3V1RUMF1dXbZjx442HWtHIZfLWX5+Plu9ejUbMGAA09TUZP369WPLli1jV69eZWKxmM2cOZMFBAQwoVCotEAiPz+fubu7s//973+PDMoJIYR0HBQ/tQ6Kn9qeXC5naWlp7IsvvmC9e/dmOjo6bOTIkWz9+vWsoKCAlZWVsWHDhrFXXnmFlZeXKy1+On/+PLO0tGTffPMNJVwIeQgqU+rgZs2ahXnz5iE8PBz9+vXD9OnT8cUXXyAtLQ0A8Mcff2DAgAFwdHTkvicqKgqGhobo3bs3GGNISEiAtrY2Bg0axB2Tk5ODqKgorib64MGD+O2331BZWQkA0NLS4nYSYP8tPU1PT8c777yDS5cutcVTb1WKUpWsrCwAQGpqKk6dOoVRo0YBAPLy8lBcXNxkFwZTU1MEBATgzJkzKhlze8fj8dC1a1d88MEHOHnyJK5fv47Jkyfj8OHD8PDwQEBAALZt24YPPvgAhoaGSnnM27dvY8yYMXBzc8PWrVtb3MSOEEJI+0LxU+ug+KntKUrBv/76a6SlpSE1NRWDBg3Cxo0b4eTkhH79+iEnJwdz585VWmlSRkYGQkJC8O677+Lzzz+nBtaEPAQlYzo4PT09zJgxAykpKcjIyMCnn36Kffv2YcSIEQCA3bt3Izg4GKamptxFf/v27fDx8UH37t1RWlqK2NhY7njgTp1zbGws+Hw+/P39IZVKER8fj48++gi///47hg8fjpkzZyIvLw/AnfpgAPj3339x6tQpFBQUAMBje3+os08//RSTJk2Cm5sbtLW14e3tjfnz52PKlCkAwNWH8/n8Jt/H5/ObrR0nT4fH48He3h5z5szBsWPH8MknnyA/Px++vr6YOnUq+vfvjxUrViAzM7PFr7OKigqEhYWha9eu2LFjh9ICFEIIIeqP4qfWQfGTavF4PLi5uWHRokU4f/48goODUVFRAXt7ewwYMACjRo3C+vXrUVhY2OLXWXZ2NkJCQjB9+nR88803lIgh5BEoGfMcMTMzw9ChQ7kLnFAoREhICAYPHgwNDQ3weDwwxnDmzBmMHDkSpqamKCoqQkpKCqZNm8adp6ysDPv27cOYMWMAAPn5+UhOToaJiQmqq6sxZ84cXL16FbNnzwZjjFtNcPLkSXh7e8Pf3587l1wub8OfgPLs2rUL27Ztw/bt25GcnIwtW7Zg1apV2LJli6qH9txZtWoVNmzYgISEBMTGxqKoqAjz5s1DYmIiXnjhBfj7+2Pp0qVIT09/4tdbVVUVBAIBbGxssHv3bujo6LTysyCEEKKuKH5SHoqf1INMJsMbb7yB7OxspKamIiEhAdeuXYNAIEBUVBTc3NwwfPhwrFmzBgUFBU+cmMnLy0NISAgmTpyI5cuXc6u8CCEPoYLSKKLG9uzZw3g8HouNjWWMMXbmzBnG4/HY7du3uWNiY2OZpqYmO3v2LGOMsejoaObo6Mj++usv7pi///6bdenShauTzsjIYL6+vuzbb799qvGoa1O7zp07s19//bXJbUuWLGGurq6MMcauXbvGALCUlJQmxwwePJjNnTu3rYbZ4R07doyZm5s3qdlXkMvlrLy8nG3ZsoWFhYUxfX191qNHD/bRRx+xU6dOserq6mZrnIVCIevfvz8bNmwYq62tVcGzIoQQ0t5Q/PRkKH5SDz/99BNzdXXleiDdSy6Xs5s3b7I1a9awoUOHMi0tLebr68uWLFnC0tLSmt08oaamhl29epV17dqVvfvuux267xEhykTpSsJhjCE4OBinT5+Gt7c3AMDAwACurq74888/IZPJcOLECSxduhQWFhYICAiATCZDWloajIyMEBwczJ3LxMQEJiYm3NaNhw8fBo/HQ0BAAIA7GfmjR48iLi7ukWPi8Xhqubyxtrb2gWy/pqYmN1Pl5OQEW1tbxMbGcvdXVVXh3Llz6NevX5uOtSMbOnQoLl68yL1e78Xj8WBmZoZp06YhOjoaQqEQ33zzDfLz8zFy5Eh4eHhg4cKFOH/+PPd7q62txcSJE6GlpYWYmBjo6+u39VMihBDSzlD89OQoflIP77zzDk6cOAFbW9sH7ru/FPzGjRt48803ER8fD19fX64UPCMjg1sxU1hYiODgYAQGBmLt2rW0IoaQJ6XiZBBRc1KplK1evZrp6+szf39/Nn36dMbj8dgHH3zAGGMsOzubCQQCNnnyZO57Ghsb2bJly5izszNraGhgjN3plP/666+znJwcduTIERYQEMD69OnDXn31VVZYWNjsY+/atYsNGzaMicXi1n+iT2n69OnM3t6e25oxMjKSWVlZsU8++YQ7Zvny5czMzIzFxMSwtLQ0FhYWRlszqgmxWMzCw8PZ5MmTmYmJCXNwcGBvv/028/X1Zf369WOVlZWqHiIhhJB2jOKn5lH81H7J5XJWWlrK/vjjDzZ69Gimo6PD3N3d2dy5c5mjoyN79dVXmVQqVfUwCWlXKBlDntjZs2fZ5cuXmY2NDTt27BhjjLGoqCjm7e3Ntm3bxh13/fp1FhISwiZOnMgYYywrK4t5enoyd3d3JhAImL29PVuzZg27devWIx9vwoQJbNCgQdwyW5lMpjZv8lVVVWzevHmsS5cuTE9Pj3Xr1o0tWrSIC54Yu3PRWrx4MePz+UxXV5e99NJLLDMzU4WjJs2pra1lMTExLCgoiJmZmbHy8nJVD4kQQkgHQvHTXRQ/dQyKUvC//vqLeXp6MhcXFyaRSFQ9LELaHR5j7bglO1G5H374AStXrkRqaipsbGwAALGxsZg5cya++eYbTJo0Cb/88gt27NiBrKwsdO/eHXFxcVz5B2Os2WW01dXVcHBwwE8//YTXXnvtgfsVOwxoamq23pMjzx2ZTEavKUIIIa2O4ifSkVD8REjLUEEfeSYffPABTpw4wQUSMpkMJ0+eRFVVFbdbQEJCAuzs7PDDDz+gvr4e165d4459WD3z8ePH0dDQgGHDhgG4s5PA2LFjIRaLIZVKoampSW/6ROnoNUUIIaQtUPxEOhJ6TRHSMpSMIc/MxcWFa+All8vRpUsXhISEwNDQEFlZWbh8+TI8PT0xbdo0mJmZYf369QAe/ca9fft2DBgwAF26dIFUKsWxY8cQExODP//8E66urhgyZAji4+Ob/V6ZTMbN/GRmZiI1NRUAnnhbPkIIIYSQ1kbxEyGEPN8oGUOUQjFDo62tjRkzZuD//u//AADl5eVwdnZGz549AQCLFi3Crl27cPXq1Yeeq7a2FkeOHMGECRMA3Omiv2fPHri7u8PQ0BCHDx+Gubk5li1bBrFY/ECQcO+sz86dOzF8+HDcunVLLXcVIIQQQsjzi+InQgh5flEyhrSqgIAAxMTEQCAQAACGDx+Ovn374vPPP4dMJuO2MrzXiRMnUFNTg5EjRwIArl69ipSUFGzatAlvvPEGunfvjrfffhtnzpxBYmIiFySkpKTgq6++wqpVq1BbW4vKykpkZWWhf//+sLe3fy5mduLj4zFmzBjY2dmBx+MhOjq6yf2MMXzxxRfo1KkT9PX1ERgYiOzs7CbHlJWVYcqUKTAxMYGZmRneeOMNiMXiNnwWhBBCyPON4qe2RfETIUQVKBlD2oSWlhb3/7Vr1yI4OBhVVVXQ0HjwJbhr1y4EBATAyckJjDHs378fjo6O6Nu3LxcQmJubo6qqCn369AEAbNmyBUFBQTh+/Di2bt2KkJAQ7N69G9evX8dLL70E4G7TOsX/O2JwUVNTA09PT6xdu7bZ+7///nusWbMGGzZswLlz52BoaIiRI0eivr6eO2bKlClIT0/Hv//+i3379iE+Ph5vv/12Wz0FQgghhPyH4qe2QfETIUQl2nbzJkIerba2luno6LBffvmFMcZYeXk569WrF/v4448ZY4zbpnHBggXMxcWFMcbYpUuXmKOjI/voo49YdXU1k8vlbMmSJczDw4O5uLiwa9euqebJqBgAFhUVxX0tl8uZra0tW7lyJXdbRUUF09XVZTt27GCMMXblyhUGgF24cIE75uDBg4zH4z12K01CCCGEqAbFT8pD8RMhpK3QyhiiVkpLS+Hl5YURI0YAANLT03H16lVMnToVwJ3a6rq6OkRHR3NLd7ds2QJLS0u88847MDIyAo/Hw5gxY5CVlYVevXqhW7duYIyhsLAQP//8M8LCwrBo0SLk5+c/8Pj3zv50NHl5eSguLkZgYCB3m6mpKQICAnDmzBkAwJkzZ2BmZgY/Pz/umMDAQGhoaODcuXNtPmZCCCGEPB7FT62H4idCSGuhZAxRKw4ODjh37hxcXFwAAFeuXIGVlRW3nBa4UwOdl5eHyZMnA7izjePgwYNhZ2fHHVNYWAg7OzsMGTIEAHDkyBHMnDkT27ZtQ//+/XH69GmEhobi+PHjTR7//h0KWAdailtcXAwA4PP5TW7n8/ncfcXFxdw2mwpaWlqwsLDgjiGEEEKIeqH4qfVQ/EQIaS2UjCFq7a233kJubi6Auxf2LVu2wMTEBF5eXhCLxbC3t4dMJoOBgQH3fWlpadDW1kZQUBCAO7W+9fX1OHToEBYsWIC4uDj4+/tj1apVAIDc3FyMGzcOUVFRTR6fdhAgrWXt2rVwdHSEnp4eAgICcP78eVUPiRBCSAdB8RPpqCh+Ih0JJWOI2jM0NARw98I+dOhQfPvttwAAIyMj9O7dG0ePHuU61icnJ+Ovv/5Ct27d4Orqiry8PMTFxSEtLQ3du3dHSEgIDhw4gBEjRqC+vh4ZGRno1q0b5HI5duzYwT1uYWEhMjIy2vjZth5bW1sAgFAobHK7UCjk7rO1tYVIJGpyv1QqRVlZGXcMeXY7d+7EBx98gC+//BLJycnw9PTEyJEjH/jZE0IIIS1F8ZNyUPykPih+Ih2OKhvWEKIMaWlprE+fPqxbt25s7ty5rG/fvozH47Fff/2VMcbYn3/+yWxtbdm1a9fYqVOn2Ny5c1m3bt2YtrY2MzQ0ZBKJhDHGWExMDOvatSu7ffs2S0xMZF5eXszd3Z2dPn1alU+vxfCQBnSrVq3ibqusrGy2AV1iYiJ3zOHDh6kBnZL5+/uzWbNmcV/LZDJmZ2fHli1bpsJREUIIeZ5Q/NQ8ip/UF8VPpKPRenSqhhD15+HhgbNnz+Kff/5BSUkJQkJCkJ2djaFDhwIALCwsYGBgAKFQiAEDBmDAgAFYvXo1kpKSIBQKuW0jfXx8oKmpiWXLlmH9+vWYMGEC1q5dy80stQdisRg5OTnc13l5ebh48SIsLCzQpUsXzJ8/H0uXLkWPHj3g5OSExYsXw87Ojmvm17NnTwQFBeGtt97Chg0bIJFIMHv2bEyaNKlJTTlpucbGRiQlJWHhwoXcbRoaGggMDOQaARJCCCGtjeKnuyh+Un8UP5EOSdXZIEKUTSaTsZMnT3JfV1RUsAEDBrD58+czsVjMGGPcbM69EhMTWY8ePVinTp3YDz/8wN2u2A6yPYiLi2MAHvg3ffp0xtid57J48WLG5/OZrq4ue+mll1hmZmaTc9y+fZtNnjyZGRkZMRMTE/b666+z6upqFTybjunWrVsMwAMzhh9//DHz9/dX0agIIYQ87yh+ovhJnVH8RDoiHmMdqN05IfdgjHF10vHx8Xj77bdRU1OD0aNHw9nZGdbW1njttdfA4/Fw4MABzJ8/HxoaGtDV1cXhw4dha2vb5ByEKENhYSHs7e1x+vRp9OvXj7v9k08+wYkTJ2gLTEIIISpF8RNRRxQ/kY6IGviSDuveIGDw4MHIyMjAL7/8gsbGRuTn56NPnz4oLy/Hd999h8mTJyM0NBQHDx5ETk4Otw0hBRJE2aysrKCpqfnIRoCEEEKIqlD8RNQRxU+kI6KeMeS5IhAIuPpeANixYweioqLwww8/4I033kBjYyMGDBiA7du3w8vLS2XjJB2Xjo4OfH19ERsby70W5XI5YmNjMXv2bNUOjhBCCGkGxU9E1Sh+Ih0RlSkRcp8FCxbg8OHDOH36NPT19Wl2hyjdzp07MX36dPz222/w9/fHTz/9hF27diEjIwN8Pl/VwyOEEEKeGsVPpLVR/EQ6GipTIuQ+s2fPRnBwMKqqqiiQaCXx8fEYM2YM7OzswOPxEB0dzd0nkUiwYMECeHh4wNDQEHZ2dpg2bRoKCwubnKOsrAxTpkyBiYkJzMzM8MYbb0AsFrfxM2mZ//3vf1i1ahW++OILeHl54eLFizh06BAFEoQQQtotip9aH8VPFD+RjoVWxhBC2tzBgweRkJAAX19fjBs3DlFRUdyS08rKSkyYMAFvvfUWPD09UV5ejnnz5kEmkyExMZE7x6hRo1BUVITffvsNEokEr7/+Ovr27Yvt27er6FkRQgghhLQeip8I6VgoGUMIUSkej9ckmGjOhQsX4O/vj+vXr6NLly64evUq3N3dceHCBfj5+QEADh06hNGjR+PmzZuws7Nro9ETQgghhLQ9ip8Iaf+oTIkQovYqKyvB4/FgZmYGADhz5gzMzMy4QAIAAgMDoaGhQVsbEkIIIYSA4idC1B0lYwghaq2+vh4LFizA5MmTYWJiAgAoLi6GjY1Nk+O0tLRgYWHBbatJCCGEEPK8oviJEPVHyRhCiNqSSCR4+eWXwRjD+vXrVT0cQgghhBC1R/ETIe2DlqoHQAghzVEEEtevX8exY8e4WR0AsLW1hUgkanK8VCpFWVkZbG1t23qohBBCCCFqgeInQtoPWhlDCFE7ikAiOzsbR48ehaWlZZP7+/Xrh4qKCiQlJXG3HTt2DHK5HAEBAW09XEIIIYQQlaP4iZD2hVbGEELanFgsRk5ODvd1Xl4eLl68CAsLC3Tq1AkTJkxAcnIy9u3bB5lMxtUxW1hYQEdHBz179kRQUBDeeustbNiwARKJBLNnz8akSZNoJwBCCCGEdEgUPxHSsdDW1oSQNnf8+HG8+OKLD9w+ffp0fPXVV3Bycmr2++Li4jB06FAAQFlZGWbPno29e/dCQ0MD48ePx5o1a2BkZNSaQyeEEEIIUQmKnwjpWCgZQwghhBBCCCGEENKGqGcMIYQQQgghhBBCSBuiZAwhhBBCCCGEEEJIG6JkDCGEEEIIIYQQQkgbomQMIYQQQgghhBBCSBuiZAwhhBBCCCGEEEJIG6JkDCGEEEIIIYQQQkgbomQMIYQQQgghhBBCSBuiZAwhhBBCCCGEEEJIG6JkDCGEEEIIIYQQQkgbomQMIYQQQgghhBBCSBuiZAwhhBBCCCGEEEJIG6JkDCGEEEIIIYQQQkgb+n/3gulINUE02QAAAABJRU5ErkJggg==", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from turtle import left\n", "import matplotlib.pyplot as plt\n", @@ -19317,83 +2651,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "notebookRunGroups": { "groupValue": "" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 3s 3s/step\n", - "20/20 [==============================] - 2s 101ms/step\n", - "The accuracy of the model on validation data is 87.50%(87.50000%)\n", - "The accuracy of the model on test data is 96.96%(96.95513%)\n" - ] - }, - { - "data": { - "image/png": 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", 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"\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(img_array, model, last_conv_layer_name, second_last_conv_layer_name, pred_index, sensitivity_map)\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[0mheatmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheatmap\u001b[0m \u001b[1;33m**\u001b[0m \u001b[0msensitivity_map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msecond_last_conv_layer_name\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 56\u001b[0m \u001b[1;31m# Compute heatmap for the second last convolutional layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 57\u001b[1;33m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_compute_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond_last_conv_layer_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 58\u001b[0m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheatmap_second\u001b[0m \u001b[1;33m**\u001b[0m \u001b[0msensitivity_map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;31m# Average the two heatmaps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\Utils\\Grad_cam.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(model, img_array, conv_layer_name, pred_index)\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_layer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv_layer_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m )\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrad_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[0mclass_channel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclass_channel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconv_layer_output\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mcopied_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcopied_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 555\u001b[0m \u001b[0mlayout_map_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_subclass_model_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_layout_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1093\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1094\u001b[0m with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m 1095\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1096\u001b[0m ):\n\u001b[1;32m-> 1097\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 507\u001b[0m \u001b[0mA\u001b[0m \u001b[0mtensor\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0ma\u001b[0m \u001b[0msingle\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 508\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtensors\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mare\u001b[0m \u001b[0mmore\u001b[0m \u001b[0mthan\u001b[0m \u001b[0mone\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 509\u001b[0m \"\"\"\n\u001b[1;32m--> 510\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_internal_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_id\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtensor_dict\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mt_id\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat_input_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mcontinue\u001b[0m \u001b[1;31m# Node is not computable, try skipping.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 666\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 667\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 669\u001b[0m \u001b[1;31m# Update tensor_dict.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 670\u001b[0m for x_id, y in zip(\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1093\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1094\u001b[0m with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m 1095\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1096\u001b[0m ):\n\u001b[1;32m-> 1097\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1099\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[0mnew_e\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mnew_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mbound_signature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\layers\\reshaping\\reshape.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 138\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;31m# Set the static shape for the result since it might lost during\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;31m# array_ops reshape, eg, some `None` dim in the result could be\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1174\u001b[0m \u001b[1;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1176\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1178\u001b[0m \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[1;31m# TypeError, when given unexpected types. So we need to catch both.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_dispatch_handler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, out_type, name)\u001b[0m\n\u001b[0;32m 625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 626\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mout_type\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 628\u001b[0m \"\"\"\n\u001b[1;32m--> 629\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1174\u001b[0m \u001b[1;31m# Fallback dispatch system (dispatch v1):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1175\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1176\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdispatch_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1177\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1178\u001b[0m \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1179\u001b[0m \u001b[1;31m# TypeError, when given unexpected types. So we need to catch both.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1180\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_dispatch_handler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, name, out_type)\u001b[0m\n\u001b[0;32m 652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 653\u001b[0m \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 654\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mout_type\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 655\u001b[0m \"\"\"\n\u001b[1;32m--> 656\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mshape_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(input, name, optimize, out_type)\u001b[0m\n\u001b[0;32m 693\u001b[0m input_shape)\n\u001b[0;32m 694\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 695\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mout_type\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 696\u001b[0m \u001b[0mout_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 697\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0m_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 9360\u001b[1;33m \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9361\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9362\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9363\u001b[0m return shape_eager_fallback(\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", diff --git a/README.md b/README.md index 67fb40b..c6782d2 100644 --- a/README.md +++ b/README.md @@ -1,124 +1,124 @@ -# Pneumonia Detection AI πŸ€– - - - - -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![CodeQL](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml/badge.svg?branch=main)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml) -[![Dependency Review](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml)\ -[![Python application](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml) -[![Python Test [Beta-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml)\ -[![Python Test [Alpha-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml) - -### This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model. -> [!IMPORTANT] -> The code that have achived the highest acc is `backup/V6/Model_T&T.ipynb`.\ -> And the code with the light model is `backup/V7/Model_T&T.ipynb`. - -## Usage -> [!TIP] -> If you just want the model go to the Github Releases. - -The project includes a Command Line Interface (CLI) for easy use of the model. The CLI, which is based on the [Python CLI template](https://github.com/Aydinhamedi/Python-CLI-template) from the same author, provides a user-friendly, colorful interface that allows you to interact with the model. you can fined the CLI in - -``` -Interface\CLI -``` -Additionally, a Graphical User Interface (GUI) is available. you can fined the GUI in -``` -Interface\GUI -``` -### Example Image of the CLI (V0.8.9.3) ‡ -![Example](doc/Other/CLI_V0.8.9.3.png) -### Example Image of the GUI (V0.8.9.6) ‡ -![Example](doc/Other/GUI_V0.8.9.6.png) -## Release -> ### Newest release πŸ“ƒ -> #### [Go to newest release](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest) - -## Training System Specifications - -- **Graphics Card (GPU)**: RTX 3090 -- **Memory (RAM)**: 64GB -- **Operating System (OS)**: Windows 11 Pro -- **Processor (CPU)**: Intel Core i7-12700KF - -## Model - -The model is a Convolutional Neural Network (CNN) trained on a dataset of 23681 X-ray images. The dataset is a combination of the following: - -- Chest X-ray Pneumonia dataset from Kaggle -- Covid19-Pneumonia-Normal Chest X-Ray Images from Mendeley -- RSNA dataset - -This combined dataset provides a comprehensive set of images for training the model. - -## Training Methods -### The AI model supports two distinct training approaches: - -- rev1: A straightforward method using Keras' fit function for basic training. -- rev2: An enhanced training strategy incorporating data augmentation and subset training for improved accuracy and generalization. -### rev2 Training Simplified: -- Memory Optimization: Begins with clearing system memory to ensure efficient resource utilization. -- Hyperparameter Setup: Configures essential training parameters such as epoch count and batch size. -- Data Enrichment: Utilizes data augmentation techniques to introduce variability in the training dataset. -- Focused Training: Implements training on data subsets to reduce overfitting and streamline the learning process. -- Adaptive Learning Rate: Applies a dynamic learning rate schedule to fine-tune the training progression. -- Training Supervision: Uses callbacks for monitoring training, saving the best model, and enabling early stopping. -- Progressive Learning: Trains the model iteratively on subsets, evaluating and adjusting after each epoch. -- Data Standardization: Normalizes image inputs to facilitate model training. -- Robustness Enhancement: Introduces random noise to training images to strengthen model robustness against unseen data. -- While rev1 is suitable for quick and simple model training, rev2 is tailored for those seeking a more sophisticated and potentially more effective training regimen. - -## Repository Structure - -Please note that due to the large size of some files and folders, they are not available directly in the repository. However, they can be found in the [Releases](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases) page of the repository. This includes the model weights and the database, which are crucial for the functioning of the AI model. - -## Contribution - -Any contributions to improve the project are welcome. You can submit a pull request or open an issue on GitHub. Please make sure to test your changes thoroughly before submitting. We appreciate your help in making this project better. - -## WARNING -> [!CAUTION] -The model provided in this project should not be used for medical diagnosis without further validation. While the model has shown high accuracy in detecting pneumonia from X-ray images, it is not a substitute for professional medical advice. Please consult with a healthcare professional for medical advice. - - -## Other -> [!NOTE] -> Please note that this code uses my: -> - Python-CLI-template -> - for more info go to https://github.com/Aydinhamedi/Python-CLI-template. -> - Python-color-print-V2 -> - for more info go to https://github.com/Aydinhamedi/Python-color-print-V2. -> - Python-color-print -> - for more info go to https://github.com/Aydinhamedi/Python-color-print. - -## Results - -> [!WARNING] -> Results were achived using Rev2 training method and Rev1.2 model and -> with `backup/V6/Model_T&T.ipynb` code. - -### Acc: -![img_](doc/V6/D1.png) -### Grad cam: -![img_](doc/V6+/D1.png) -![img_](doc/V6+/D2.png) -![img_](doc/V6+/D3.png) -### Other: -![img_](doc/V6/D4.png) - - - - -## License - -This project is open-source and is licensed under the MIT License. See the `LICENSE` file for details. +# Pneumonia Detection AI πŸ€– + + + + +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![CodeQL](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml/badge.svg?branch=main)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml) +[![Dependency Review](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml)\ +[![Python application](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml) +[![Python Test [Beta-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Beta-b.yml)\ +[![Python Test [Alpha-b]](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app_Alpha-b.yml) + +### This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model. +> [!IMPORTANT] +> The code that have achived the highest acc is `backup/V6/Model_T&T.ipynb`.\ +> And the code with the light model is `backup/V7/Model_T&T.ipynb`. + +## Usage +> [!TIP] +> If you just want the model go to the Github Releases. + +The project includes a Command Line Interface (CLI) for easy use of the model. The CLI, which is based on the [Python CLI template](https://github.com/Aydinhamedi/Python-CLI-template) from the same author, provides a user-friendly, colorful interface that allows you to interact with the model. you can fined the CLI in + +``` +Interface\CLI +``` +Additionally, a Graphical User Interface (GUI) is available. you can fined the GUI in +``` +Interface\GUI +``` +### Example Image of the CLI (V0.8.9.3) ‡ +![Example](doc/Other/CLI_V0.8.9.3.png) +### Example Image of the GUI (V0.8.9.6) ‡ +![Example](doc/Other/GUI_V0.8.9.6.png) +## Release +> ### Newest release πŸ“ƒ +> #### [Go to newest release](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases/latest) + +## Training System Specifications + +- **Graphics Card (GPU)**: RTX 3090 +- **Memory (RAM)**: 64GB +- **Operating System (OS)**: Windows 11 Pro +- **Processor (CPU)**: Intel Core i7-12700KF + +## Model + +The model is a Convolutional Neural Network (CNN) trained on a dataset of 23681 X-ray images. The dataset is a combination of the following: + +- Chest X-ray Pneumonia dataset from Kaggle +- Covid19-Pneumonia-Normal Chest X-Ray Images from Mendeley +- RSNA dataset + +This combined dataset provides a comprehensive set of images for training the model. + +## Training Methods +### The AI model supports two distinct training approaches: + +- rev1: A straightforward method using Keras' fit function for basic training. +- rev2: An enhanced training strategy incorporating data augmentation and subset training for improved accuracy and generalization. +### rev2 Training Simplified: +- Memory Optimization: Begins with clearing system memory to ensure efficient resource utilization. +- Hyperparameter Setup: Configures essential training parameters such as epoch count and batch size. +- Data Enrichment: Utilizes data augmentation techniques to introduce variability in the training dataset. +- Focused Training: Implements training on data subsets to reduce overfitting and streamline the learning process. +- Adaptive Learning Rate: Applies a dynamic learning rate schedule to fine-tune the training progression. +- Training Supervision: Uses callbacks for monitoring training, saving the best model, and enabling early stopping. +- Progressive Learning: Trains the model iteratively on subsets, evaluating and adjusting after each epoch. +- Data Standardization: Normalizes image inputs to facilitate model training. +- Robustness Enhancement: Introduces random noise to training images to strengthen model robustness against unseen data. +- While rev1 is suitable for quick and simple model training, rev2 is tailored for those seeking a more sophisticated and potentially more effective training regimen. + +## Repository Structure + +Please note that due to the large size of some files and folders, they are not available directly in the repository. However, they can be found in the [Releases](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/releases) page of the repository. This includes the model weights and the database, which are crucial for the functioning of the AI model. + +## Contribution + +Any contributions to improve the project are welcome. You can submit a pull request or open an issue on GitHub. Please make sure to test your changes thoroughly before submitting. We appreciate your help in making this project better. + +## WARNING +> [!CAUTION] +The model provided in this project should not be used for medical diagnosis without further validation. While the model has shown high accuracy in detecting pneumonia from X-ray images, it is not a substitute for professional medical advice. Please consult with a healthcare professional for medical advice. + + +## Other +> [!NOTE] +> Please note that this code uses my: +> - Python-CLI-template +> - for more info go to https://github.com/Aydinhamedi/Python-CLI-template. +> - Python-color-print-V2 +> - for more info go to https://github.com/Aydinhamedi/Python-color-print-V2. +> - Python-color-print +> - for more info go to https://github.com/Aydinhamedi/Python-color-print. + +## Results + +> [!WARNING] +> Results were achived using Rev2 training method and Rev1.2 model and +> with `backup/V6/Model_T&T.ipynb` code. + +### Acc: +![img_](doc/V6/D1.png) +### Grad cam: +![img_](doc/V6+/D1.png) +![img_](doc/V6+/D2.png) +![img_](doc/V6+/D3.png) +### Other: +![img_](doc/V6/D4.png) + + + + +## License + +This project is open-source and is licensed under the MIT License. See the `LICENSE` file for details. diff --git a/SECURITY.md b/SECURITY.md index 8c3e1be..af2621c 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,4 +1,4 @@ -# Security Policy - -> [!IMPORTANT] -> The project doesnt have any Security Policy. +# Security Policy + +> [!IMPORTANT] +> The project doesnt have any Security Policy. diff --git a/Temp/temp.ans b/Temp/temp.ans index 89cc95c..fd415a6 100644 --- a/Temp/temp.ans +++ b/Temp/temp.ans @@ -1,4779 +1,4779 @@ -Training the model... - -Setup Verbose: -Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... -Use_extended_tensorboard [False]. -Debug_OUTPUT_DPS [True]. -OneCycleLr_UFTS [False]. -Setup Verbose END. - -Epoch: 1/486 (TSEC: 0) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Fitting ImageDataGenerator... -- ImageDataGenerator fit done. -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 -Epoch 2/6 -128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 -Epoch 3/6 -128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 -Epoch 4/6 -128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 -Epoch 5/6 -128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 -Epoch 6/6 -128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... -Model Test acc: 0.8974 -Model Test loss: 2.4019 -Improved model accuracy from 0 to 0.8974359035491943. Saving model. -Saving full model H5 format... -Improved model loss from inf to 2.4019267559051514. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 676.74 sec -Time taken for epoch(SUBo): 271.12 sec -Time taken for epoch(OTHERo): 405.62 sec -<---------------------------------------|Epoch [1] END|---------------------------------------> - -Epoch: 2/486 (TSEC: 6) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 7/12 -128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 -Epoch 8/12 -128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 -Epoch 9/12 -128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 -Epoch 10/12 -128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 -Epoch 11/12 -128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 -Epoch 12/12 -128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... -Model Test acc: 0.9407 -Model Test loss: 0.5406 -Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. -Saving full model H5 format... -Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.91 sec -Time taken for epoch(SUBo): 257.59 sec -Time taken for epoch(OTHERo): 68.33 sec -<---------------------------------------|Epoch [2] END|---------------------------------------> - -Epoch: 3/486 (TSEC: 12) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 13/18 -128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 -Epoch 14/18 -128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 -Epoch 15/18 -128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 -Epoch 16/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 -Epoch 17/18 -128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 -Epoch 18/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... -Model Test acc: 0.9423 -Model Test loss: 0.4656 -Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. -Saving full model H5 format... -Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 324.58 sec -Time taken for epoch(SUBo): 255.82 sec -Time taken for epoch(OTHERo): 68.76 sec -<---------------------------------------|Epoch [3] END|---------------------------------------> - -Epoch: 4/486 (TSEC: 18) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 19/24 -128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 -Epoch 20/24 -128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 -Epoch 21/24 -128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 -Epoch 22/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 -Epoch 23/24 -128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 -Epoch 24/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... -Model Test acc: 0.9343 -Model Test loss: 0.4089 -Model accuracy did not improve from 0.942307710647583. Not saving model. -Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 323.62 sec -Time taken for epoch(SUBo): 256.60 sec -Time taken for epoch(OTHERo): 67.02 sec -<---------------------------------------|Epoch [4] END|---------------------------------------> - -Epoch: 5/486 (TSEC: 24) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 25/30 -128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 -Epoch 26/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 -Epoch 27/30 -128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 -Epoch 28/30 -128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 -Epoch 29/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 -Epoch 30/30 -128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.2338 -Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. -Saving full model H5 format... -Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.89 sec -Time taken for epoch(SUBo): 258.52 sec -Time taken for epoch(OTHERo): 67.37 sec -<---------------------------------------|Epoch [5] END|---------------------------------------> - -Epoch: 6/486 (TSEC: 30) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 31/36 -128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 -Epoch 32/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 -Epoch 33/36 -128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 -Epoch 34/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 -Epoch 35/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 -Epoch 36/36 -128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2253 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 321.73 sec -Time taken for epoch(SUBo): 256.17 sec -Time taken for epoch(OTHERo): 65.57 sec -<---------------------------------------|Epoch [6] END|---------------------------------------> - -Epoch: 7/486 (TSEC: 36) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 37/42 -128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 -Epoch 38/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 -Epoch 39/42 -128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 -Epoch 40/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 -Epoch 41/42 -128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 -Epoch 42/42 -128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2070 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 326.03 sec -Time taken for epoch(SUBo): 259.27 sec -Time taken for epoch(OTHERo): 66.76 sec -<---------------------------------------|Epoch [7] END|---------------------------------------> - -Epoch: 8/486 (TSEC: 42) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 43/48 -128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 -Epoch 44/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 -Epoch 45/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 -Epoch 46/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 -Epoch 47/48 -128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 -Epoch 48/48 -128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2199 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Model loss did not improve from 0.20697814226150513. Not saving model. -Time taken for epoch(FULL): 322.67 sec -Time taken for epoch(SUBo): 256.59 sec -Time taken for epoch(OTHERo): 66.08 sec -<---------------------------------------|Epoch [8] END|---------------------------------------> - -Epoch: 9/486 (TSEC: 48) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 49/54 -128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 -Epoch 50/54 -128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 -Epoch 51/54 -128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 -Epoch 52/54 -128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 -Epoch 53/54 -128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 -Epoch 54/54 -128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.1662 -Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. -Saving full model H5 format... -Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 327.90 sec -Time taken for epoch(SUBo): 257.53 sec -Time taken for epoch(OTHERo): 70.37 sec -<---------------------------------------|Epoch [9] END|---------------------------------------> - -Epoch: 10/486 (TSEC: 54) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 55/60 -128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 -Epoch 56/60 -128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 -Epoch 57/60 -128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 -Epoch 58/60 -128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 -Epoch 59/60 -128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 -Epoch 60/60 -128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2059 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 327.86 sec -Time taken for epoch(SUBo): 259.66 sec -Time taken for epoch(OTHERo): 68.20 sec -<---------------------------------------|Epoch [10] END|---------------------------------------> - -Epoch: 11/486 (TSEC: 60) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 61/66 -128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 -Epoch 62/66 -128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 -Epoch 63/66 -128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 -Epoch 64/66 -128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 -Epoch 65/66 -128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 -Epoch 66/66 -128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2938 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 257.49 sec -Time taken for epoch(OTHERo): 69.06 sec -<---------------------------------------|Epoch [11] END|---------------------------------------> - -Epoch: 12/486 (TSEC: 66) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 67/72 -128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 -Epoch 68/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 -Epoch 69/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 -Epoch 70/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 -Epoch 71/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 -Epoch 72/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... -Model Test acc: 0.9631 -Model Test loss: 0.2115 -Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 324.68 sec -Time taken for epoch(SUBo): 253.65 sec -Time taken for epoch(OTHERo): 71.03 sec -<---------------------------------------|Epoch [12] END|---------------------------------------> - -Epoch: 13/486 (TSEC: 72) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 73/78 -128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 -Epoch 74/78 -128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 -Epoch 75/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 -Epoch 76/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 -Epoch 77/78 -128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 -Epoch 78/78 -128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.1545 -Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. -Saving full model H5 format... -Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.97 sec -Time taken for epoch(SUBo): 251.55 sec -Time taken for epoch(OTHERo): 74.42 sec -<---------------------------------------|Epoch [13] END|---------------------------------------> - -Epoch: 14/486 (TSEC: 78) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 79/84 -128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 -Epoch 80/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 -Epoch 81/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 -Epoch 82/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 -Epoch 83/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 -Epoch 84/84 -128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1780 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 71.59 sec -<---------------------------------------|Epoch [14] END|---------------------------------------> - -Epoch: 15/486 (TSEC: 84) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 85/90 -128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 -Epoch 86/90 -128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 -Epoch 87/90 -128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 -Epoch 88/90 -128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 -Epoch 89/90 -128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 -Epoch 90/90 -128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... -Model Test acc: 0.9615 -Model Test loss: 0.2233 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.17 sec -Time taken for epoch(SUBo): 252.81 sec -Time taken for epoch(OTHERo): 70.36 sec -<---------------------------------------|Epoch [15] END|---------------------------------------> - -Epoch: 16/486 (TSEC: 90) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 91/96 -128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 -Epoch 92/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 -Epoch 93/96 -128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 -Epoch 94/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 -Epoch 95/96 -128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 -Epoch 96/96 -128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1648 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 319.96 sec -Time taken for epoch(SUBo): 249.52 sec -Time taken for epoch(OTHERo): 70.43 sec -<---------------------------------------|Epoch [16] END|---------------------------------------> - -Epoch: 17/486 (TSEC: 96) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 97/102 -128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 -Epoch 98/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 -Epoch 99/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 -Epoch 100/102 -128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 -Epoch 101/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 -Epoch 102/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1764 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.46 sec -Time taken for epoch(SUBo): 253.14 sec -Time taken for epoch(OTHERo): 67.31 sec -<---------------------------------------|Epoch [17] END|---------------------------------------> - -Epoch: 18/486 (TSEC: 102) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 103/108 -128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 -Epoch 104/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 -Epoch 105/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 -Epoch 106/108 -128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 -Epoch 107/108 -128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 -Epoch 108/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2677 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.64 sec -Time taken for epoch(SUBo): 253.71 sec -Time taken for epoch(OTHERo): 70.93 sec -<---------------------------------------|Epoch [18] END|---------------------------------------> - -Epoch: 19/486 (TSEC: 108) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 109/114 -128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 -Epoch 110/114 -128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 -Epoch 111/114 -128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 -Epoch 112/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 -Epoch 113/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 -Epoch 114/114 -128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2055 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.02 sec -Time taken for epoch(SUBo): 253.02 sec -Time taken for epoch(OTHERo): 69.00 sec -<---------------------------------------|Epoch [19] END|---------------------------------------> - -Epoch: 20/486 (TSEC: 114) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 115/120 -128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 -Epoch 116/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 -Epoch 117/120 -128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 -Epoch 118/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 -Epoch 119/120 -128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 -Epoch 120/120 -128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... -Model Test acc: 0.9439 -Model Test loss: 0.3367 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 252.69 sec -Time taken for epoch(OTHERo): 70.57 sec -<---------------------------------------|Epoch [20] END|---------------------------------------> - -Epoch: 21/486 (TSEC: 120) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 121/126 -128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 -Epoch 122/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 -Epoch 123/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 -Epoch 124/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 -Epoch 125/126 -128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 -Epoch 126/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... -Model Test acc: 0.9583 -Model Test loss: 0.2373 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.10 sec -Time taken for epoch(SUBo): 252.57 sec -Time taken for epoch(OTHERo): 68.53 sec -<---------------------------------------|Epoch [21] END|---------------------------------------> - -Epoch: 22/486 (TSEC: 126) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 127/132 -128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 -Epoch 128/132 -128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 -Epoch 129/132 -128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 -Epoch 130/132 -128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 -Epoch 131/132 -128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 -Epoch 132/132 -128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2035 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.92 sec -Time taken for epoch(SUBo): 252.61 sec -Time taken for epoch(OTHERo): 69.31 sec -<---------------------------------------|Epoch [22] END|---------------------------------------> - -Epoch: 23/486 (TSEC: 132) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 133/138 -128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 -Epoch 134/138 -128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 -Epoch 135/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 -Epoch 136/138 -128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 -Epoch 137/138 -128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 -Epoch 138/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1983 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 251.30 sec -Time taken for epoch(OTHERo): 71.96 sec -<---------------------------------------|Epoch [23] END|---------------------------------------> - -Epoch: 24/486 (TSEC: 138) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01094]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 139/144 -128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 -Epoch 140/144 -128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 -Epoch 141/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 -Epoch 142/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 -Epoch 143/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 144/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... -Model Test acc: 0.9567 -Model Test loss: 0.2415 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.37 sec -Time taken for epoch(SUBo): 251.97 sec -Time taken for epoch(OTHERo): 72.40 sec -<---------------------------------------|Epoch [24] END|---------------------------------------> - -Epoch: 25/486 (TSEC: 144) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01088]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 145/150 -128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 -Epoch 146/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 -Epoch 147/150 -128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 -Epoch 148/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 -Epoch 149/150 -128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 -Epoch 150/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2961 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.24 sec -Time taken for epoch(SUBo): 250.77 sec -Time taken for epoch(OTHERo): 69.47 sec -<---------------------------------------|Epoch [25] END|---------------------------------------> - -Epoch: 26/486 (TSEC: 150) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01082]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 151/156 -128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 -Epoch 152/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 -Epoch 153/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 -Epoch 154/156 -128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 -Epoch 155/156 -128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 -Epoch 156/156 -128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2054 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.50 sec -Time taken for epoch(SUBo): 253.89 sec -Time taken for epoch(OTHERo): 68.61 sec -<---------------------------------------|Epoch [26] END|---------------------------------------> - -Epoch: 27/486 (TSEC: 156) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01076]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 157/162 -128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 -Epoch 158/162 -128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 -Epoch 159/162 -128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 -Epoch 160/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 -Epoch 161/162 -128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 -Epoch 162/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2812 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.25 sec -Time taken for epoch(SUBo): 253.57 sec -Time taken for epoch(OTHERo): 69.69 sec -<---------------------------------------|Epoch [27] END|---------------------------------------> - -Epoch: 28/486 (TSEC: 162) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0107]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 163/168 -128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 -Epoch 164/168 -128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 165/168 -128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 -Epoch 166/168 -128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 -Epoch 167/168 -128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 -Epoch 168/168 -128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3527 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.79 sec -Time taken for epoch(SUBo): 252.26 sec -Time taken for epoch(OTHERo): 68.52 sec -<---------------------------------------|Epoch [28] END|---------------------------------------> - -Epoch: 29/486 (TSEC: 168) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01064]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 169/174 -128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 -Epoch 170/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 -Epoch 171/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 -Epoch 172/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 -Epoch 173/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 -Epoch 174/174 -128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3280 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.85 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 70.35 sec -<---------------------------------------|Epoch [29] END|---------------------------------------> - -Epoch: 30/486 (TSEC: 174) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01058]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 175/180 -128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 -Epoch 176/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 -Epoch 177/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 -Epoch 178/180 -128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 -Epoch 179/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 -Epoch 180/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2430 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.08 sec -Time taken for epoch(SUBo): 252.22 sec -Time taken for epoch(OTHERo): 69.87 sec -<---------------------------------------|Epoch [30] END|---------------------------------------> - -Epoch: 31/486 (TSEC: 180) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01052]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 181/186 -128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 -Epoch 182/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 -Epoch 183/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 -Epoch 184/186 -128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 -Epoch 185/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 -Epoch 186/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9022 -Model Test loss: 0.5451 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.75 sec -Time taken for epoch(SUBo): 253.25 sec -Time taken for epoch(OTHERo): 69.50 sec -<---------------------------------------|Epoch [31] END|---------------------------------------> - -Epoch: 32/486 (TSEC: 186) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 -Setting training OneCycleLr::maxlr to [0.01046]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 187/192 -128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 -Epoch 188/192 -128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 -Epoch 189/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 -Epoch 190/192 -128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 -Epoch 191/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 -Epoch 192/192 -128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.2548 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 338.08 sec -Time taken for epoch(SUBo): 252.96 sec -Time taken for epoch(OTHERo): 85.12 sec -<---------------------------------------|Epoch [32] END|---------------------------------------> - -Epoch: 33/486 (TSEC: 192) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0104]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 193/198 -128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 -Epoch 194/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 -Epoch 195/198 -128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 -Epoch 196/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 -Epoch 197/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 -Epoch 198/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... -Model Test acc: 0.9663 -Model Test loss: 0.1816 -Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 328.41 sec -Time taken for epoch(SUBo): 253.11 sec -Time taken for epoch(OTHERo): 75.30 sec -<---------------------------------------|Epoch [33] END|---------------------------------------> - -Epoch: 34/486 (TSEC: 198) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01034]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 199/204 -128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 -Epoch 200/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 -Epoch 201/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 -Epoch 202/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 -Epoch 203/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 -Epoch 204/204 -128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2118 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 327.41 sec -Time taken for epoch(SUBo): 254.14 sec -Time taken for epoch(OTHERo): 73.27 sec -<---------------------------------------|Epoch [34] END|---------------------------------------> - -Epoch: 35/486 (TSEC: 204) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01028]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 205/210 -128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 -Epoch 206/210 -128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 -Epoch 207/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 -Epoch 208/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 -Epoch 209/210 -128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 -Epoch 210/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... -Model Test acc: 0.9551 -Model Test loss: 0.1544 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 329.96 sec -Time taken for epoch(SUBo): 252.88 sec -Time taken for epoch(OTHERo): 77.08 sec -<---------------------------------------|Epoch [35] END|---------------------------------------> - -Epoch: 36/486 (TSEC: 210) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01022]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 211/216 -128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 -Epoch 212/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 -Epoch 213/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 -Epoch 214/216 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 -Epoch 215/216 -128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 -Epoch 216/216 -128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3144 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 328.83 sec -Time taken for epoch(SUBo): 253.49 sec -Time taken for epoch(OTHERo): 75.34 sec -<---------------------------------------|Epoch [36] END|---------------------------------------> - -Epoch: 37/486 (TSEC: 216) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01016]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 217/222 -128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 -Epoch 218/222 -128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 -Epoch 219/222 -128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 -Epoch 220/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 -Epoch 221/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 -Epoch 222/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.4185 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.53 sec -Time taken for epoch(SUBo): 253.85 sec -Time taken for epoch(OTHERo): 73.68 sec -<---------------------------------------|Epoch [37] END|---------------------------------------> - -Epoch: 38/486 (TSEC: 222) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0101]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 223/228 -128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 -Epoch 224/228 -128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 -Epoch 225/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 -Epoch 226/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 -Epoch 227/228 -128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 -Epoch 228/228 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2645 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 252.83 sec -Time taken for epoch(OTHERo): 72.28 sec -<---------------------------------------|Epoch [38] END|---------------------------------------> - -Epoch: 39/486 (TSEC: 228) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01004]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 229/234 -128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 -Epoch 230/234 -128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 -Epoch 231/234 -128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 -Epoch 232/234 -128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 -Epoch 233/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 -Epoch 234/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.3982 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.39 sec -Time taken for epoch(SUBo): 254.95 sec -Time taken for epoch(OTHERo): 71.43 sec -<---------------------------------------|Epoch [39] END|---------------------------------------> - -Epoch: 40/486 (TSEC: 234) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00998]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 235/240 -128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 -Epoch 236/240 -128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 -Epoch 237/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 -Epoch 238/240 -128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 -Epoch 239/240 -128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 -Epoch 240/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2006 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.76 sec -Time taken for epoch(SUBo): 253.96 sec -Time taken for epoch(OTHERo): 71.80 sec -<---------------------------------------|Epoch [40] END|---------------------------------------> - -Epoch: 41/486 (TSEC: 240) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00992]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 241/246 -128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 -Epoch 242/246 -128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 -Epoch 243/246 -128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 -Epoch 244/246 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 -Epoch 245/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 -Epoch 246/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2011 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.07 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.68 sec -<---------------------------------------|Epoch [41] END|---------------------------------------> - -Epoch: 42/486 (TSEC: 246) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00986]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 247/252 -128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 -Epoch 248/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 -Epoch 249/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 -Epoch 250/252 -128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 -Epoch 251/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 -Epoch 252/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2136 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 253.59 sec -Time taken for epoch(OTHERo): 72.54 sec -<---------------------------------------|Epoch [42] END|---------------------------------------> - -Epoch: 43/486 (TSEC: 252) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0098]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 253/258 -128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 -Epoch 254/258 -128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 -Epoch 255/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 -Epoch 256/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 -Epoch 257/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 -Epoch 258/258 -128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2042 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.11 sec -Time taken for epoch(SUBo): 254.27 sec -Time taken for epoch(OTHERo): 72.84 sec -<---------------------------------------|Epoch [43] END|---------------------------------------> - -Epoch: 44/486 (TSEC: 258) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00974]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 259/264 -128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 -Epoch 260/264 -128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 -Epoch 261/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 -Epoch 262/264 -128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 -Epoch 263/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 -Epoch 264/264 -128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.3485 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 252.93 sec -Time taken for epoch(OTHERo): 73.19 sec -<---------------------------------------|Epoch [44] END|---------------------------------------> - -Epoch: 45/486 (TSEC: 264) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00968]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 265/270 -128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 -Epoch 266/270 -128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 -Epoch 267/270 -128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 -Epoch 268/270 -128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 -Epoch 269/270 -128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 -Epoch 270/270 -128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2324 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.17 sec -<---------------------------------------|Epoch [45] END|---------------------------------------> - -Epoch: 46/486 (TSEC: 270) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00962]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 271/276 -128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 -Epoch 272/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 -Epoch 273/276 -128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 -Epoch 274/276 -128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 -Epoch 275/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 -Epoch 276/276 -128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3108 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.76 sec -Time taken for epoch(SUBo): 254.60 sec -Time taken for epoch(OTHERo): 72.16 sec -<---------------------------------------|Epoch [46] END|---------------------------------------> - -Epoch: 47/486 (TSEC: 276) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00956]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 277/282 -128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 -Epoch 278/282 -128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 -Epoch 279/282 -128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 -Epoch 280/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 -Epoch 281/282 -128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 -Epoch 282/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3641 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.51 sec -Time taken for epoch(SUBo): 253.63 sec -Time taken for epoch(OTHERo): 72.88 sec -<---------------------------------------|Epoch [47] END|---------------------------------------> - -Epoch: 48/486 (TSEC: 282) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0095]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 283/288 -128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 -Epoch 284/288 -128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 -Epoch 285/288 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 -Epoch 286/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 -Epoch 287/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 -Epoch 288/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8958 -Model Test loss: 0.7104 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.17 sec -Time taken for epoch(SUBo): 255.62 sec -Time taken for epoch(OTHERo): 74.55 sec -<---------------------------------------|Epoch [48] END|---------------------------------------> - -Epoch: 49/486 (TSEC: 288) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00944]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 289/294 -128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 -Epoch 290/294 -128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 -Epoch 291/294 -128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 -Epoch 292/294 -128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 -Epoch 293/294 -128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 -Epoch 294/294 -128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9183 -Model Test loss: 0.5959 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.11 sec -Time taken for epoch(SUBo): 254.80 sec -Time taken for epoch(OTHERo): 75.32 sec -<---------------------------------------|Epoch [49] END|---------------------------------------> - -Epoch: 50/486 (TSEC: 294) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00938]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 295/300 -128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 -Epoch 296/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 -Epoch 297/300 -128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 -Epoch 298/300 -128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 -Epoch 299/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 -Epoch 300/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4308 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 331.10 sec -Time taken for epoch(SUBo): 255.03 sec -Time taken for epoch(OTHERo): 76.07 sec -<---------------------------------------|Epoch [50] END|---------------------------------------> - -Epoch: 51/486 (TSEC: 300) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00932]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 301/306 -128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 -Epoch 302/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 -Epoch 303/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 -Epoch 304/306 -128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 -Epoch 305/306 -128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 -Epoch 306/306 -128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8974 -Model Test loss: 0.7052 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 362.92 sec -Time taken for epoch(SUBo): 286.09 sec -Time taken for epoch(OTHERo): 76.83 sec -<---------------------------------------|Epoch [51] END|---------------------------------------> - -Epoch: 52/486 (TSEC: 306) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00926]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 307/312 -128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 -Epoch 308/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 -Epoch 309/312 -128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 -Epoch 310/312 -128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 -Epoch 311/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 -Epoch 312/312 -128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9135 -Model Test loss: 0.5680 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.05 sec -Time taken for epoch(SUBo): 284.61 sec -Time taken for epoch(OTHERo): 95.44 sec -<---------------------------------------|Epoch [52] END|---------------------------------------> - -Epoch: 53/486 (TSEC: 312) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0092]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 313/318 -128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 -Epoch 314/318 -128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 -Epoch 315/318 -128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 -Epoch 316/318 -128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 -Epoch 317/318 -128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 -Epoch 318/318 -128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4632 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.68 sec -Time taken for epoch(SUBo): 286.71 sec -Time taken for epoch(OTHERo): 93.97 sec -<---------------------------------------|Epoch [53] END|---------------------------------------> - -Epoch: 54/486 (TSEC: 318) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00914]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 319/324 -128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 -Epoch 320/324 -128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 -Epoch 321/324 -128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 -Epoch 322/324 -128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 -Epoch 323/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 -Epoch 324/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5101 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 389.45 sec -Time taken for epoch(SUBo): 287.64 sec -Time taken for epoch(OTHERo): 101.81 sec -<---------------------------------------|Epoch [54] END|---------------------------------------> - -Epoch: 55/486 (TSEC: 324) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00908]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 325/330 -128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 -Epoch 326/330 -128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 -Epoch 327/330 -128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 -Epoch 328/330 -128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 -Epoch 329/330 -128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 -Epoch 330/330 -128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3231 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.47 sec -Time taken for epoch(SUBo): 281.24 sec -Time taken for epoch(OTHERo): 99.23 sec -<---------------------------------------|Epoch [55] END|---------------------------------------> - -Epoch: 56/486 (TSEC: 330) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00902]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 331/336 -128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 -Epoch 332/336 -128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 -Epoch 333/336 -128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 -Epoch 334/336 -128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 -Epoch 335/336 -128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 -Epoch 336/336 -128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3479 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 392.43 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 103.65 sec -<---------------------------------------|Epoch [56] END|---------------------------------------> - -Epoch: 57/486 (TSEC: 336) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00896]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 337/342 -128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 -Epoch 338/342 -128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 -Epoch 339/342 -128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 -Epoch 340/342 -128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 -Epoch 341/342 -128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 -Epoch 342/342 -128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4083 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 387.98 sec -Time taken for epoch(SUBo): 285.68 sec -Time taken for epoch(OTHERo): 102.30 sec -<---------------------------------------|Epoch [57] END|---------------------------------------> - -Epoch: 58/486 (TSEC: 342) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0089]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 343/348 -128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 -Epoch 344/348 -128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 -Epoch 345/348 -128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 -Epoch 346/348 -128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 -Epoch 347/348 -128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 -Epoch 348/348 -128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3776 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 350.83 sec -Time taken for epoch(SUBo): 268.69 sec -Time taken for epoch(OTHERo): 82.14 sec -<---------------------------------------|Epoch [58] END|---------------------------------------> - -Epoch: 59/486 (TSEC: 348) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00884]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 349/354 -128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 -Epoch 350/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 -Epoch 351/354 -128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 -Epoch 352/354 -128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 -Epoch 353/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 -Epoch 354/354 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4066 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 353.60 sec -Time taken for epoch(SUBo): 258.60 sec -Time taken for epoch(OTHERo): 95.01 sec -<---------------------------------------|Epoch [59] END|---------------------------------------> - -Epoch: 60/486 (TSEC: 354) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00878]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 355/360 -128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 -Epoch 356/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 -Epoch 357/360 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 -Epoch 358/360 -128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 -Epoch 359/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 -Epoch 360/360 -128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4142 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 346.87 sec -Time taken for epoch(SUBo): 257.34 sec -Time taken for epoch(OTHERo): 89.53 sec -<---------------------------------------|Epoch [60] END|---------------------------------------> - -Epoch: 61/486 (TSEC: 360) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00872]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 361/366 -128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 -Epoch 362/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 -Epoch 363/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 -Epoch 364/366 -128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 -Epoch 365/366 -128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 -Epoch 366/366 -128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.7276 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.90 sec -Time taken for epoch(SUBo): 264.37 sec -Time taken for epoch(OTHERo): 77.53 sec -<---------------------------------------|Epoch [61] END|---------------------------------------> - -Epoch: 62/486 (TSEC: 366) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00866]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 367/372 -128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 -Epoch 368/372 -128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 -Epoch 369/372 -128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 -Epoch 370/372 -128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 -Epoch 371/372 -128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 -Epoch 372/372 -128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.2727 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 344.05 sec -Time taken for epoch(SUBo): 266.44 sec -Time taken for epoch(OTHERo): 77.61 sec -<---------------------------------------|Epoch [62] END|---------------------------------------> - -Epoch: 63/486 (TSEC: 372) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0086]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 373/378 -128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 -Epoch 374/378 -128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 -Epoch 375/378 -128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 -Epoch 376/378 -128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 -Epoch 377/378 -128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 -Epoch 378/378 -128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4211 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 334.11 sec -Time taken for epoch(SUBo): 258.37 sec -Time taken for epoch(OTHERo): 75.73 sec -<---------------------------------------|Epoch [63] END|---------------------------------------> - -Epoch: 64/486 (TSEC: 378) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 -Setting training OneCycleLr::maxlr to [0.00854]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 379/384 -128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 -Epoch 380/384 -128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 -Epoch 381/384 -128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 -Epoch 382/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 -Epoch 383/384 -128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 -Epoch 384/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3692 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 356.90 sec -Time taken for epoch(SUBo): 259.87 sec -Time taken for epoch(OTHERo): 97.03 sec -<---------------------------------------|Epoch [64] END|---------------------------------------> - -Epoch: 65/486 (TSEC: 384) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00848]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 385/390 -128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 -Epoch 386/390 -128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 -Epoch 387/390 -128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 -Epoch 388/390 -128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 -Epoch 389/390 -128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 -Epoch 390/390 -128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1823 -Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.22 sec -Time taken for epoch(SUBo): 257.30 sec -Time taken for epoch(OTHERo): 83.93 sec -<---------------------------------------|Epoch [65] END|---------------------------------------> - -Epoch: 66/486 (TSEC: 390) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00842]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 391/396 -128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 -Epoch 392/396 -128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 -Epoch 393/396 -128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 -Epoch 394/396 -128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 -Epoch 395/396 -128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 -Epoch 396/396 -128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9551 -Model Test loss: 0.2462 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 340.59 sec -Time taken for epoch(SUBo): 259.99 sec -Time taken for epoch(OTHERo): 80.59 sec -<---------------------------------------|Epoch [66] END|---------------------------------------> - -Epoch: 67/486 (TSEC: 396) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00836]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 397/402 -128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 -Epoch 398/402 -128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 -Epoch 399/402 -128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 -Epoch 400/402 -128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 -Epoch 401/402 -128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 -Epoch 402/402 -128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2736 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 348.32 sec -Time taken for epoch(SUBo): 267.55 sec -Time taken for epoch(OTHERo): 80.77 sec -<---------------------------------------|Epoch [67] END|---------------------------------------> - -Epoch: 68/486 (TSEC: 402) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0083]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 403/408 -128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 -Epoch 404/408 -128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 -Epoch 405/408 -128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 -Epoch 406/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 -Epoch 407/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 -Epoch 408/408 -128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.2983 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 374.47 sec -Time taken for epoch(SUBo): 276.39 sec -Time taken for epoch(OTHERo): 98.08 sec -<---------------------------------------|Epoch [68] END|---------------------------------------> - -Epoch: 69/486 (TSEC: 408) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00824]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 409/414 -128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 -Epoch 410/414 -128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 -Epoch 411/414 -128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 -Epoch 412/414 -128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 -Epoch 413/414 -128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 -Epoch 414/414 -128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3477 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 343.05 sec -Time taken for epoch(SUBo): 260.05 sec -Time taken for epoch(OTHERo): 83.00 sec -<---------------------------------------|Epoch [69] END|---------------------------------------> - -Epoch: 70/486 (TSEC: 414) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00818]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 415/420 -128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 -Epoch 416/420 -128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 -Epoch 417/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 -Epoch 418/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 -Epoch 419/420 -128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 -Epoch 420/420 -128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9167 -Model Test loss: 0.4541 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 342.13 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 79.85 sec -<---------------------------------------|Epoch [70] END|---------------------------------------> - -Epoch: 71/486 (TSEC: 420) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00812]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 421/426 -128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 -Epoch 422/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 -Epoch 423/426 -128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 -Epoch 424/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 -Epoch 425/426 -128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 -Epoch 426/426 -128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2110 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.68 sec -Time taken for epoch(SUBo): 260.39 sec -Time taken for epoch(OTHERo): 81.29 sec -<---------------------------------------|Epoch [71] END|---------------------------------------> - -Epoch: 72/486 (TSEC: 426) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00806]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 427/432 -128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 -Epoch 428/432 -128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 -Epoch 429/432 -128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 -Epoch 430/432 -128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 -Epoch 431/432 -128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 -Epoch 432/432 -128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... -Model Test acc: 0.9615 -Model Test loss: 0.1528 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 353.28 sec -Time taken for epoch(SUBo): 265.48 sec -Time taken for epoch(OTHERo): 87.80 sec -<---------------------------------------|Epoch [72] END|---------------------------------------> - -Epoch: 73/486 (TSEC: 432) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.008]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 433/438 -128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 -Epoch 434/438 -128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 -Epoch 435/438 -128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 -Epoch 436/438 -128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 -Epoch 437/438 -128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 -Epoch 438/438 -128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1676 -Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.79 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 111.40 sec -<---------------------------------------|Epoch [73] END|---------------------------------------> - -Epoch: 74/486 (TSEC: 438) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00794]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 439/444 -128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 -Epoch 440/444 -128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 -Epoch 441/444 -128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 -Epoch 442/444 -128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 -Epoch 443/444 -128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 -Epoch 444/444 -128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.3061 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.10 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 108.32 sec -<---------------------------------------|Epoch [74] END|---------------------------------------> - -Epoch: 75/486 (TSEC: 444) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00788]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 445/450 -128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 -Epoch 446/450 -128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 -Epoch 447/450 -128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 -Epoch 448/450 -128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 -Epoch 449/450 -128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 -Epoch 450/450 -128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.2149 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.95 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 108.55 sec -<---------------------------------------|Epoch [75] END|---------------------------------------> - -Epoch: 76/486 (TSEC: 450) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00782]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 451/456 -128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 -Epoch 452/456 -128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 -Epoch 453/456 -128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 -Epoch 454/456 -128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 -Epoch 455/456 -128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 -Epoch 456/456 -128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1664 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.35 sec -Time taken for epoch(SUBo): 290.41 sec -Time taken for epoch(OTHERo): 109.94 sec -<---------------------------------------|Epoch [76] END|---------------------------------------> - -Epoch: 77/486 (TSEC: 456) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00776]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 457/462 -128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 -Epoch 458/462 -128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 -Epoch 459/462 -128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 -Epoch 460/462 -128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 -Epoch 461/462 -128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 -Epoch 462/462 -128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1549 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 394.70 sec -Time taken for epoch(SUBo): 289.87 sec -Time taken for epoch(OTHERo): 104.83 sec -<---------------------------------------|Epoch [77] END|---------------------------------------> - -Epoch: 78/486 (TSEC: 462) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0077]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 463/468 -128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 -Epoch 464/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 -Epoch 465/468 -128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 -Epoch 466/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 -Epoch 467/468 -128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 -Epoch 468/468 -128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1365 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 398.75 sec -Time taken for epoch(SUBo): 289.42 sec -Time taken for epoch(OTHERo): 109.33 sec -<---------------------------------------|Epoch [78] END|---------------------------------------> - -Epoch: 79/486 (TSEC: 468) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00764]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 469/474 -128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 -Epoch 470/474 -128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 -Epoch 471/474 -128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 -Epoch 472/474 -128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 -Epoch 473/474 -128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 -Epoch 474/474 -128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2348 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 395.44 sec -Time taken for epoch(SUBo): 291.06 sec -Time taken for epoch(OTHERo): 104.39 sec -<---------------------------------------|Epoch [79] END|---------------------------------------> - -Epoch: 80/486 (TSEC: 474) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00758]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 475/480 -128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 -Epoch 476/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 -Epoch 477/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 -Epoch 478/480 -128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 -Epoch 479/480 -128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 -Epoch 480/480 -128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2441 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 399.87 sec -Time taken for epoch(SUBo): 293.34 sec -Time taken for epoch(OTHERo): 106.54 sec -<---------------------------------------|Epoch [80] END|---------------------------------------> - -Epoch: 81/486 (TSEC: 480) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00752]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 481/486 -128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 -Epoch 482/486 -128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 -Epoch 483/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 -Epoch 484/486 -128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 -Epoch 485/486 -128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 -Epoch 486/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3567 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.57 sec -Time taken for epoch(SUBo): 258.36 sec -Time taken for epoch(OTHERo): 102.21 sec -<---------------------------------------|Epoch [81] END|---------------------------------------> - -Epoch: 82/486 (TSEC: 486) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00746]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 487/492 -128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 -Epoch 488/492 -128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 -Epoch 489/492 -128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 -Epoch 490/492 -128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 -Epoch 491/492 -128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 -Epoch 492/492 -128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2877 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 338.30 sec -Time taken for epoch(SUBo): 256.81 sec -Time taken for epoch(OTHERo): 81.49 sec -<---------------------------------------|Epoch [82] END|---------------------------------------> - -Epoch: 83/486 (TSEC: 492) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0074]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 493/498 -128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 -Epoch 494/498 -128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 -Epoch 495/498 -128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 -Epoch 496/498 -128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 -Epoch 497/498 -128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 -Epoch 498/498 -128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3008 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 341.92 sec -Time taken for epoch(SUBo): 260.54 sec -Time taken for epoch(OTHERo): 81.38 sec -<---------------------------------------|Epoch [83] END|---------------------------------------> - -Epoch: 84/486 (TSEC: 498) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00734]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 499/504 -128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 -Epoch 500/504 -128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 -Epoch 501/504 -128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 -Epoch 502/504 -128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 -Epoch 503/504 -128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 -Epoch 504/504 -128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3369 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 401.59 sec -Time taken for epoch(SUBo): 296.36 sec -Time taken for epoch(OTHERo): 105.23 sec -<---------------------------------------|Epoch [84] END|---------------------------------------> - -Epoch: 85/486 (TSEC: 504) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00728]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 505/510 -128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 -Epoch 506/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 -Epoch 507/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 -Epoch 508/510 -128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 -Epoch 509/510 -128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 -Epoch 510/510 -128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2355 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 378.93 sec -Time taken for epoch(SUBo): 271.88 sec -Time taken for epoch(OTHERo): 107.05 sec -<---------------------------------------|Epoch [85] END|---------------------------------------> - -Epoch: 86/486 (TSEC: 510) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00722]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 511/516 -128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 -Epoch 512/516 -128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 -Epoch 513/516 -128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 -Epoch 514/516 -128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 -Epoch 515/516 -128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 -Epoch 516/516 -128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2163 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 348.35 sec -Time taken for epoch(SUBo): 264.53 sec -Time taken for epoch(OTHERo): 83.82 sec -<---------------------------------------|Epoch [86] END|---------------------------------------> - -Epoch: 87/486 (TSEC: 516) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00716]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 517/522 -128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 -Epoch 518/522 -128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 -Epoch 519/522 -128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 -Epoch 520/522 -128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 -Epoch 521/522 -128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 -Epoch 522/522 -128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.32 sec -Time taken for epoch(SUBo): 265.09 sec -Time taken for epoch(OTHERo): 84.23 sec -<---------------------------------------|Epoch [87] END|---------------------------------------> - -Epoch: 88/486 (TSEC: 522) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0071]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 523/528 -128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 -Epoch 524/528 -128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 -Epoch 525/528 -128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 -Epoch 526/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 -Epoch 527/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 -Epoch 528/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3755 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.31 sec -Time taken for epoch(SUBo): 260.67 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [88] END|---------------------------------------> - -Epoch: 89/486 (TSEC: 528) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00704]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 529/534 -128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 -Epoch 530/534 -128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 -Epoch 531/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 -Epoch 532/534 -128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 -Epoch 533/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 -Epoch 534/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1451 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 344.88 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 83.03 sec -<---------------------------------------|Epoch [89] END|---------------------------------------> - -Epoch: 90/486 (TSEC: 534) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00698]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 535/540 -128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 -Epoch 536/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 -Epoch 537/540 -128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 -Epoch 538/540 -128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 -Epoch 539/540 -128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 -Epoch 540/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2261 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 376.02 sec -Time taken for epoch(SUBo): 290.31 sec -Time taken for epoch(OTHERo): 85.71 sec -<---------------------------------------|Epoch [90] END|---------------------------------------> - -Epoch: 91/486 (TSEC: 540) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00692]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 541/546 -128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 -Epoch 542/546 -128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 -Epoch 543/546 -128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 -Epoch 544/546 -128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 -Epoch 545/546 -128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 -Epoch 546/546 -128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2810 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 409.06 sec -Time taken for epoch(SUBo): 293.27 sec -Time taken for epoch(OTHERo): 115.79 sec -<---------------------------------------|Epoch [91] END|---------------------------------------> - -Epoch: 92/486 (TSEC: 546) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00686]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 547/552 -128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 -Epoch 548/552 -128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 -Epoch 549/552 -128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 -Epoch 550/552 -128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 -Epoch 551/552 -128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 -Epoch 552/552 -128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2734 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 402.95 sec -Time taken for epoch(SUBo): 289.90 sec -Time taken for epoch(OTHERo): 113.04 sec -<---------------------------------------|Epoch [92] END|---------------------------------------> - -Epoch: 93/486 (TSEC: 552) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0068]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 553/558 -128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 -Epoch 554/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 -Epoch 555/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 -Epoch 556/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 -Epoch 557/558 -128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 -Epoch 558/558 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4314 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 343.82 sec -Time taken for epoch(SUBo): 258.19 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [93] END|---------------------------------------> - -Epoch: 94/486 (TSEC: 558) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00674]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 559/564 -128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 -Epoch 560/564 -128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 -Epoch 561/564 -128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 -Epoch 562/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 -Epoch 563/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 -Epoch 564/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2974 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.85 sec -Time taken for epoch(SUBo): 261.88 sec -Time taken for epoch(OTHERo): 85.97 sec -<---------------------------------------|Epoch [94] END|---------------------------------------> - -Epoch: 95/486 (TSEC: 564) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00668]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 565/570 -128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 -Epoch 566/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 -Epoch 567/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 -Epoch 568/570 -128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 -Epoch 569/570 -128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 -Epoch 570/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.4469 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.57 sec -Time taken for epoch(SUBo): 260.42 sec -Time taken for epoch(OTHERo): 89.15 sec -<---------------------------------------|Epoch [95] END|---------------------------------------> - -Epoch: 96/486 (TSEC: 570) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 -Setting training OneCycleLr::maxlr to [0.00662]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 571/576 -128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 -Epoch 572/576 -128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 -Epoch 573/576 -128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 -Epoch 574/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 -Epoch 575/576 -128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 -Epoch 576/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3027 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.90 sec -Time taken for epoch(SUBo): 261.28 sec -Time taken for epoch(OTHERo): 99.62 sec -<---------------------------------------|Epoch [96] END|---------------------------------------> - -Epoch: 97/486 (TSEC: 576) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00656]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 577/582 -128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 -Epoch 578/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 -Epoch 579/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 -Epoch 580/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 -Epoch 581/582 -128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 -Epoch 582/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3618 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.27 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 84.42 sec -<---------------------------------------|Epoch [97] END|---------------------------------------> - -Epoch: 98/486 (TSEC: 582) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0065]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 583/588 -128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 -Epoch 584/588 -128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 -Epoch 585/588 -128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 -Epoch 586/588 -128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 -Epoch 587/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 -Epoch 588/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3557 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.51 sec -Time taken for epoch(SUBo): 262.33 sec -Time taken for epoch(OTHERo): 83.18 sec -<---------------------------------------|Epoch [98] END|---------------------------------------> - -Epoch: 99/486 (TSEC: 588) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00644]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 589/594 -128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 -Epoch 590/594 -128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 -Epoch 591/594 -128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 -Epoch 592/594 -128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 -Epoch 593/594 -128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 -Epoch 594/594 -128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3592 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.37 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 85.09 sec -<---------------------------------------|Epoch [99] END|---------------------------------------> - -Epoch: 100/486 (TSEC: 594) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00638]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 595/600 -128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 -Epoch 596/600 -128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 -Epoch 597/600 -128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 -Epoch 598/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 -Epoch 599/600 -128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 -Epoch 600/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.3042 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.54 sec -Time taken for epoch(SUBo): 263.28 sec -Time taken for epoch(OTHERo): 82.26 sec -<---------------------------------------|Epoch [100] END|---------------------------------------> - -Epoch: 101/486 (TSEC: 600) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00632]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 601/606 -128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 -Epoch 602/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 -Epoch 603/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 -Epoch 604/606 -128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 605/606 -128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 -Epoch 606/606 -128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1757 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.57 sec -Time taken for epoch(SUBo): 261.73 sec -Time taken for epoch(OTHERo): 85.84 sec -<---------------------------------------|Epoch [101] END|---------------------------------------> - -Epoch: 102/486 (TSEC: 606) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00626]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 607/612 -128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 -Epoch 608/612 -128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 -Epoch 609/612 -128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 -Epoch 610/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 -Epoch 611/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 -Epoch 612/612 -128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.4659 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 350.48 sec -Time taken for epoch(SUBo): 263.62 sec -Time taken for epoch(OTHERo): 86.85 sec -<---------------------------------------|Epoch [102] END|---------------------------------------> - -Epoch: 103/486 (TSEC: 612) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0062]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 613/618 -128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 -Epoch 614/618 -128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 -Epoch 615/618 -128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 -Epoch 616/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 -Epoch 617/618 -128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 -Epoch 618/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3278 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 356.91 sec -Time taken for epoch(SUBo): 264.67 sec -Time taken for epoch(OTHERo): 92.23 sec -<---------------------------------------|Epoch [103] END|---------------------------------------> - -Epoch: 104/486 (TSEC: 618) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00614]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 619/624 -128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 -Epoch 620/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 -Epoch 621/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 -Epoch 622/624 -128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 -Epoch 623/624 -128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 -Epoch 624/624 -128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.4053 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.90 sec -Time taken for epoch(SUBo): 260.79 sec -Time taken for epoch(OTHERo): 86.11 sec -<---------------------------------------|Epoch [104] END|---------------------------------------> - -Epoch: 105/486 (TSEC: 624) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00608]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 625/630 -128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 -Epoch 626/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 -Epoch 627/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 -Epoch 628/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 -Epoch 629/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 -Epoch 630/630 -128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2148 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 353.04 sec -Time taken for epoch(SUBo): 264.40 sec -Time taken for epoch(OTHERo): 88.64 sec -<---------------------------------------|Epoch [105] END|---------------------------------------> - -Epoch: 106/486 (TSEC: 630) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00602]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 631/636 -128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 -Epoch 632/636 -128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 -Epoch 633/636 -128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 -Epoch 634/636 -128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 -Epoch 635/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 -Epoch 636/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1188 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 356.65 sec -Time taken for epoch(SUBo): 263.16 sec -Time taken for epoch(OTHERo): 93.49 sec -<---------------------------------------|Epoch [106] END|---------------------------------------> - -Epoch: 107/486 (TSEC: 636) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00596]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 637/642 -128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 -Epoch 638/642 -128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 -Epoch 639/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 -Epoch 640/642 -128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 -Epoch 641/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 -Epoch 642/642 -128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2388 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 353.97 sec -Time taken for epoch(SUBo): 260.86 sec -Time taken for epoch(OTHERo): 93.11 sec -<---------------------------------------|Epoch [107] END|---------------------------------------> - -Epoch: 108/486 (TSEC: 642) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0059]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 643/648 -128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 -Epoch 644/648 -128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 -Epoch 645/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 -Epoch 646/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 -Epoch 647/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 -Epoch 648/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2612 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 351.69 sec -Time taken for epoch(SUBo): 260.60 sec -Time taken for epoch(OTHERo): 91.10 sec -<---------------------------------------|Epoch [108] END|---------------------------------------> - -Epoch: 109/486 (TSEC: 648) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00584]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 649/654 -128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 -Epoch 650/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 -Epoch 651/654 -128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 -Epoch 652/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 -Epoch 653/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 -Epoch 654/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3569 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 346.32 sec -Time taken for epoch(SUBo): 259.69 sec -Time taken for epoch(OTHERo): 86.63 sec -<---------------------------------------|Epoch [109] END|---------------------------------------> - -Epoch: 110/486 (TSEC: 654) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00578]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 655/660 -128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 -Epoch 656/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 -Epoch 657/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 -Epoch 658/660 -128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 -Epoch 659/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 -Epoch 660/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3959 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 347.66 sec -Time taken for epoch(SUBo): 261.10 sec -Time taken for epoch(OTHERo): 86.56 sec -<---------------------------------------|Epoch [110] END|---------------------------------------> - -Epoch: 111/486 (TSEC: 660) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00572]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 661/666 -128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 -Epoch 662/666 -128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 -Epoch 663/666 -128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 -Epoch 664/666 -128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 -Epoch 665/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 -Epoch 666/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2614 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 374.77 sec -Time taken for epoch(SUBo): 287.07 sec -Time taken for epoch(OTHERo): 87.70 sec -<---------------------------------------|Epoch [111] END|---------------------------------------> - -Epoch: 112/486 (TSEC: 666) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00566]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 667/672 -128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 -Epoch 668/672 -128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 -Epoch 669/672 -128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 -Epoch 670/672 -128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 -Epoch 671/672 -128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 -Epoch 672/672 -128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2773 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.29 sec -Time taken for epoch(SUBo): 294.69 sec -Time taken for epoch(OTHERo): 108.60 sec -<---------------------------------------|Epoch [112] END|---------------------------------------> - -Epoch: 113/486 (TSEC: 672) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0056]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 673/678 -128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 -Epoch 674/678 -128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 -Epoch 675/678 -128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 676/678 -128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 -Epoch 677/678 -128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 -Epoch 678/678 -128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3138 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 405.22 sec -Time taken for epoch(SUBo): 290.78 sec -Time taken for epoch(OTHERo): 114.43 sec -<---------------------------------------|Epoch [113] END|---------------------------------------> - -Epoch: 114/486 (TSEC: 678) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00554]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 679/684 -128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 -Epoch 680/684 -128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 -Epoch 681/684 -128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 -Epoch 682/684 -128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 -Epoch 683/684 -128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 -Epoch 684/684 -128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.12 sec -Time taken for epoch(SUBo): 294.80 sec -Time taken for epoch(OTHERo): 117.31 sec -<---------------------------------------|Epoch [114] END|---------------------------------------> - -Epoch: 115/486 (TSEC: 684) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00548]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 685/690 -128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 -Epoch 686/690 -128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 -Epoch 687/690 -128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 -Epoch 688/690 -128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 -Epoch 689/690 -128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 -Epoch 690/690 -128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4240 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.16 sec -Time taken for epoch(SUBo): 291.71 sec -Time taken for epoch(OTHERo): 110.46 sec -<---------------------------------------|Epoch [115] END|---------------------------------------> - -Epoch: 116/486 (TSEC: 690) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00542]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 691/696 -128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 -Epoch 692/696 -128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 -Epoch 693/696 -128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 -Epoch 694/696 -128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 -Epoch 695/696 -128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 -Epoch 696/696 -128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3796 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.58 sec -Time taken for epoch(SUBo): 295.23 sec -Time taken for epoch(OTHERo): 113.35 sec -<---------------------------------------|Epoch [116] END|---------------------------------------> - -Epoch: 117/486 (TSEC: 696) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00536]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 697/702 -128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 -Epoch 698/702 -128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 -Epoch 699/702 -128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 -Epoch 700/702 -128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 -Epoch 701/702 -128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 -Epoch 702/702 -128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3449 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.03 sec -Time taken for epoch(SUBo): 295.99 sec -Time taken for epoch(OTHERo): 115.05 sec -<---------------------------------------|Epoch [117] END|---------------------------------------> - -Epoch: 118/486 (TSEC: 702) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0053]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 703/708 -128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 -Epoch 704/708 -128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 -Epoch 705/708 -128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 -Epoch 706/708 -128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 -Epoch 707/708 -128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 -Epoch 708/708 -128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2279 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.63 sec -Time taken for epoch(SUBo): 296.34 sec -Time taken for epoch(OTHERo): 117.29 sec -<---------------------------------------|Epoch [118] END|---------------------------------------> - -Epoch: 119/486 (TSEC: 708) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00524]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 709/714 -128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 -Epoch 710/714 -128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 -Epoch 711/714 -128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 -Epoch 712/714 -128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 -Epoch 713/714 -128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 -Epoch 714/714 -128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3194 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.60 sec -Time taken for epoch(SUBo): 294.03 sec -Time taken for epoch(OTHERo): 114.57 sec -<---------------------------------------|Epoch [119] END|---------------------------------------> - -Epoch: 120/486 (TSEC: 714) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00518]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 715/720 -128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 -Epoch 716/720 -128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 -Epoch 717/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 -Epoch 718/720 -128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 -Epoch 719/720 -128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 -Epoch 720/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.5143 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.26 sec -Time taken for epoch(SUBo): 295.66 sec -Time taken for epoch(OTHERo): 112.60 sec -<---------------------------------------|Epoch [120] END|---------------------------------------> - -Epoch: 121/486 (TSEC: 720) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00512]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 721/726 -128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 -Epoch 722/726 -128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 -Epoch 723/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 -Epoch 724/726 -128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 -Epoch 725/726 -128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 -Epoch 726/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4177 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.06 sec -Time taken for epoch(SUBo): 295.42 sec -Time taken for epoch(OTHERo): 118.64 sec -<---------------------------------------|Epoch [121] END|---------------------------------------> - -Epoch: 122/486 (TSEC: 726) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00506]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 727/732 -128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 -Epoch 728/732 -128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 -Epoch 729/732 -128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 -Epoch 730/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 -Epoch 731/732 -128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 -Epoch 732/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4436 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.37 sec -Time taken for epoch(SUBo): 293.85 sec -Time taken for epoch(OTHERo): 117.52 sec -<---------------------------------------|Epoch [122] END|---------------------------------------> - -Epoch: 123/486 (TSEC: 732) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.005]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 733/738 -128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 -Epoch 734/738 -128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 -Epoch 735/738 -128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 -Epoch 736/738 -128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 -Epoch 737/738 -128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 -Epoch 738/738 -128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4303 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.96 sec -Time taken for epoch(SUBo): 294.39 sec -Time taken for epoch(OTHERo): 118.57 sec -<---------------------------------------|Epoch [123] END|---------------------------------------> - -Epoch: 124/486 (TSEC: 738) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00494]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 739/744 -128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 -Epoch 740/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 -Epoch 741/744 -128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 -Epoch 742/744 -128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 -Epoch 743/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 -Epoch 744/744 -128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4234 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.23 sec -Time taken for epoch(SUBo): 298.41 sec -Time taken for epoch(OTHERo): 114.83 sec -<---------------------------------------|Epoch [124] END|---------------------------------------> - -Epoch: 125/486 (TSEC: 744) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00488]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 745/750 -128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 -Epoch 746/750 -128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 -Epoch 747/750 -128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 -Epoch 748/750 -128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 -Epoch 749/750 -128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 -Epoch 750/750 -128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3677 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.29 sec -Time taken for epoch(SUBo): 293.29 sec -Time taken for epoch(OTHERo): 119.99 sec -<---------------------------------------|Epoch [125] END|---------------------------------------> - -Epoch: 126/486 (TSEC: 750) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00482]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 751/756 -128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 -Epoch 752/756 -128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 -Epoch 753/756 -128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 -Epoch 754/756 -128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 -Epoch 755/756 -128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 -Epoch 756/756 -128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3409 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.45 sec -Time taken for epoch(SUBo): 293.26 sec -Time taken for epoch(OTHERo): 110.19 sec -<---------------------------------------|Epoch [126] END|---------------------------------------> - -Epoch: 127/486 (TSEC: 756) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00476]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 757/762 -128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 -Epoch 758/762 -128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 -Epoch 759/762 -128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 -Epoch 760/762 -128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 -Epoch 761/762 -128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 -Epoch 762/762 -128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3632 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.89 sec -Time taken for epoch(SUBo): 291.93 sec -Time taken for epoch(OTHERo): 111.96 sec -<---------------------------------------|Epoch [127] END|---------------------------------------> - -Epoch: 128/486 (TSEC: 762) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 -Setting training OneCycleLr::maxlr to [0.0047]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 763/768 -128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 -Epoch 764/768 -128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 -Epoch 765/768 -128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 -Epoch 766/768 -128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 -Epoch 767/768 -128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 -Epoch 768/768 -128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3065 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 425.95 sec -Time taken for epoch(SUBo): 291.59 sec -Time taken for epoch(OTHERo): 134.36 sec -<---------------------------------------|Epoch [128] END|---------------------------------------> - -Epoch: 129/486 (TSEC: 768) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00464]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 769/774 -128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 -Epoch 770/774 -128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 -Epoch 771/774 -128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 -Epoch 772/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 -Epoch 773/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 -Epoch 774/774 -128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2221 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.23 sec -Time taken for epoch(SUBo): 293.03 sec -Time taken for epoch(OTHERo): 109.20 sec -<---------------------------------------|Epoch [129] END|---------------------------------------> - -Epoch: 130/486 (TSEC: 774) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00458]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 775/780 -128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 -Epoch 776/780 -128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 -Epoch 777/780 -128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 -Epoch 778/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 -Epoch 779/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 -Epoch 780/780 -128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3315 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.71 sec -Time taken for epoch(SUBo): 293.90 sec -Time taken for epoch(OTHERo): 108.81 sec -<---------------------------------------|Epoch [130] END|---------------------------------------> - -Epoch: 131/486 (TSEC: 780) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00452]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 781/786 -128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 -Epoch 782/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 -Epoch 783/786 -128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 -Epoch 784/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 -Epoch 785/786 -128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 -Epoch 786/786 -128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.5019 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 424.42 sec -Time taken for epoch(SUBo): 300.59 sec -Time taken for epoch(OTHERo): 123.83 sec -<---------------------------------------|Epoch [131] END|---------------------------------------> - -Epoch: 132/486 (TSEC: 786) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00446]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 787/792 -128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 -Epoch 788/792 -128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 -Epoch 789/792 -128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 -Epoch 790/792 -128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 -Epoch 791/792 -128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 -Epoch 792/792 -128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9215 -Model Test loss: 0.5337 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.90 sec -Time taken for epoch(SUBo): 299.61 sec -Time taken for epoch(OTHERo): 121.28 sec -<---------------------------------------|Epoch [132] END|---------------------------------------> - -Epoch: 133/486 (TSEC: 792) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0044]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 793/798 -128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 -Epoch 794/798 -128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 -Epoch 795/798 -128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 -Epoch 796/798 -128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 -Epoch 797/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 -Epoch 798/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.5005 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.52 sec -Time taken for epoch(SUBo): 296.92 sec -Time taken for epoch(OTHERo): 120.59 sec -<---------------------------------------|Epoch [133] END|---------------------------------------> - -Epoch: 134/486 (TSEC: 798) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00434]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 799/804 -128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 -Epoch 800/804 -128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 -Epoch 801/804 -128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 -Epoch 802/804 -128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 -Epoch 803/804 -128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 -Epoch 804/804 -128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9151 -Model Test loss: 0.5460 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.80 sec -Time taken for epoch(SUBo): 297.98 sec -Time taken for epoch(OTHERo): 122.82 sec -<---------------------------------------|Epoch [134] END|---------------------------------------> - -Epoch: 135/486 (TSEC: 804) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00428]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 805/810 -128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 -Epoch 806/810 -128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 -Epoch 807/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 -Epoch 808/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 -Epoch 809/810 -128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 -Epoch 810/810 -128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9103 -Model Test loss: 0.5751 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.70 sec -Time taken for epoch(SUBo): 297.58 sec -Time taken for epoch(OTHERo): 117.13 sec -<---------------------------------------|Epoch [135] END|---------------------------------------> - -Epoch: 136/486 (TSEC: 810) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00422]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 811/816 -128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 -Epoch 812/816 -128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 -Epoch 813/816 -128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 -Epoch 814/816 -128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 -Epoch 815/816 -128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 -Epoch 816/816 -128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3819 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.58 sec -Time taken for epoch(SUBo): 300.30 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [136] END|---------------------------------------> - -Epoch: 137/486 (TSEC: 816) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00416]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 817/822 -128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 -Epoch 818/822 -128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 -Epoch 819/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 -Epoch 820/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 -Epoch 821/822 -128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 -Epoch 822/822 -128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4434 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.54 sec -Time taken for epoch(SUBo): 297.62 sec -Time taken for epoch(OTHERo): 118.92 sec -<---------------------------------------|Epoch [137] END|---------------------------------------> - -Epoch: 138/486 (TSEC: 822) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0041]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 823/828 -128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 -Epoch 824/828 -128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 -Epoch 825/828 -128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 -Epoch 826/828 -128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 -Epoch 827/828 -128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 -Epoch 828/828 -128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4551 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.46 sec -Time taken for epoch(SUBo): 297.55 sec -Time taken for epoch(OTHERo): 117.91 sec -<---------------------------------------|Epoch [138] END|---------------------------------------> - -Epoch: 139/486 (TSEC: 828) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00404]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 829/834 -128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 -Epoch 830/834 -128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 -Epoch 831/834 -128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 -Epoch 832/834 -128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 -Epoch 833/834 -128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 -Epoch 834/834 -128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.5237 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 418.66 sec -Time taken for epoch(SUBo): 295.90 sec -Time taken for epoch(OTHERo): 122.76 sec -<---------------------------------------|Epoch [139] END|---------------------------------------> - -Epoch: 140/486 (TSEC: 834) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00398]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 835/840 -128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 -Epoch 836/840 -128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 -Epoch 837/840 -128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 -Epoch 838/840 -128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 -Epoch 839/840 -128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 -Epoch 840/840 -128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5710 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.43 sec -Time taken for epoch(SUBo): 298.12 sec -Time taken for epoch(OTHERo): 122.31 sec -<---------------------------------------|Epoch [140] END|---------------------------------------> - -Epoch: 141/486 (TSEC: 840) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00392]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 841/846 -128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 -Epoch 842/846 -128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 -Epoch 843/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 -Epoch 844/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 -Epoch 845/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 -Epoch 846/846 -128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.5128 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 423.15 sec -Time taken for epoch(SUBo): 298.17 sec -Time taken for epoch(OTHERo): 124.98 sec -<---------------------------------------|Epoch [141] END|---------------------------------------> - -Epoch: 142/486 (TSEC: 846) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00386]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 847/852 -128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 -Epoch 848/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 -Epoch 849/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 -Epoch 850/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 -Epoch 851/852 -128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 -Epoch 852/852 -128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5103 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.49 sec -Time taken for epoch(SUBo): 301.87 sec -Time taken for epoch(OTHERo): 114.61 sec -<---------------------------------------|Epoch [142] END|---------------------------------------> - -Epoch: 143/486 (TSEC: 852) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0038]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 853/858 -128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 -Epoch 854/858 -128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 -Epoch 855/858 -128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 -Epoch 856/858 -128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 -Epoch 857/858 -128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 -Epoch 858/858 -128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.7814 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.74 sec -Time taken for epoch(SUBo): 295.81 sec -Time taken for epoch(OTHERo): 113.94 sec -<---------------------------------------|Epoch [143] END|---------------------------------------> - -Epoch: 144/486 (TSEC: 858) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00374]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 859/864 -128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 -Epoch 860/864 -128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 -Epoch 861/864 -128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 -Epoch 862/864 -128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 -Epoch 863/864 -128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 -Epoch 864/864 -128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3838 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.79 sec -Time taken for epoch(SUBo): 295.17 sec -Time taken for epoch(OTHERo): 118.61 sec -<---------------------------------------|Epoch [144] END|---------------------------------------> - -Epoch: 145/486 (TSEC: 864) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00368]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 865/870 -128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 -Epoch 866/870 -128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 -Epoch 867/870 -128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 -Epoch 868/870 -128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 -Epoch 869/870 -128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 -Epoch 870/870 -128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3789 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.68 sec -Time taken for epoch(SUBo): 295.62 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [145] END|---------------------------------------> - -Epoch: 146/486 (TSEC: 870) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00362]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 871/876 -128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 -Epoch 872/876 -128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 -Epoch 873/876 -128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 -Epoch 874/876 -128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 -Epoch 875/876 -128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 -Epoch 876/876 -128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.5834 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.30 sec -Time taken for epoch(SUBo): 298.45 sec -Time taken for epoch(OTHERo): 116.86 sec -<---------------------------------------|Epoch [146] END|---------------------------------------> - -Epoch: 147/486 (TSEC: 876) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00356]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 877/882 -128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 -Epoch 878/882 -128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 -Epoch 879/882 -128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 -Epoch 880/882 -128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 -Epoch 881/882 -128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 -Epoch 882/882 -128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3896 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.42 sec -Time taken for epoch(SUBo): 297.44 sec -Time taken for epoch(OTHERo): 119.98 sec -<---------------------------------------|Epoch [147] END|---------------------------------------> - -Epoch: 148/486 (TSEC: 882) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0035]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 883/888 -128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 -Epoch 884/888 -128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 -Epoch 885/888 -128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 -Epoch 886/888 -128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 -Epoch 887/888 -128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 -Epoch 888/888 -128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5339 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.09 sec -Time taken for epoch(SUBo): 293.02 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [148] END|---------------------------------------> - -Epoch: 149/486 (TSEC: 888) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00344]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 889/894 -128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 -Epoch 890/894 -128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 -Epoch 891/894 -128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 -Epoch 892/894 -128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 -Epoch 893/894 -128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 -Epoch 894/894 -128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.4329 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.69 sec -Time taken for epoch(SUBo): 298.62 sec -Time taken for epoch(OTHERo): 117.07 sec -<---------------------------------------|Epoch [149] END|---------------------------------------> - -Epoch: 150/486 (TSEC: 894) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00338]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 895/900 -128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 -Epoch 896/900 -128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 -Epoch 897/900 -128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 -Epoch 898/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 -Epoch 899/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 -Epoch 900/900 -128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.6034 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.43 sec -Time taken for epoch(SUBo): 292.15 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [150] END|---------------------------------------> - -Epoch: 151/486 (TSEC: 900) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00332]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 901/906 -128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 -Epoch 902/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 -Epoch 903/906 -128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 -Epoch 904/906 -128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 -Epoch 905/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 -Epoch 906/906 -128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3160 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.64 sec -Time taken for epoch(SUBo): 296.21 sec -Time taken for epoch(OTHERo): 120.43 sec -<---------------------------------------|Epoch [151] END|---------------------------------------> - -Epoch: 152/486 (TSEC: 906) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00326]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 907/912 -128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 -Epoch 908/912 -128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 -Epoch 909/912 -128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 -Epoch 910/912 -128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 -Epoch 911/912 - 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 -KeyboardInterrupt. -Training done. - +Training the model... + +Setup Verbose: +Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... +Use_extended_tensorboard [False]. +Debug_OUTPUT_DPS [True]. +OneCycleLr_UFTS [False]. +Setup Verbose END. + +Epoch: 1/486 (TSEC: 0) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Fitting ImageDataGenerator... +- ImageDataGenerator fit done. +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 +Epoch 2/6 +128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 +Epoch 3/6 +128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 +Epoch 4/6 +128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 +Epoch 5/6 +128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 +Epoch 6/6 +128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... +Model Test acc: 0.8974 +Model Test loss: 2.4019 +Improved model accuracy from 0 to 0.8974359035491943. Saving model. +Saving full model H5 format... +Improved model loss from inf to 2.4019267559051514. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 676.74 sec +Time taken for epoch(SUBo): 271.12 sec +Time taken for epoch(OTHERo): 405.62 sec +<---------------------------------------|Epoch [1] END|---------------------------------------> + +Epoch: 2/486 (TSEC: 6) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 7/12 +128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 +Epoch 8/12 +128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 +Epoch 9/12 +128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 +Epoch 10/12 +128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 +Epoch 11/12 +128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 +Epoch 12/12 +128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... +Model Test acc: 0.9407 +Model Test loss: 0.5406 +Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. +Saving full model H5 format... +Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.91 sec +Time taken for epoch(SUBo): 257.59 sec +Time taken for epoch(OTHERo): 68.33 sec +<---------------------------------------|Epoch [2] END|---------------------------------------> + +Epoch: 3/486 (TSEC: 12) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 13/18 +128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 +Epoch 14/18 +128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 +Epoch 15/18 +128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 +Epoch 16/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 +Epoch 17/18 +128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 +Epoch 18/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... +Model Test acc: 0.9423 +Model Test loss: 0.4656 +Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. +Saving full model H5 format... +Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 324.58 sec +Time taken for epoch(SUBo): 255.82 sec +Time taken for epoch(OTHERo): 68.76 sec +<---------------------------------------|Epoch [3] END|---------------------------------------> + +Epoch: 4/486 (TSEC: 18) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 19/24 +128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 +Epoch 20/24 +128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 +Epoch 21/24 +128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 +Epoch 22/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 +Epoch 23/24 +128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 +Epoch 24/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... +Model Test acc: 0.9343 +Model Test loss: 0.4089 +Model accuracy did not improve from 0.942307710647583. Not saving model. +Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 323.62 sec +Time taken for epoch(SUBo): 256.60 sec +Time taken for epoch(OTHERo): 67.02 sec +<---------------------------------------|Epoch [4] END|---------------------------------------> + +Epoch: 5/486 (TSEC: 24) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 25/30 +128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 +Epoch 26/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 +Epoch 27/30 +128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 +Epoch 28/30 +128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 +Epoch 29/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 +Epoch 30/30 +128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.2338 +Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. +Saving full model H5 format... +Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.89 sec +Time taken for epoch(SUBo): 258.52 sec +Time taken for epoch(OTHERo): 67.37 sec +<---------------------------------------|Epoch [5] END|---------------------------------------> + +Epoch: 6/486 (TSEC: 30) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 31/36 +128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 +Epoch 32/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 +Epoch 33/36 +128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 +Epoch 34/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 +Epoch 35/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 +Epoch 36/36 +128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2253 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 321.73 sec +Time taken for epoch(SUBo): 256.17 sec +Time taken for epoch(OTHERo): 65.57 sec +<---------------------------------------|Epoch [6] END|---------------------------------------> + +Epoch: 7/486 (TSEC: 36) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 37/42 +128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 +Epoch 38/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 +Epoch 39/42 +128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 +Epoch 40/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 +Epoch 41/42 +128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 +Epoch 42/42 +128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2070 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 326.03 sec +Time taken for epoch(SUBo): 259.27 sec +Time taken for epoch(OTHERo): 66.76 sec +<---------------------------------------|Epoch [7] END|---------------------------------------> + +Epoch: 8/486 (TSEC: 42) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 43/48 +128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 +Epoch 44/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 +Epoch 45/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 +Epoch 46/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 +Epoch 47/48 +128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 +Epoch 48/48 +128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2199 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Model loss did not improve from 0.20697814226150513. Not saving model. +Time taken for epoch(FULL): 322.67 sec +Time taken for epoch(SUBo): 256.59 sec +Time taken for epoch(OTHERo): 66.08 sec +<---------------------------------------|Epoch [8] END|---------------------------------------> + +Epoch: 9/486 (TSEC: 48) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 49/54 +128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 +Epoch 50/54 +128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 +Epoch 51/54 +128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 +Epoch 52/54 +128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 +Epoch 53/54 +128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 +Epoch 54/54 +128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.1662 +Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. +Saving full model H5 format... +Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 327.90 sec +Time taken for epoch(SUBo): 257.53 sec +Time taken for epoch(OTHERo): 70.37 sec +<---------------------------------------|Epoch [9] END|---------------------------------------> + +Epoch: 10/486 (TSEC: 54) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 55/60 +128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 +Epoch 56/60 +128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 +Epoch 57/60 +128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 +Epoch 58/60 +128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 +Epoch 59/60 +128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 +Epoch 60/60 +128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2059 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 327.86 sec +Time taken for epoch(SUBo): 259.66 sec +Time taken for epoch(OTHERo): 68.20 sec +<---------------------------------------|Epoch [10] END|---------------------------------------> + +Epoch: 11/486 (TSEC: 60) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 61/66 +128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 +Epoch 62/66 +128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 +Epoch 63/66 +128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 +Epoch 64/66 +128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 +Epoch 65/66 +128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 +Epoch 66/66 +128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2938 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 257.49 sec +Time taken for epoch(OTHERo): 69.06 sec +<---------------------------------------|Epoch [11] END|---------------------------------------> + +Epoch: 12/486 (TSEC: 66) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 67/72 +128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 +Epoch 68/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 +Epoch 69/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 +Epoch 70/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 +Epoch 71/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 +Epoch 72/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... +Model Test acc: 0.9631 +Model Test loss: 0.2115 +Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 324.68 sec +Time taken for epoch(SUBo): 253.65 sec +Time taken for epoch(OTHERo): 71.03 sec +<---------------------------------------|Epoch [12] END|---------------------------------------> + +Epoch: 13/486 (TSEC: 72) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 73/78 +128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 +Epoch 74/78 +128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 +Epoch 75/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 +Epoch 76/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 +Epoch 77/78 +128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 +Epoch 78/78 +128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.1545 +Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. +Saving full model H5 format... +Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.97 sec +Time taken for epoch(SUBo): 251.55 sec +Time taken for epoch(OTHERo): 74.42 sec +<---------------------------------------|Epoch [13] END|---------------------------------------> + +Epoch: 14/486 (TSEC: 78) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 79/84 +128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 +Epoch 80/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 +Epoch 81/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 +Epoch 82/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 +Epoch 83/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 +Epoch 84/84 +128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1780 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 71.59 sec +<---------------------------------------|Epoch [14] END|---------------------------------------> + +Epoch: 15/486 (TSEC: 84) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 85/90 +128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 +Epoch 86/90 +128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 +Epoch 87/90 +128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 +Epoch 88/90 +128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 +Epoch 89/90 +128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 +Epoch 90/90 +128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... +Model Test acc: 0.9615 +Model Test loss: 0.2233 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.17 sec +Time taken for epoch(SUBo): 252.81 sec +Time taken for epoch(OTHERo): 70.36 sec +<---------------------------------------|Epoch [15] END|---------------------------------------> + +Epoch: 16/486 (TSEC: 90) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 91/96 +128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 +Epoch 92/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 +Epoch 93/96 +128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 +Epoch 94/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 +Epoch 95/96 +128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 +Epoch 96/96 +128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1648 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 319.96 sec +Time taken for epoch(SUBo): 249.52 sec +Time taken for epoch(OTHERo): 70.43 sec +<---------------------------------------|Epoch [16] END|---------------------------------------> + +Epoch: 17/486 (TSEC: 96) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 97/102 +128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 +Epoch 98/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 +Epoch 99/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 +Epoch 100/102 +128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 +Epoch 101/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 +Epoch 102/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1764 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.46 sec +Time taken for epoch(SUBo): 253.14 sec +Time taken for epoch(OTHERo): 67.31 sec +<---------------------------------------|Epoch [17] END|---------------------------------------> + +Epoch: 18/486 (TSEC: 102) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 103/108 +128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 +Epoch 104/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 +Epoch 105/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 +Epoch 106/108 +128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 +Epoch 107/108 +128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 +Epoch 108/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2677 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.64 sec +Time taken for epoch(SUBo): 253.71 sec +Time taken for epoch(OTHERo): 70.93 sec +<---------------------------------------|Epoch [18] END|---------------------------------------> + +Epoch: 19/486 (TSEC: 108) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 109/114 +128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 +Epoch 110/114 +128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 +Epoch 111/114 +128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 +Epoch 112/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 +Epoch 113/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 +Epoch 114/114 +128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2055 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.02 sec +Time taken for epoch(SUBo): 253.02 sec +Time taken for epoch(OTHERo): 69.00 sec +<---------------------------------------|Epoch [19] END|---------------------------------------> + +Epoch: 20/486 (TSEC: 114) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 115/120 +128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 +Epoch 116/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 +Epoch 117/120 +128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 +Epoch 118/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 +Epoch 119/120 +128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 +Epoch 120/120 +128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... +Model Test acc: 0.9439 +Model Test loss: 0.3367 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 252.69 sec +Time taken for epoch(OTHERo): 70.57 sec +<---------------------------------------|Epoch [20] END|---------------------------------------> + +Epoch: 21/486 (TSEC: 120) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 121/126 +128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 +Epoch 122/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 +Epoch 123/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 +Epoch 124/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 +Epoch 125/126 +128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 +Epoch 126/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... +Model Test acc: 0.9583 +Model Test loss: 0.2373 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.10 sec +Time taken for epoch(SUBo): 252.57 sec +Time taken for epoch(OTHERo): 68.53 sec +<---------------------------------------|Epoch [21] END|---------------------------------------> + +Epoch: 22/486 (TSEC: 126) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 127/132 +128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 +Epoch 128/132 +128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 +Epoch 129/132 +128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 +Epoch 130/132 +128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 +Epoch 131/132 +128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 +Epoch 132/132 +128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2035 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.92 sec +Time taken for epoch(SUBo): 252.61 sec +Time taken for epoch(OTHERo): 69.31 sec +<---------------------------------------|Epoch [22] END|---------------------------------------> + +Epoch: 23/486 (TSEC: 132) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 133/138 +128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 +Epoch 134/138 +128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 +Epoch 135/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 +Epoch 136/138 +128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 +Epoch 137/138 +128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 +Epoch 138/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1983 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 251.30 sec +Time taken for epoch(OTHERo): 71.96 sec +<---------------------------------------|Epoch [23] END|---------------------------------------> + +Epoch: 24/486 (TSEC: 138) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01094]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 139/144 +128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 +Epoch 140/144 +128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 +Epoch 141/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 +Epoch 142/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 +Epoch 143/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 144/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... +Model Test acc: 0.9567 +Model Test loss: 0.2415 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.37 sec +Time taken for epoch(SUBo): 251.97 sec +Time taken for epoch(OTHERo): 72.40 sec +<---------------------------------------|Epoch [24] END|---------------------------------------> + +Epoch: 25/486 (TSEC: 144) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01088]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 145/150 +128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 +Epoch 146/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 +Epoch 147/150 +128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 +Epoch 148/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 +Epoch 149/150 +128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 +Epoch 150/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2961 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.24 sec +Time taken for epoch(SUBo): 250.77 sec +Time taken for epoch(OTHERo): 69.47 sec +<---------------------------------------|Epoch [25] END|---------------------------------------> + +Epoch: 26/486 (TSEC: 150) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01082]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 151/156 +128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 +Epoch 152/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 +Epoch 153/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 +Epoch 154/156 +128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 +Epoch 155/156 +128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 +Epoch 156/156 +128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2054 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.50 sec +Time taken for epoch(SUBo): 253.89 sec +Time taken for epoch(OTHERo): 68.61 sec +<---------------------------------------|Epoch [26] END|---------------------------------------> + +Epoch: 27/486 (TSEC: 156) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01076]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 157/162 +128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 +Epoch 158/162 +128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 +Epoch 159/162 +128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 +Epoch 160/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 +Epoch 161/162 +128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 +Epoch 162/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2812 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.25 sec +Time taken for epoch(SUBo): 253.57 sec +Time taken for epoch(OTHERo): 69.69 sec +<---------------------------------------|Epoch [27] END|---------------------------------------> + +Epoch: 28/486 (TSEC: 162) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0107]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 163/168 +128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 +Epoch 164/168 +128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 165/168 +128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 +Epoch 166/168 +128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 +Epoch 167/168 +128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 +Epoch 168/168 +128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3527 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.79 sec +Time taken for epoch(SUBo): 252.26 sec +Time taken for epoch(OTHERo): 68.52 sec +<---------------------------------------|Epoch [28] END|---------------------------------------> + +Epoch: 29/486 (TSEC: 168) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01064]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 169/174 +128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 +Epoch 170/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 +Epoch 171/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 +Epoch 172/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 +Epoch 173/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 +Epoch 174/174 +128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3280 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.85 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 70.35 sec +<---------------------------------------|Epoch [29] END|---------------------------------------> + +Epoch: 30/486 (TSEC: 174) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01058]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 175/180 +128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 +Epoch 176/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 +Epoch 177/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 +Epoch 178/180 +128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 +Epoch 179/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 +Epoch 180/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2430 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.08 sec +Time taken for epoch(SUBo): 252.22 sec +Time taken for epoch(OTHERo): 69.87 sec +<---------------------------------------|Epoch [30] END|---------------------------------------> + +Epoch: 31/486 (TSEC: 180) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01052]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 181/186 +128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 +Epoch 182/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 +Epoch 183/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 +Epoch 184/186 +128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 +Epoch 185/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 +Epoch 186/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9022 +Model Test loss: 0.5451 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.75 sec +Time taken for epoch(SUBo): 253.25 sec +Time taken for epoch(OTHERo): 69.50 sec +<---------------------------------------|Epoch [31] END|---------------------------------------> + +Epoch: 32/486 (TSEC: 186) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 +Setting training OneCycleLr::maxlr to [0.01046]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 187/192 +128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 +Epoch 188/192 +128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 +Epoch 189/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 +Epoch 190/192 +128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 +Epoch 191/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 +Epoch 192/192 +128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.2548 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 338.08 sec +Time taken for epoch(SUBo): 252.96 sec +Time taken for epoch(OTHERo): 85.12 sec +<---------------------------------------|Epoch [32] END|---------------------------------------> + +Epoch: 33/486 (TSEC: 192) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0104]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 193/198 +128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 +Epoch 194/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 +Epoch 195/198 +128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 +Epoch 196/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 +Epoch 197/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 +Epoch 198/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... +Model Test acc: 0.9663 +Model Test loss: 0.1816 +Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 328.41 sec +Time taken for epoch(SUBo): 253.11 sec +Time taken for epoch(OTHERo): 75.30 sec +<---------------------------------------|Epoch [33] END|---------------------------------------> + +Epoch: 34/486 (TSEC: 198) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01034]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 199/204 +128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 +Epoch 200/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 +Epoch 201/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 +Epoch 202/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 +Epoch 203/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 +Epoch 204/204 +128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2118 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 327.41 sec +Time taken for epoch(SUBo): 254.14 sec +Time taken for epoch(OTHERo): 73.27 sec +<---------------------------------------|Epoch [34] END|---------------------------------------> + +Epoch: 35/486 (TSEC: 204) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01028]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 205/210 +128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 +Epoch 206/210 +128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 +Epoch 207/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 +Epoch 208/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 +Epoch 209/210 +128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 +Epoch 210/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... +Model Test acc: 0.9551 +Model Test loss: 0.1544 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 329.96 sec +Time taken for epoch(SUBo): 252.88 sec +Time taken for epoch(OTHERo): 77.08 sec +<---------------------------------------|Epoch [35] END|---------------------------------------> + +Epoch: 36/486 (TSEC: 210) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01022]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 211/216 +128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 +Epoch 212/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 +Epoch 213/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 +Epoch 214/216 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 +Epoch 215/216 +128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 +Epoch 216/216 +128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3144 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 328.83 sec +Time taken for epoch(SUBo): 253.49 sec +Time taken for epoch(OTHERo): 75.34 sec +<---------------------------------------|Epoch [36] END|---------------------------------------> + +Epoch: 37/486 (TSEC: 216) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01016]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 217/222 +128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 +Epoch 218/222 +128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 +Epoch 219/222 +128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 +Epoch 220/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 +Epoch 221/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 +Epoch 222/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.4185 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.53 sec +Time taken for epoch(SUBo): 253.85 sec +Time taken for epoch(OTHERo): 73.68 sec +<---------------------------------------|Epoch [37] END|---------------------------------------> + +Epoch: 38/486 (TSEC: 222) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0101]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 223/228 +128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 +Epoch 224/228 +128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 +Epoch 225/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 +Epoch 226/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 +Epoch 227/228 +128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 +Epoch 228/228 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2645 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 252.83 sec +Time taken for epoch(OTHERo): 72.28 sec +<---------------------------------------|Epoch [38] END|---------------------------------------> + +Epoch: 39/486 (TSEC: 228) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01004]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 229/234 +128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 +Epoch 230/234 +128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 +Epoch 231/234 +128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 +Epoch 232/234 +128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 +Epoch 233/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 +Epoch 234/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.3982 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.39 sec +Time taken for epoch(SUBo): 254.95 sec +Time taken for epoch(OTHERo): 71.43 sec +<---------------------------------------|Epoch [39] END|---------------------------------------> + +Epoch: 40/486 (TSEC: 234) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00998]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 235/240 +128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 +Epoch 236/240 +128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 +Epoch 237/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 +Epoch 238/240 +128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 +Epoch 239/240 +128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 +Epoch 240/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2006 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.76 sec +Time taken for epoch(SUBo): 253.96 sec +Time taken for epoch(OTHERo): 71.80 sec +<---------------------------------------|Epoch [40] END|---------------------------------------> + +Epoch: 41/486 (TSEC: 240) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00992]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 241/246 +128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 +Epoch 242/246 +128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 +Epoch 243/246 +128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 +Epoch 244/246 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 +Epoch 245/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 +Epoch 246/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2011 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.07 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.68 sec +<---------------------------------------|Epoch [41] END|---------------------------------------> + +Epoch: 42/486 (TSEC: 246) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00986]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 247/252 +128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 +Epoch 248/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 +Epoch 249/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 +Epoch 250/252 +128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 +Epoch 251/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 +Epoch 252/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2136 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 253.59 sec +Time taken for epoch(OTHERo): 72.54 sec +<---------------------------------------|Epoch [42] END|---------------------------------------> + +Epoch: 43/486 (TSEC: 252) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0098]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 253/258 +128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 +Epoch 254/258 +128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 +Epoch 255/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 +Epoch 256/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 +Epoch 257/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 +Epoch 258/258 +128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2042 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.11 sec +Time taken for epoch(SUBo): 254.27 sec +Time taken for epoch(OTHERo): 72.84 sec +<---------------------------------------|Epoch [43] END|---------------------------------------> + +Epoch: 44/486 (TSEC: 258) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00974]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 259/264 +128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 +Epoch 260/264 +128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 +Epoch 261/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 +Epoch 262/264 +128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 +Epoch 263/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 +Epoch 264/264 +128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.3485 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 252.93 sec +Time taken for epoch(OTHERo): 73.19 sec +<---------------------------------------|Epoch [44] END|---------------------------------------> + +Epoch: 45/486 (TSEC: 264) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00968]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 265/270 +128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 +Epoch 266/270 +128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 +Epoch 267/270 +128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 +Epoch 268/270 +128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 +Epoch 269/270 +128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 +Epoch 270/270 +128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2324 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.17 sec +<---------------------------------------|Epoch [45] END|---------------------------------------> + +Epoch: 46/486 (TSEC: 270) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00962]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 271/276 +128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 +Epoch 272/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 +Epoch 273/276 +128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 +Epoch 274/276 +128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 +Epoch 275/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 +Epoch 276/276 +128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3108 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.76 sec +Time taken for epoch(SUBo): 254.60 sec +Time taken for epoch(OTHERo): 72.16 sec +<---------------------------------------|Epoch [46] END|---------------------------------------> + +Epoch: 47/486 (TSEC: 276) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00956]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 277/282 +128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 +Epoch 278/282 +128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 +Epoch 279/282 +128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 +Epoch 280/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 +Epoch 281/282 +128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 +Epoch 282/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3641 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.51 sec +Time taken for epoch(SUBo): 253.63 sec +Time taken for epoch(OTHERo): 72.88 sec +<---------------------------------------|Epoch [47] END|---------------------------------------> + +Epoch: 48/486 (TSEC: 282) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0095]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 283/288 +128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 +Epoch 284/288 +128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 +Epoch 285/288 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 +Epoch 286/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 +Epoch 287/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 +Epoch 288/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8958 +Model Test loss: 0.7104 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.17 sec +Time taken for epoch(SUBo): 255.62 sec +Time taken for epoch(OTHERo): 74.55 sec +<---------------------------------------|Epoch [48] END|---------------------------------------> + +Epoch: 49/486 (TSEC: 288) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00944]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 289/294 +128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 +Epoch 290/294 +128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 +Epoch 291/294 +128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 +Epoch 292/294 +128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 +Epoch 293/294 +128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 +Epoch 294/294 +128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9183 +Model Test loss: 0.5959 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.11 sec +Time taken for epoch(SUBo): 254.80 sec +Time taken for epoch(OTHERo): 75.32 sec +<---------------------------------------|Epoch [49] END|---------------------------------------> + +Epoch: 50/486 (TSEC: 294) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00938]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 295/300 +128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 +Epoch 296/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 +Epoch 297/300 +128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 +Epoch 298/300 +128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 +Epoch 299/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 +Epoch 300/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4308 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 331.10 sec +Time taken for epoch(SUBo): 255.03 sec +Time taken for epoch(OTHERo): 76.07 sec +<---------------------------------------|Epoch [50] END|---------------------------------------> + +Epoch: 51/486 (TSEC: 300) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00932]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 301/306 +128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 +Epoch 302/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 +Epoch 303/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 +Epoch 304/306 +128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 +Epoch 305/306 +128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 +Epoch 306/306 +128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8974 +Model Test loss: 0.7052 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 362.92 sec +Time taken for epoch(SUBo): 286.09 sec +Time taken for epoch(OTHERo): 76.83 sec +<---------------------------------------|Epoch [51] END|---------------------------------------> + +Epoch: 52/486 (TSEC: 306) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00926]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 307/312 +128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 +Epoch 308/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 +Epoch 309/312 +128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 +Epoch 310/312 +128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 +Epoch 311/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 +Epoch 312/312 +128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9135 +Model Test loss: 0.5680 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.05 sec +Time taken for epoch(SUBo): 284.61 sec +Time taken for epoch(OTHERo): 95.44 sec +<---------------------------------------|Epoch [52] END|---------------------------------------> + +Epoch: 53/486 (TSEC: 312) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0092]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 313/318 +128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 +Epoch 314/318 +128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 +Epoch 315/318 +128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 +Epoch 316/318 +128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 +Epoch 317/318 +128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 +Epoch 318/318 +128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4632 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.68 sec +Time taken for epoch(SUBo): 286.71 sec +Time taken for epoch(OTHERo): 93.97 sec +<---------------------------------------|Epoch [53] END|---------------------------------------> + +Epoch: 54/486 (TSEC: 318) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00914]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 319/324 +128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 +Epoch 320/324 +128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 +Epoch 321/324 +128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 +Epoch 322/324 +128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 +Epoch 323/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 +Epoch 324/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5101 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 389.45 sec +Time taken for epoch(SUBo): 287.64 sec +Time taken for epoch(OTHERo): 101.81 sec +<---------------------------------------|Epoch [54] END|---------------------------------------> + +Epoch: 55/486 (TSEC: 324) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00908]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 325/330 +128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 +Epoch 326/330 +128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 +Epoch 327/330 +128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 +Epoch 328/330 +128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 +Epoch 329/330 +128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 +Epoch 330/330 +128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3231 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.47 sec +Time taken for epoch(SUBo): 281.24 sec +Time taken for epoch(OTHERo): 99.23 sec +<---------------------------------------|Epoch [55] END|---------------------------------------> + +Epoch: 56/486 (TSEC: 330) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00902]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 331/336 +128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 +Epoch 332/336 +128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 +Epoch 333/336 +128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 +Epoch 334/336 +128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 +Epoch 335/336 +128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 +Epoch 336/336 +128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3479 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 392.43 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 103.65 sec +<---------------------------------------|Epoch [56] END|---------------------------------------> + +Epoch: 57/486 (TSEC: 336) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00896]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 337/342 +128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 +Epoch 338/342 +128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 +Epoch 339/342 +128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 +Epoch 340/342 +128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 +Epoch 341/342 +128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 +Epoch 342/342 +128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4083 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 387.98 sec +Time taken for epoch(SUBo): 285.68 sec +Time taken for epoch(OTHERo): 102.30 sec +<---------------------------------------|Epoch [57] END|---------------------------------------> + +Epoch: 58/486 (TSEC: 342) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0089]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 343/348 +128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 +Epoch 344/348 +128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 +Epoch 345/348 +128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 +Epoch 346/348 +128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 +Epoch 347/348 +128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 +Epoch 348/348 +128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3776 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 350.83 sec +Time taken for epoch(SUBo): 268.69 sec +Time taken for epoch(OTHERo): 82.14 sec +<---------------------------------------|Epoch [58] END|---------------------------------------> + +Epoch: 59/486 (TSEC: 348) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00884]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 349/354 +128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 +Epoch 350/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 +Epoch 351/354 +128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 +Epoch 352/354 +128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 +Epoch 353/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 +Epoch 354/354 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4066 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 353.60 sec +Time taken for epoch(SUBo): 258.60 sec +Time taken for epoch(OTHERo): 95.01 sec +<---------------------------------------|Epoch [59] END|---------------------------------------> + +Epoch: 60/486 (TSEC: 354) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00878]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 355/360 +128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 +Epoch 356/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 +Epoch 357/360 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 +Epoch 358/360 +128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 +Epoch 359/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 +Epoch 360/360 +128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4142 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 346.87 sec +Time taken for epoch(SUBo): 257.34 sec +Time taken for epoch(OTHERo): 89.53 sec +<---------------------------------------|Epoch [60] END|---------------------------------------> + +Epoch: 61/486 (TSEC: 360) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00872]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 361/366 +128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 +Epoch 362/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 +Epoch 363/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 +Epoch 364/366 +128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 +Epoch 365/366 +128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 +Epoch 366/366 +128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.7276 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.90 sec +Time taken for epoch(SUBo): 264.37 sec +Time taken for epoch(OTHERo): 77.53 sec +<---------------------------------------|Epoch [61] END|---------------------------------------> + +Epoch: 62/486 (TSEC: 366) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00866]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 367/372 +128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 +Epoch 368/372 +128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 +Epoch 369/372 +128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 +Epoch 370/372 +128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 +Epoch 371/372 +128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 +Epoch 372/372 +128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.2727 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 344.05 sec +Time taken for epoch(SUBo): 266.44 sec +Time taken for epoch(OTHERo): 77.61 sec +<---------------------------------------|Epoch [62] END|---------------------------------------> + +Epoch: 63/486 (TSEC: 372) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0086]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 373/378 +128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 +Epoch 374/378 +128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 +Epoch 375/378 +128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 +Epoch 376/378 +128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 +Epoch 377/378 +128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 +Epoch 378/378 +128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4211 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 334.11 sec +Time taken for epoch(SUBo): 258.37 sec +Time taken for epoch(OTHERo): 75.73 sec +<---------------------------------------|Epoch [63] END|---------------------------------------> + +Epoch: 64/486 (TSEC: 378) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 +Setting training OneCycleLr::maxlr to [0.00854]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 379/384 +128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 +Epoch 380/384 +128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 +Epoch 381/384 +128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 +Epoch 382/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 +Epoch 383/384 +128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 +Epoch 384/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3692 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 356.90 sec +Time taken for epoch(SUBo): 259.87 sec +Time taken for epoch(OTHERo): 97.03 sec +<---------------------------------------|Epoch [64] END|---------------------------------------> + +Epoch: 65/486 (TSEC: 384) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00848]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 385/390 +128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 +Epoch 386/390 +128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 +Epoch 387/390 +128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 +Epoch 388/390 +128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 +Epoch 389/390 +128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 +Epoch 390/390 +128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1823 +Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.22 sec +Time taken for epoch(SUBo): 257.30 sec +Time taken for epoch(OTHERo): 83.93 sec +<---------------------------------------|Epoch [65] END|---------------------------------------> + +Epoch: 66/486 (TSEC: 390) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00842]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 391/396 +128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 +Epoch 392/396 +128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 +Epoch 393/396 +128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 +Epoch 394/396 +128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 +Epoch 395/396 +128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 +Epoch 396/396 +128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9551 +Model Test loss: 0.2462 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 340.59 sec +Time taken for epoch(SUBo): 259.99 sec +Time taken for epoch(OTHERo): 80.59 sec +<---------------------------------------|Epoch [66] END|---------------------------------------> + +Epoch: 67/486 (TSEC: 396) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00836]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 397/402 +128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 +Epoch 398/402 +128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 +Epoch 399/402 +128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 +Epoch 400/402 +128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 +Epoch 401/402 +128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 +Epoch 402/402 +128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2736 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 348.32 sec +Time taken for epoch(SUBo): 267.55 sec +Time taken for epoch(OTHERo): 80.77 sec +<---------------------------------------|Epoch [67] END|---------------------------------------> + +Epoch: 68/486 (TSEC: 402) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0083]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 403/408 +128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 +Epoch 404/408 +128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 +Epoch 405/408 +128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 +Epoch 406/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 +Epoch 407/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 +Epoch 408/408 +128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.2983 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 374.47 sec +Time taken for epoch(SUBo): 276.39 sec +Time taken for epoch(OTHERo): 98.08 sec +<---------------------------------------|Epoch [68] END|---------------------------------------> + +Epoch: 69/486 (TSEC: 408) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00824]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 409/414 +128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 +Epoch 410/414 +128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 +Epoch 411/414 +128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 +Epoch 412/414 +128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 +Epoch 413/414 +128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 +Epoch 414/414 +128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3477 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 343.05 sec +Time taken for epoch(SUBo): 260.05 sec +Time taken for epoch(OTHERo): 83.00 sec +<---------------------------------------|Epoch [69] END|---------------------------------------> + +Epoch: 70/486 (TSEC: 414) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00818]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 415/420 +128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 +Epoch 416/420 +128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 +Epoch 417/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 +Epoch 418/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 +Epoch 419/420 +128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 +Epoch 420/420 +128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9167 +Model Test loss: 0.4541 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 342.13 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 79.85 sec +<---------------------------------------|Epoch [70] END|---------------------------------------> + +Epoch: 71/486 (TSEC: 420) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00812]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 421/426 +128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 +Epoch 422/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 +Epoch 423/426 +128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 +Epoch 424/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 +Epoch 425/426 +128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 +Epoch 426/426 +128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2110 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.68 sec +Time taken for epoch(SUBo): 260.39 sec +Time taken for epoch(OTHERo): 81.29 sec +<---------------------------------------|Epoch [71] END|---------------------------------------> + +Epoch: 72/486 (TSEC: 426) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00806]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 427/432 +128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 +Epoch 428/432 +128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 +Epoch 429/432 +128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 +Epoch 430/432 +128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 +Epoch 431/432 +128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 +Epoch 432/432 +128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... +Model Test acc: 0.9615 +Model Test loss: 0.1528 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 353.28 sec +Time taken for epoch(SUBo): 265.48 sec +Time taken for epoch(OTHERo): 87.80 sec +<---------------------------------------|Epoch [72] END|---------------------------------------> + +Epoch: 73/486 (TSEC: 432) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.008]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 433/438 +128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 +Epoch 434/438 +128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 +Epoch 435/438 +128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 +Epoch 436/438 +128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 +Epoch 437/438 +128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 +Epoch 438/438 +128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1676 +Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.79 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 111.40 sec +<---------------------------------------|Epoch [73] END|---------------------------------------> + +Epoch: 74/486 (TSEC: 438) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00794]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 439/444 +128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 +Epoch 440/444 +128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 +Epoch 441/444 +128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 +Epoch 442/444 +128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 +Epoch 443/444 +128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 +Epoch 444/444 +128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.3061 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.10 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 108.32 sec +<---------------------------------------|Epoch [74] END|---------------------------------------> + +Epoch: 75/486 (TSEC: 444) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00788]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 445/450 +128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 +Epoch 446/450 +128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 +Epoch 447/450 +128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 +Epoch 448/450 +128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 +Epoch 449/450 +128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 +Epoch 450/450 +128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.2149 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.95 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 108.55 sec +<---------------------------------------|Epoch [75] END|---------------------------------------> + +Epoch: 76/486 (TSEC: 450) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00782]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 451/456 +128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 +Epoch 452/456 +128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 +Epoch 453/456 +128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 +Epoch 454/456 +128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 +Epoch 455/456 +128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 +Epoch 456/456 +128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1664 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.35 sec +Time taken for epoch(SUBo): 290.41 sec +Time taken for epoch(OTHERo): 109.94 sec +<---------------------------------------|Epoch [76] END|---------------------------------------> + +Epoch: 77/486 (TSEC: 456) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00776]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 457/462 +128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 +Epoch 458/462 +128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 +Epoch 459/462 +128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 +Epoch 460/462 +128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 +Epoch 461/462 +128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 +Epoch 462/462 +128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1549 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 394.70 sec +Time taken for epoch(SUBo): 289.87 sec +Time taken for epoch(OTHERo): 104.83 sec +<---------------------------------------|Epoch [77] END|---------------------------------------> + +Epoch: 78/486 (TSEC: 462) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0077]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 463/468 +128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 +Epoch 464/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 +Epoch 465/468 +128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 +Epoch 466/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 +Epoch 467/468 +128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 +Epoch 468/468 +128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1365 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 398.75 sec +Time taken for epoch(SUBo): 289.42 sec +Time taken for epoch(OTHERo): 109.33 sec +<---------------------------------------|Epoch [78] END|---------------------------------------> + +Epoch: 79/486 (TSEC: 468) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00764]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 469/474 +128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 +Epoch 470/474 +128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 +Epoch 471/474 +128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 +Epoch 472/474 +128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 +Epoch 473/474 +128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 +Epoch 474/474 +128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2348 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 395.44 sec +Time taken for epoch(SUBo): 291.06 sec +Time taken for epoch(OTHERo): 104.39 sec +<---------------------------------------|Epoch [79] END|---------------------------------------> + +Epoch: 80/486 (TSEC: 474) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00758]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 475/480 +128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 +Epoch 476/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 +Epoch 477/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 +Epoch 478/480 +128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 +Epoch 479/480 +128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 +Epoch 480/480 +128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2441 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 399.87 sec +Time taken for epoch(SUBo): 293.34 sec +Time taken for epoch(OTHERo): 106.54 sec +<---------------------------------------|Epoch [80] END|---------------------------------------> + +Epoch: 81/486 (TSEC: 480) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00752]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 481/486 +128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 +Epoch 482/486 +128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 +Epoch 483/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 +Epoch 484/486 +128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 +Epoch 485/486 +128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 +Epoch 486/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3567 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.57 sec +Time taken for epoch(SUBo): 258.36 sec +Time taken for epoch(OTHERo): 102.21 sec +<---------------------------------------|Epoch [81] END|---------------------------------------> + +Epoch: 82/486 (TSEC: 486) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00746]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 487/492 +128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 +Epoch 488/492 +128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 +Epoch 489/492 +128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 +Epoch 490/492 +128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 +Epoch 491/492 +128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 +Epoch 492/492 +128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2877 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 338.30 sec +Time taken for epoch(SUBo): 256.81 sec +Time taken for epoch(OTHERo): 81.49 sec +<---------------------------------------|Epoch [82] END|---------------------------------------> + +Epoch: 83/486 (TSEC: 492) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0074]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 493/498 +128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 +Epoch 494/498 +128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 +Epoch 495/498 +128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 +Epoch 496/498 +128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 +Epoch 497/498 +128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 +Epoch 498/498 +128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3008 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 341.92 sec +Time taken for epoch(SUBo): 260.54 sec +Time taken for epoch(OTHERo): 81.38 sec +<---------------------------------------|Epoch [83] END|---------------------------------------> + +Epoch: 84/486 (TSEC: 498) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00734]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 499/504 +128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 +Epoch 500/504 +128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 +Epoch 501/504 +128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 +Epoch 502/504 +128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 +Epoch 503/504 +128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 +Epoch 504/504 +128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3369 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 401.59 sec +Time taken for epoch(SUBo): 296.36 sec +Time taken for epoch(OTHERo): 105.23 sec +<---------------------------------------|Epoch [84] END|---------------------------------------> + +Epoch: 85/486 (TSEC: 504) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00728]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 505/510 +128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 +Epoch 506/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 +Epoch 507/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 +Epoch 508/510 +128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 +Epoch 509/510 +128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 +Epoch 510/510 +128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2355 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 378.93 sec +Time taken for epoch(SUBo): 271.88 sec +Time taken for epoch(OTHERo): 107.05 sec +<---------------------------------------|Epoch [85] END|---------------------------------------> + +Epoch: 86/486 (TSEC: 510) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00722]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 511/516 +128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 +Epoch 512/516 +128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 +Epoch 513/516 +128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 +Epoch 514/516 +128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 +Epoch 515/516 +128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 +Epoch 516/516 +128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2163 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 348.35 sec +Time taken for epoch(SUBo): 264.53 sec +Time taken for epoch(OTHERo): 83.82 sec +<---------------------------------------|Epoch [86] END|---------------------------------------> + +Epoch: 87/486 (TSEC: 516) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00716]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 517/522 +128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 +Epoch 518/522 +128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 +Epoch 519/522 +128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 +Epoch 520/522 +128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 +Epoch 521/522 +128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 +Epoch 522/522 +128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.32 sec +Time taken for epoch(SUBo): 265.09 sec +Time taken for epoch(OTHERo): 84.23 sec +<---------------------------------------|Epoch [87] END|---------------------------------------> + +Epoch: 88/486 (TSEC: 522) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0071]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 523/528 +128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 +Epoch 524/528 +128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 +Epoch 525/528 +128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 +Epoch 526/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 +Epoch 527/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 +Epoch 528/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3755 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.31 sec +Time taken for epoch(SUBo): 260.67 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [88] END|---------------------------------------> + +Epoch: 89/486 (TSEC: 528) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00704]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 529/534 +128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 +Epoch 530/534 +128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 +Epoch 531/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 +Epoch 532/534 +128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 +Epoch 533/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 +Epoch 534/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1451 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 344.88 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 83.03 sec +<---------------------------------------|Epoch [89] END|---------------------------------------> + +Epoch: 90/486 (TSEC: 534) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00698]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 535/540 +128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 +Epoch 536/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 +Epoch 537/540 +128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 +Epoch 538/540 +128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 +Epoch 539/540 +128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 +Epoch 540/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2261 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 376.02 sec +Time taken for epoch(SUBo): 290.31 sec +Time taken for epoch(OTHERo): 85.71 sec +<---------------------------------------|Epoch [90] END|---------------------------------------> + +Epoch: 91/486 (TSEC: 540) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00692]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 541/546 +128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 +Epoch 542/546 +128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 +Epoch 543/546 +128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 +Epoch 544/546 +128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 +Epoch 545/546 +128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 +Epoch 546/546 +128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2810 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 409.06 sec +Time taken for epoch(SUBo): 293.27 sec +Time taken for epoch(OTHERo): 115.79 sec +<---------------------------------------|Epoch [91] END|---------------------------------------> + +Epoch: 92/486 (TSEC: 546) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00686]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 547/552 +128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 +Epoch 548/552 +128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 +Epoch 549/552 +128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 +Epoch 550/552 +128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 +Epoch 551/552 +128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 +Epoch 552/552 +128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2734 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 402.95 sec +Time taken for epoch(SUBo): 289.90 sec +Time taken for epoch(OTHERo): 113.04 sec +<---------------------------------------|Epoch [92] END|---------------------------------------> + +Epoch: 93/486 (TSEC: 552) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0068]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 553/558 +128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 +Epoch 554/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 +Epoch 555/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 +Epoch 556/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 +Epoch 557/558 +128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 +Epoch 558/558 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4314 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 343.82 sec +Time taken for epoch(SUBo): 258.19 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [93] END|---------------------------------------> + +Epoch: 94/486 (TSEC: 558) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00674]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 559/564 +128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 +Epoch 560/564 +128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 +Epoch 561/564 +128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 +Epoch 562/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 +Epoch 563/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 +Epoch 564/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2974 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.85 sec +Time taken for epoch(SUBo): 261.88 sec +Time taken for epoch(OTHERo): 85.97 sec +<---------------------------------------|Epoch [94] END|---------------------------------------> + +Epoch: 95/486 (TSEC: 564) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00668]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 565/570 +128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 +Epoch 566/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 +Epoch 567/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 +Epoch 568/570 +128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 +Epoch 569/570 +128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 +Epoch 570/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.4469 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.57 sec +Time taken for epoch(SUBo): 260.42 sec +Time taken for epoch(OTHERo): 89.15 sec +<---------------------------------------|Epoch [95] END|---------------------------------------> + +Epoch: 96/486 (TSEC: 570) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 +Setting training OneCycleLr::maxlr to [0.00662]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 571/576 +128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 +Epoch 572/576 +128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 +Epoch 573/576 +128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 +Epoch 574/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 +Epoch 575/576 +128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 +Epoch 576/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3027 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.90 sec +Time taken for epoch(SUBo): 261.28 sec +Time taken for epoch(OTHERo): 99.62 sec +<---------------------------------------|Epoch [96] END|---------------------------------------> + +Epoch: 97/486 (TSEC: 576) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00656]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 577/582 +128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 +Epoch 578/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 +Epoch 579/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 +Epoch 580/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 +Epoch 581/582 +128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 +Epoch 582/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3618 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.27 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 84.42 sec +<---------------------------------------|Epoch [97] END|---------------------------------------> + +Epoch: 98/486 (TSEC: 582) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0065]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 583/588 +128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 +Epoch 584/588 +128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 +Epoch 585/588 +128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 +Epoch 586/588 +128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 +Epoch 587/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 +Epoch 588/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3557 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.51 sec +Time taken for epoch(SUBo): 262.33 sec +Time taken for epoch(OTHERo): 83.18 sec +<---------------------------------------|Epoch [98] END|---------------------------------------> + +Epoch: 99/486 (TSEC: 588) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00644]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 589/594 +128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 +Epoch 590/594 +128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 +Epoch 591/594 +128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 +Epoch 592/594 +128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 +Epoch 593/594 +128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 +Epoch 594/594 +128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3592 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.37 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 85.09 sec +<---------------------------------------|Epoch [99] END|---------------------------------------> + +Epoch: 100/486 (TSEC: 594) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00638]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 595/600 +128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 +Epoch 596/600 +128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 +Epoch 597/600 +128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 +Epoch 598/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 +Epoch 599/600 +128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 +Epoch 600/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.3042 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.54 sec +Time taken for epoch(SUBo): 263.28 sec +Time taken for epoch(OTHERo): 82.26 sec +<---------------------------------------|Epoch [100] END|---------------------------------------> + +Epoch: 101/486 (TSEC: 600) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00632]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 601/606 +128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 +Epoch 602/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 +Epoch 603/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 +Epoch 604/606 +128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 605/606 +128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 +Epoch 606/606 +128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1757 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.57 sec +Time taken for epoch(SUBo): 261.73 sec +Time taken for epoch(OTHERo): 85.84 sec +<---------------------------------------|Epoch [101] END|---------------------------------------> + +Epoch: 102/486 (TSEC: 606) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00626]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 607/612 +128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 +Epoch 608/612 +128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 +Epoch 609/612 +128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 +Epoch 610/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 +Epoch 611/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 +Epoch 612/612 +128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.4659 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 350.48 sec +Time taken for epoch(SUBo): 263.62 sec +Time taken for epoch(OTHERo): 86.85 sec +<---------------------------------------|Epoch [102] END|---------------------------------------> + +Epoch: 103/486 (TSEC: 612) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0062]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 613/618 +128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 +Epoch 614/618 +128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 +Epoch 615/618 +128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 +Epoch 616/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 +Epoch 617/618 +128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 +Epoch 618/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3278 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 356.91 sec +Time taken for epoch(SUBo): 264.67 sec +Time taken for epoch(OTHERo): 92.23 sec +<---------------------------------------|Epoch [103] END|---------------------------------------> + +Epoch: 104/486 (TSEC: 618) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00614]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 619/624 +128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 +Epoch 620/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 +Epoch 621/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 +Epoch 622/624 +128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 +Epoch 623/624 +128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 +Epoch 624/624 +128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.4053 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.90 sec +Time taken for epoch(SUBo): 260.79 sec +Time taken for epoch(OTHERo): 86.11 sec +<---------------------------------------|Epoch [104] END|---------------------------------------> + +Epoch: 105/486 (TSEC: 624) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00608]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 625/630 +128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 +Epoch 626/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 +Epoch 627/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 +Epoch 628/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 +Epoch 629/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 +Epoch 630/630 +128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2148 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 353.04 sec +Time taken for epoch(SUBo): 264.40 sec +Time taken for epoch(OTHERo): 88.64 sec +<---------------------------------------|Epoch [105] END|---------------------------------------> + +Epoch: 106/486 (TSEC: 630) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00602]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 631/636 +128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 +Epoch 632/636 +128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 +Epoch 633/636 +128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 +Epoch 634/636 +128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 +Epoch 635/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 +Epoch 636/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1188 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 356.65 sec +Time taken for epoch(SUBo): 263.16 sec +Time taken for epoch(OTHERo): 93.49 sec +<---------------------------------------|Epoch [106] END|---------------------------------------> + +Epoch: 107/486 (TSEC: 636) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00596]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 637/642 +128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 +Epoch 638/642 +128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 +Epoch 639/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 +Epoch 640/642 +128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 +Epoch 641/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 +Epoch 642/642 +128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2388 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 353.97 sec +Time taken for epoch(SUBo): 260.86 sec +Time taken for epoch(OTHERo): 93.11 sec +<---------------------------------------|Epoch [107] END|---------------------------------------> + +Epoch: 108/486 (TSEC: 642) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0059]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 643/648 +128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 +Epoch 644/648 +128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 +Epoch 645/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 +Epoch 646/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 +Epoch 647/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 +Epoch 648/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2612 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 351.69 sec +Time taken for epoch(SUBo): 260.60 sec +Time taken for epoch(OTHERo): 91.10 sec +<---------------------------------------|Epoch [108] END|---------------------------------------> + +Epoch: 109/486 (TSEC: 648) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00584]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 649/654 +128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 +Epoch 650/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 +Epoch 651/654 +128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 +Epoch 652/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 +Epoch 653/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 +Epoch 654/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3569 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 346.32 sec +Time taken for epoch(SUBo): 259.69 sec +Time taken for epoch(OTHERo): 86.63 sec +<---------------------------------------|Epoch [109] END|---------------------------------------> + +Epoch: 110/486 (TSEC: 654) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00578]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 655/660 +128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 +Epoch 656/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 +Epoch 657/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 +Epoch 658/660 +128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 +Epoch 659/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 +Epoch 660/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3959 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 347.66 sec +Time taken for epoch(SUBo): 261.10 sec +Time taken for epoch(OTHERo): 86.56 sec +<---------------------------------------|Epoch [110] END|---------------------------------------> + +Epoch: 111/486 (TSEC: 660) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00572]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 661/666 +128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 +Epoch 662/666 +128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 +Epoch 663/666 +128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 +Epoch 664/666 +128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 +Epoch 665/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 +Epoch 666/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2614 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 374.77 sec +Time taken for epoch(SUBo): 287.07 sec +Time taken for epoch(OTHERo): 87.70 sec +<---------------------------------------|Epoch [111] END|---------------------------------------> + +Epoch: 112/486 (TSEC: 666) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00566]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 667/672 +128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 +Epoch 668/672 +128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 +Epoch 669/672 +128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 +Epoch 670/672 +128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 +Epoch 671/672 +128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 +Epoch 672/672 +128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2773 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.29 sec +Time taken for epoch(SUBo): 294.69 sec +Time taken for epoch(OTHERo): 108.60 sec +<---------------------------------------|Epoch [112] END|---------------------------------------> + +Epoch: 113/486 (TSEC: 672) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0056]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 673/678 +128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 +Epoch 674/678 +128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 +Epoch 675/678 +128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 676/678 +128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 +Epoch 677/678 +128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 +Epoch 678/678 +128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3138 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 405.22 sec +Time taken for epoch(SUBo): 290.78 sec +Time taken for epoch(OTHERo): 114.43 sec +<---------------------------------------|Epoch [113] END|---------------------------------------> + +Epoch: 114/486 (TSEC: 678) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00554]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 679/684 +128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 +Epoch 680/684 +128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 +Epoch 681/684 +128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 +Epoch 682/684 +128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 +Epoch 683/684 +128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 +Epoch 684/684 +128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.12 sec +Time taken for epoch(SUBo): 294.80 sec +Time taken for epoch(OTHERo): 117.31 sec +<---------------------------------------|Epoch [114] END|---------------------------------------> + +Epoch: 115/486 (TSEC: 684) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00548]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 685/690 +128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 +Epoch 686/690 +128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 +Epoch 687/690 +128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 +Epoch 688/690 +128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 +Epoch 689/690 +128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 +Epoch 690/690 +128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4240 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.16 sec +Time taken for epoch(SUBo): 291.71 sec +Time taken for epoch(OTHERo): 110.46 sec +<---------------------------------------|Epoch [115] END|---------------------------------------> + +Epoch: 116/486 (TSEC: 690) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00542]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 691/696 +128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 +Epoch 692/696 +128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 +Epoch 693/696 +128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 +Epoch 694/696 +128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 +Epoch 695/696 +128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 +Epoch 696/696 +128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3796 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.58 sec +Time taken for epoch(SUBo): 295.23 sec +Time taken for epoch(OTHERo): 113.35 sec +<---------------------------------------|Epoch [116] END|---------------------------------------> + +Epoch: 117/486 (TSEC: 696) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00536]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 697/702 +128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 +Epoch 698/702 +128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 +Epoch 699/702 +128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 +Epoch 700/702 +128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 +Epoch 701/702 +128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 +Epoch 702/702 +128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3449 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.03 sec +Time taken for epoch(SUBo): 295.99 sec +Time taken for epoch(OTHERo): 115.05 sec +<---------------------------------------|Epoch [117] END|---------------------------------------> + +Epoch: 118/486 (TSEC: 702) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0053]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 703/708 +128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 +Epoch 704/708 +128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 +Epoch 705/708 +128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 +Epoch 706/708 +128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 +Epoch 707/708 +128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 +Epoch 708/708 +128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2279 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.63 sec +Time taken for epoch(SUBo): 296.34 sec +Time taken for epoch(OTHERo): 117.29 sec +<---------------------------------------|Epoch [118] END|---------------------------------------> + +Epoch: 119/486 (TSEC: 708) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00524]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 709/714 +128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 +Epoch 710/714 +128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 +Epoch 711/714 +128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 +Epoch 712/714 +128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 +Epoch 713/714 +128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 +Epoch 714/714 +128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3194 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.60 sec +Time taken for epoch(SUBo): 294.03 sec +Time taken for epoch(OTHERo): 114.57 sec +<---------------------------------------|Epoch [119] END|---------------------------------------> + +Epoch: 120/486 (TSEC: 714) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00518]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 715/720 +128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 +Epoch 716/720 +128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 +Epoch 717/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 +Epoch 718/720 +128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 +Epoch 719/720 +128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 +Epoch 720/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.5143 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.26 sec +Time taken for epoch(SUBo): 295.66 sec +Time taken for epoch(OTHERo): 112.60 sec +<---------------------------------------|Epoch [120] END|---------------------------------------> + +Epoch: 121/486 (TSEC: 720) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00512]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 721/726 +128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 +Epoch 722/726 +128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 +Epoch 723/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 +Epoch 724/726 +128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 +Epoch 725/726 +128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 +Epoch 726/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4177 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.06 sec +Time taken for epoch(SUBo): 295.42 sec +Time taken for epoch(OTHERo): 118.64 sec +<---------------------------------------|Epoch [121] END|---------------------------------------> + +Epoch: 122/486 (TSEC: 726) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00506]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 727/732 +128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 +Epoch 728/732 +128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 +Epoch 729/732 +128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 +Epoch 730/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 +Epoch 731/732 +128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 +Epoch 732/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4436 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.37 sec +Time taken for epoch(SUBo): 293.85 sec +Time taken for epoch(OTHERo): 117.52 sec +<---------------------------------------|Epoch [122] END|---------------------------------------> + +Epoch: 123/486 (TSEC: 732) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.005]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 733/738 +128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 +Epoch 734/738 +128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 +Epoch 735/738 +128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 +Epoch 736/738 +128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 +Epoch 737/738 +128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 +Epoch 738/738 +128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4303 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.96 sec +Time taken for epoch(SUBo): 294.39 sec +Time taken for epoch(OTHERo): 118.57 sec +<---------------------------------------|Epoch [123] END|---------------------------------------> + +Epoch: 124/486 (TSEC: 738) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00494]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 739/744 +128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 +Epoch 740/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 +Epoch 741/744 +128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 +Epoch 742/744 +128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 +Epoch 743/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 +Epoch 744/744 +128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4234 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.23 sec +Time taken for epoch(SUBo): 298.41 sec +Time taken for epoch(OTHERo): 114.83 sec +<---------------------------------------|Epoch [124] END|---------------------------------------> + +Epoch: 125/486 (TSEC: 744) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00488]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 745/750 +128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 +Epoch 746/750 +128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 +Epoch 747/750 +128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 +Epoch 748/750 +128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 +Epoch 749/750 +128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 +Epoch 750/750 +128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3677 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.29 sec +Time taken for epoch(SUBo): 293.29 sec +Time taken for epoch(OTHERo): 119.99 sec +<---------------------------------------|Epoch [125] END|---------------------------------------> + +Epoch: 126/486 (TSEC: 750) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00482]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 751/756 +128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 +Epoch 752/756 +128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 +Epoch 753/756 +128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 +Epoch 754/756 +128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 +Epoch 755/756 +128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 +Epoch 756/756 +128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3409 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.45 sec +Time taken for epoch(SUBo): 293.26 sec +Time taken for epoch(OTHERo): 110.19 sec +<---------------------------------------|Epoch [126] END|---------------------------------------> + +Epoch: 127/486 (TSEC: 756) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00476]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 757/762 +128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 +Epoch 758/762 +128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 +Epoch 759/762 +128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 +Epoch 760/762 +128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 +Epoch 761/762 +128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 +Epoch 762/762 +128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3632 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.89 sec +Time taken for epoch(SUBo): 291.93 sec +Time taken for epoch(OTHERo): 111.96 sec +<---------------------------------------|Epoch [127] END|---------------------------------------> + +Epoch: 128/486 (TSEC: 762) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 +Setting training OneCycleLr::maxlr to [0.0047]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 763/768 +128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 +Epoch 764/768 +128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 +Epoch 765/768 +128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 +Epoch 766/768 +128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 +Epoch 767/768 +128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 +Epoch 768/768 +128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3065 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 425.95 sec +Time taken for epoch(SUBo): 291.59 sec +Time taken for epoch(OTHERo): 134.36 sec +<---------------------------------------|Epoch [128] END|---------------------------------------> + +Epoch: 129/486 (TSEC: 768) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00464]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 769/774 +128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 +Epoch 770/774 +128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 +Epoch 771/774 +128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 +Epoch 772/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 +Epoch 773/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 +Epoch 774/774 +128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2221 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.23 sec +Time taken for epoch(SUBo): 293.03 sec +Time taken for epoch(OTHERo): 109.20 sec +<---------------------------------------|Epoch [129] END|---------------------------------------> + +Epoch: 130/486 (TSEC: 774) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00458]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 775/780 +128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 +Epoch 776/780 +128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 +Epoch 777/780 +128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 +Epoch 778/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 +Epoch 779/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 +Epoch 780/780 +128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3315 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.71 sec +Time taken for epoch(SUBo): 293.90 sec +Time taken for epoch(OTHERo): 108.81 sec +<---------------------------------------|Epoch [130] END|---------------------------------------> + +Epoch: 131/486 (TSEC: 780) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00452]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 781/786 +128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 +Epoch 782/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 +Epoch 783/786 +128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 +Epoch 784/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 +Epoch 785/786 +128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 +Epoch 786/786 +128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.5019 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 424.42 sec +Time taken for epoch(SUBo): 300.59 sec +Time taken for epoch(OTHERo): 123.83 sec +<---------------------------------------|Epoch [131] END|---------------------------------------> + +Epoch: 132/486 (TSEC: 786) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00446]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 787/792 +128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 +Epoch 788/792 +128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 +Epoch 789/792 +128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 +Epoch 790/792 +128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 +Epoch 791/792 +128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 +Epoch 792/792 +128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9215 +Model Test loss: 0.5337 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.90 sec +Time taken for epoch(SUBo): 299.61 sec +Time taken for epoch(OTHERo): 121.28 sec +<---------------------------------------|Epoch [132] END|---------------------------------------> + +Epoch: 133/486 (TSEC: 792) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0044]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 793/798 +128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 +Epoch 794/798 +128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 +Epoch 795/798 +128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 +Epoch 796/798 +128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 +Epoch 797/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 +Epoch 798/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.5005 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.52 sec +Time taken for epoch(SUBo): 296.92 sec +Time taken for epoch(OTHERo): 120.59 sec +<---------------------------------------|Epoch [133] END|---------------------------------------> + +Epoch: 134/486 (TSEC: 798) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00434]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 799/804 +128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 +Epoch 800/804 +128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 +Epoch 801/804 +128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 +Epoch 802/804 +128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 +Epoch 803/804 +128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 +Epoch 804/804 +128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9151 +Model Test loss: 0.5460 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.80 sec +Time taken for epoch(SUBo): 297.98 sec +Time taken for epoch(OTHERo): 122.82 sec +<---------------------------------------|Epoch [134] END|---------------------------------------> + +Epoch: 135/486 (TSEC: 804) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00428]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 805/810 +128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 +Epoch 806/810 +128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 +Epoch 807/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 +Epoch 808/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 +Epoch 809/810 +128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 +Epoch 810/810 +128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9103 +Model Test loss: 0.5751 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.70 sec +Time taken for epoch(SUBo): 297.58 sec +Time taken for epoch(OTHERo): 117.13 sec +<---------------------------------------|Epoch [135] END|---------------------------------------> + +Epoch: 136/486 (TSEC: 810) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00422]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 811/816 +128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 +Epoch 812/816 +128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 +Epoch 813/816 +128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 +Epoch 814/816 +128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 +Epoch 815/816 +128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 +Epoch 816/816 +128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3819 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.58 sec +Time taken for epoch(SUBo): 300.30 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [136] END|---------------------------------------> + +Epoch: 137/486 (TSEC: 816) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00416]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 817/822 +128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 +Epoch 818/822 +128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 +Epoch 819/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 +Epoch 820/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 +Epoch 821/822 +128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 +Epoch 822/822 +128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4434 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.54 sec +Time taken for epoch(SUBo): 297.62 sec +Time taken for epoch(OTHERo): 118.92 sec +<---------------------------------------|Epoch [137] END|---------------------------------------> + +Epoch: 138/486 (TSEC: 822) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0041]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 823/828 +128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 +Epoch 824/828 +128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 +Epoch 825/828 +128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 +Epoch 826/828 +128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 +Epoch 827/828 +128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 +Epoch 828/828 +128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4551 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.46 sec +Time taken for epoch(SUBo): 297.55 sec +Time taken for epoch(OTHERo): 117.91 sec +<---------------------------------------|Epoch [138] END|---------------------------------------> + +Epoch: 139/486 (TSEC: 828) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00404]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 829/834 +128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 +Epoch 830/834 +128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 +Epoch 831/834 +128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 +Epoch 832/834 +128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 +Epoch 833/834 +128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 +Epoch 834/834 +128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.5237 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 418.66 sec +Time taken for epoch(SUBo): 295.90 sec +Time taken for epoch(OTHERo): 122.76 sec +<---------------------------------------|Epoch [139] END|---------------------------------------> + +Epoch: 140/486 (TSEC: 834) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00398]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 835/840 +128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 +Epoch 836/840 +128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 +Epoch 837/840 +128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 +Epoch 838/840 +128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 +Epoch 839/840 +128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 +Epoch 840/840 +128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5710 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.43 sec +Time taken for epoch(SUBo): 298.12 sec +Time taken for epoch(OTHERo): 122.31 sec +<---------------------------------------|Epoch [140] END|---------------------------------------> + +Epoch: 141/486 (TSEC: 840) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00392]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 841/846 +128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 +Epoch 842/846 +128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 +Epoch 843/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 +Epoch 844/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 +Epoch 845/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 +Epoch 846/846 +128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.5128 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 423.15 sec +Time taken for epoch(SUBo): 298.17 sec +Time taken for epoch(OTHERo): 124.98 sec +<---------------------------------------|Epoch [141] END|---------------------------------------> + +Epoch: 142/486 (TSEC: 846) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00386]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 847/852 +128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 +Epoch 848/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 +Epoch 849/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 +Epoch 850/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 +Epoch 851/852 +128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 +Epoch 852/852 +128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5103 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.49 sec +Time taken for epoch(SUBo): 301.87 sec +Time taken for epoch(OTHERo): 114.61 sec +<---------------------------------------|Epoch [142] END|---------------------------------------> + +Epoch: 143/486 (TSEC: 852) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0038]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 853/858 +128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 +Epoch 854/858 +128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 +Epoch 855/858 +128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 +Epoch 856/858 +128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 +Epoch 857/858 +128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 +Epoch 858/858 +128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.7814 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.74 sec +Time taken for epoch(SUBo): 295.81 sec +Time taken for epoch(OTHERo): 113.94 sec +<---------------------------------------|Epoch [143] END|---------------------------------------> + +Epoch: 144/486 (TSEC: 858) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00374]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 859/864 +128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 +Epoch 860/864 +128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 +Epoch 861/864 +128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 +Epoch 862/864 +128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 +Epoch 863/864 +128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 +Epoch 864/864 +128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3838 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.79 sec +Time taken for epoch(SUBo): 295.17 sec +Time taken for epoch(OTHERo): 118.61 sec +<---------------------------------------|Epoch [144] END|---------------------------------------> + +Epoch: 145/486 (TSEC: 864) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00368]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 865/870 +128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 +Epoch 866/870 +128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 +Epoch 867/870 +128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 +Epoch 868/870 +128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 +Epoch 869/870 +128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 +Epoch 870/870 +128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3789 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.68 sec +Time taken for epoch(SUBo): 295.62 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [145] END|---------------------------------------> + +Epoch: 146/486 (TSEC: 870) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00362]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 871/876 +128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 +Epoch 872/876 +128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 +Epoch 873/876 +128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 +Epoch 874/876 +128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 +Epoch 875/876 +128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 +Epoch 876/876 +128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.5834 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.30 sec +Time taken for epoch(SUBo): 298.45 sec +Time taken for epoch(OTHERo): 116.86 sec +<---------------------------------------|Epoch [146] END|---------------------------------------> + +Epoch: 147/486 (TSEC: 876) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00356]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 877/882 +128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 +Epoch 878/882 +128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 +Epoch 879/882 +128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 +Epoch 880/882 +128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 +Epoch 881/882 +128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 +Epoch 882/882 +128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3896 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.42 sec +Time taken for epoch(SUBo): 297.44 sec +Time taken for epoch(OTHERo): 119.98 sec +<---------------------------------------|Epoch [147] END|---------------------------------------> + +Epoch: 148/486 (TSEC: 882) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0035]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 883/888 +128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 +Epoch 884/888 +128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 +Epoch 885/888 +128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 +Epoch 886/888 +128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 +Epoch 887/888 +128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 +Epoch 888/888 +128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5339 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.09 sec +Time taken for epoch(SUBo): 293.02 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [148] END|---------------------------------------> + +Epoch: 149/486 (TSEC: 888) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00344]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 889/894 +128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 +Epoch 890/894 +128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 +Epoch 891/894 +128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 +Epoch 892/894 +128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 +Epoch 893/894 +128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 +Epoch 894/894 +128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.4329 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.69 sec +Time taken for epoch(SUBo): 298.62 sec +Time taken for epoch(OTHERo): 117.07 sec +<---------------------------------------|Epoch [149] END|---------------------------------------> + +Epoch: 150/486 (TSEC: 894) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00338]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 895/900 +128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 +Epoch 896/900 +128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 +Epoch 897/900 +128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 +Epoch 898/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 +Epoch 899/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 +Epoch 900/900 +128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.6034 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.43 sec +Time taken for epoch(SUBo): 292.15 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [150] END|---------------------------------------> + +Epoch: 151/486 (TSEC: 900) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00332]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 901/906 +128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 +Epoch 902/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 +Epoch 903/906 +128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 +Epoch 904/906 +128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 +Epoch 905/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 +Epoch 906/906 +128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3160 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.64 sec +Time taken for epoch(SUBo): 296.21 sec +Time taken for epoch(OTHERo): 120.43 sec +<---------------------------------------|Epoch [151] END|---------------------------------------> + +Epoch: 152/486 (TSEC: 906) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00326]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 907/912 +128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 +Epoch 908/912 +128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 +Epoch 909/912 +128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 +Epoch 910/912 +128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 +Epoch 911/912 + 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 +KeyboardInterrupt. +Training done. + diff --git a/Tools/Change_imgF.py b/Tools/Change_imgF.py index 5cab880..90d4ae1 100644 --- a/Tools/Change_imgF.py +++ b/Tools/Change_imgF.py @@ -1,50 +1,50 @@ -import os -import uuid -import shutil -from PIL import Image -from tqdm import tqdm - -def convert_image_format(image_path, output_path, output_format): - # Open an image file - with Image.open(image_path) as img: - # Convert the image to the desired format and save it - img.save(output_path, output_format) - -def convert_images_in_dir(input_dir, output_dir, output_format): - # Check if output directory exists, if not, create it - if not os.path.exists(output_dir): - os.makedirs(output_dir) - - # Get a list of all files in the input directory - filenames = os.listdir(input_dir) - - # Create a progress bar - with tqdm(total=len(filenames), desc="Converting images", ncols=75, unit='img') as pbar: - # Iterate over all files in the input directory - for filename in filenames: - # Create the full input path - input_path = os.path.join(input_dir, filename) - - # Generate a random unique name for the output file - unique_name = str(uuid.uuid4()) - output_path = os.path.join(output_dir, f"{unique_name}.{output_format}") - - # Check if the image is already in the desired format - if filename.lower().endswith(f".{output_format}"): - # If it is, copy the image to the new location - # Use the original file extension for the output path - original_extension = os.path.splitext(filename)[1] - shutil.copy(input_path, os.path.join(output_dir, f"{unique_name}{original_extension}")) - else: - # If it's not, open and save the image in the new format - with Image.open(input_path) as img: - img.save(output_path, output_format) - - # Update the progress bar - pbar.update(1) - -print('NORMAL conv...') -convert_images_in_dir('Database\\Train\\Data\\train\\NORMAL', 'Database\\Train\\Data\\train\\NORMAL_NEW', 'jpeg') -print('PNEUMONIA conv...') -convert_images_in_dir('Database\\Train\\Data\\train\\PNEUMONIA_NEW', 'Database\\Train\\Data\\train\\PNEUMONIA_NEW', 'jpeg') -print('done.') +import os +import uuid +import shutil +from PIL import Image +from tqdm import tqdm + +def convert_image_format(image_path, output_path, output_format): + # Open an image file + with Image.open(image_path) as img: + # Convert the image to the desired format and save it + img.save(output_path, output_format) + +def convert_images_in_dir(input_dir, output_dir, output_format): + # Check if output directory exists, if not, create it + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Get a list of all files in the input directory + filenames = os.listdir(input_dir) + + # Create a progress bar + with tqdm(total=len(filenames), desc="Converting images", ncols=75, unit='img') as pbar: + # Iterate over all files in the input directory + for filename in filenames: + # Create the full input path + input_path = os.path.join(input_dir, filename) + + # Generate a random unique name for the output file + unique_name = str(uuid.uuid4()) + output_path = os.path.join(output_dir, f"{unique_name}.{output_format}") + + # Check if the image is already in the desired format + if filename.lower().endswith(f".{output_format}"): + # If it is, copy the image to the new location + # Use the original file extension for the output path + original_extension = os.path.splitext(filename)[1] + shutil.copy(input_path, os.path.join(output_dir, f"{unique_name}{original_extension}")) + else: + # If it's not, open and save the image in the new format + with Image.open(input_path) as img: + img.save(output_path, output_format) + + # Update the progress bar + pbar.update(1) + +print('NORMAL conv...') +convert_images_in_dir('Database\\Train\\Data\\train\\NORMAL', 'Database\\Train\\Data\\train\\NORMAL_NEW', 'jpeg') +print('PNEUMONIA conv...') +convert_images_in_dir('Database\\Train\\Data\\train\\PNEUMONIA_NEW', 'Database\\Train\\Data\\train\\PNEUMONIA_NEW', 'jpeg') +print('done.') diff --git a/Tools/README.txt b/Tools/README.txt index b8e4e67..909bb55 100644 --- a/Tools/README.txt +++ b/Tools/README.txt @@ -1,2 +1,2 @@ -'Tools\Change_imgF.py': You can use this code to merging two datasets with different image formats. +'Tools\Change_imgF.py': You can use this code to merging two datasets with different image formats. 'Tools\RSNA_DICOM_data.py': You can use this code to convert the RSNA dataset DICOM files to normal image (jpeg) files to expand the dataset. \ No newline at end of file diff --git a/Tools/RSNA_DICOM_data.py b/Tools/RSNA_DICOM_data.py index 4dc5f35..4419e06 100644 --- a/Tools/RSNA_DICOM_data.py +++ b/Tools/RSNA_DICOM_data.py @@ -1,54 +1,54 @@ -import os -import pydicom -import pandas as pd -import numpy as np -from PIL import Image -from tqdm import tqdm - -# Directory containing the DICOM files -dcm_dir = 'stage_2_train_images' - -# Read the CSV file -df = pd.read_csv('stage_2_detailed_class_info.csv') - -# Directories for the three classes -not_normal_dir = 'database/Not Normal' -normal_dir = 'database/NORMAL' -pneumonia_dir = 'database/PNEUMONIA' - -# Create the directories if they don't exist -os.makedirs(not_normal_dir, exist_ok=True) -os.makedirs(normal_dir, exist_ok=True) -os.makedirs(pneumonia_dir, exist_ok=True) - -# Get the list of files -files = [f for f in os.listdir(dcm_dir) if f.endswith('.dcm')] - -# Initialize the progress bar -pbar = tqdm(total=len(files), desc='Processing DICOM files') - -# Loop over all files in the directory -for filename in files: - # Read the DICOM file - dcm = pydicom.dcmread(os.path.join(dcm_dir, filename)) - # Get the pixel array from the DICOM file - pixels = dcm.pixel_array - # Convert the pixel array to an image - img = Image.fromarray(pixels) - # Get the label for this file - label = df[df['patientId'] == filename[:-4]]['class'].values[0] - # Save the image to the appropriate directory with the original filename - if label == 'No Lung Opacity / Not Normal': - img.save(os.path.join(not_normal_dir, filename[:-4] + '.jpeg')) - # print(f'Saved {filename[:-4]}.jpeg to {not_normal_dir}') - elif label == 'Normal': - img.save(os.path.join(normal_dir, filename[:-4] + '.jpeg')) - # print(f'Saved {filename[:-4]}.jpeg to {normal_dir}') - else: # 'Lung Opacity' - img.save(os.path.join(pneumonia_dir, filename[:-4] + '.jpeg')) - # print(f'Saved {filename[:-4]}.jpeg to {pneumonia_dir}') - # Update the progress bar - pbar.update(1) - -# Close the progress bar -pbar.close() +import os +import pydicom +import pandas as pd +import numpy as np +from PIL import Image +from tqdm import tqdm + +# Directory containing the DICOM files +dcm_dir = 'stage_2_train_images' + +# Read the CSV file +df = pd.read_csv('stage_2_detailed_class_info.csv') + +# Directories for the three classes +not_normal_dir = 'database/Not Normal' +normal_dir = 'database/NORMAL' +pneumonia_dir = 'database/PNEUMONIA' + +# Create the directories if they don't exist +os.makedirs(not_normal_dir, exist_ok=True) +os.makedirs(normal_dir, exist_ok=True) +os.makedirs(pneumonia_dir, exist_ok=True) + +# Get the list of files +files = [f for f in os.listdir(dcm_dir) if f.endswith('.dcm')] + +# Initialize the progress bar +pbar = tqdm(total=len(files), desc='Processing DICOM files') + +# Loop over all files in the directory +for filename in files: + # Read the DICOM file + dcm = pydicom.dcmread(os.path.join(dcm_dir, filename)) + # Get the pixel array from the DICOM file + pixels = dcm.pixel_array + # Convert the pixel array to an image + img = Image.fromarray(pixels) + # Get the label for this file + label = df[df['patientId'] == filename[:-4]]['class'].values[0] + # Save the image to the appropriate directory with the original filename + if label == 'No Lung Opacity / Not Normal': + img.save(os.path.join(not_normal_dir, filename[:-4] + '.jpeg')) + # print(f'Saved {filename[:-4]}.jpeg to {not_normal_dir}') + elif label == 'Normal': + img.save(os.path.join(normal_dir, filename[:-4] + '.jpeg')) + # print(f'Saved {filename[:-4]}.jpeg to {normal_dir}') + else: # 'Lung Opacity' + img.save(os.path.join(pneumonia_dir, filename[:-4] + '.jpeg')) + # print(f'Saved {filename[:-4]}.jpeg to {pneumonia_dir}') + # Update the progress bar + pbar.update(1) + +# Close the progress bar +pbar.close() diff --git a/Update_Code.cmd b/Update_Code.cmd index 7b921c7..1badd28 100644 --- a/Update_Code.cmd +++ b/Update_Code.cmd @@ -1,3 +1,3 @@ -@echo off -del "Model_T&T.ipynb" +@echo off +del "Model_T&T.ipynb" copy "BETA_E_Model_T&T.ipynb" "Model_T&T.ipynb" \ No newline at end of file diff --git a/Utils/Grad_cam.py b/Utils/Grad_cam.py index fc2a71f..c63729a 100644 --- a/Utils/Grad_cam.py +++ b/Utils/Grad_cam.py @@ -1,63 +1,63 @@ -import os -import glob -import numpy as np -import tensorflow as tf -# Other -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -tf.get_logger().setLevel('ERROR') -physical_devices = tf.config.list_physical_devices('GPU') -for gpu_instance in physical_devices: - tf.config.experimental.set_memory_growth(gpu_instance, True) - -# Main -def _compute_heatmap(model, - img_array, - conv_layer_name, - pred_index): - """ - Helper function to compute the heatmap for a given convolutional layer. - """ - grad_model = tf.keras.models.Model( - [model.inputs], - [model.get_layer(conv_layer_name).output, model.output] - ) - - with tf.GradientTape() as tape: - conv_layer_output, preds = grad_model(img_array) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, conv_layer_output) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - - conv_layer_output = conv_layer_output[0] - heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] - heatmap = tf.squeeze(heatmap) - heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) - return heatmap - -def make_gradcam_heatmap(img_array, - model, - last_conv_layer_name, - second_last_conv_layer_name=None, - pred_index=None, - sensitivity_map=1.0): - """ - Function to compute the Grad-CAM heatmap for a specific class, given an input image. - """ - if pred_index is None: - preds = model.predict(img_array) - pred_index = tf.argmax(preds[0]) - - # Compute heatmap for the last convolutional layer - heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) - heatmap = heatmap ** sensitivity_map - - if second_last_conv_layer_name is not None: - # Compute heatmap for the second last convolutional layer - heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) - heatmap_second = heatmap_second ** sensitivity_map - - # Average the two heatmaps - heatmap = (heatmap + heatmap_second) / 2.0 - +import os +import glob +import numpy as np +import tensorflow as tf +# Other +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + +# Main +def _compute_heatmap(model, + img_array, + conv_layer_name, + pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, + model, + last_conv_layer_name, + second_last_conv_layer_name=None, + pred_index=None, + sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + return heatmap \ No newline at end of file diff --git a/Utils/Other.py b/Utils/Other.py index b36d78f..b882eb7 100644 --- a/Utils/Other.py +++ b/Utils/Other.py @@ -1,92 +1,92 @@ -from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from tabulate import tabulate -import numpy as np -import pickle -import gzip - -def save_list(history, filename, compress=True): - """Saves a list to a file. - - Args: - history: The list to save. - filename: The file to save the list to. - compress: Whether to gzip compress the file. Default is True. - - """ - if compress: - with gzip.open(filename, 'wb') as f: - pickle.dump(history, f) - else: - with open(filename, 'wb') as f: - pickle.dump(history, f) - - -def load_list(filename, compressed=True): - """Loads a list from a file. - - Args: - filename: The file to load from. - compressed: Whether the file is gzip compressed. Default is True. - - Returns: - The loaded list from the file. - """ - if compressed: - with gzip.open(filename, 'rb') as f: - return pickle.load(f) - else: - with open(filename, 'rb') as f: - return pickle.load(f) - - -def P_warning(msg): - """Prints a warning message to the console. - - Args: - msg (str): The warning message to print. - """ - print_Color_V2(f'Warning: {msg}') - -def evaluate_model_full(y_test, model_pred, model=None, x_test=None): - """Evaluates a machine learning model on a test set. - - Args: - x_test: Test set features. - y_test: Test set labels. - model_pred: Model predictions. - model: The model object. - - Returns: - None. Prints a table with accuracy, precision, recall and - F1 score. - """ - # Get the model predictions - if model_pred is None: - y_pred = model.predict(x_test) - else: - y_pred = model_pred - - # Convert one-hot encoded predictions and labels to label encoded form - y_pred_bin = np.argmax(y_pred, axis=1) - y_test_bin = np.argmax(y_test, axis=1) - - # Calculate normal metrics - accuracy = accuracy_score(y_test_bin, y_pred_bin) - - # Calculate weighted metrics - weighted_precision = precision_score( - y_test_bin, y_pred_bin, average='weighted') - weighted_f1 = f1_score(y_test_bin, y_pred_bin, average='weighted') - weighted_recall = recall_score(y_test_bin, y_pred_bin, average='weighted') - - # Prepare data for the table - metrics = [["Accuracy", round(accuracy * 100, 6)], - ["Precision", round(weighted_precision * 100, 6)], - ["F1 Score", round(weighted_f1 * 100, 6)], - ["Recall", round(weighted_recall * 100, 6)]] - - # Print the table - print(tabulate(metrics, headers=["Metric", "Value"], tablefmt="pretty")) - +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from tabulate import tabulate +import numpy as np +import pickle +import gzip + +def save_list(history, filename, compress=True): + """Saves a list to a file. + + Args: + history: The list to save. + filename: The file to save the list to. + compress: Whether to gzip compress the file. Default is True. + + """ + if compress: + with gzip.open(filename, 'wb') as f: + pickle.dump(history, f) + else: + with open(filename, 'wb') as f: + pickle.dump(history, f) + + +def load_list(filename, compressed=True): + """Loads a list from a file. + + Args: + filename: The file to load from. + compressed: Whether the file is gzip compressed. Default is True. + + Returns: + The loaded list from the file. + """ + if compressed: + with gzip.open(filename, 'rb') as f: + return pickle.load(f) + else: + with open(filename, 'rb') as f: + return pickle.load(f) + + +def P_warning(msg): + """Prints a warning message to the console. + + Args: + msg (str): The warning message to print. + """ + print_Color_V2(f'Warning: {msg}') + +def evaluate_model_full(y_test, model_pred, model=None, x_test=None): + """Evaluates a machine learning model on a test set. + + Args: + x_test: Test set features. + y_test: Test set labels. + model_pred: Model predictions. + model: The model object. + + Returns: + None. Prints a table with accuracy, precision, recall and + F1 score. + """ + # Get the model predictions + if model_pred is None: + y_pred = model.predict(x_test) + else: + y_pred = model_pred + + # Convert one-hot encoded predictions and labels to label encoded form + y_pred_bin = np.argmax(y_pred, axis=1) + y_test_bin = np.argmax(y_test, axis=1) + + # Calculate normal metrics + accuracy = accuracy_score(y_test_bin, y_pred_bin) + + # Calculate weighted metrics + weighted_precision = precision_score( + y_test_bin, y_pred_bin, average='weighted') + weighted_f1 = f1_score(y_test_bin, y_pred_bin, average='weighted') + weighted_recall = recall_score(y_test_bin, y_pred_bin, average='weighted') + + # Prepare data for the table + metrics = [["Accuracy", round(accuracy * 100, 6)], + ["Precision", round(weighted_precision * 100, 6)], + ["F1 Score", round(weighted_f1 * 100, 6)], + ["Recall", round(weighted_recall * 100, 6)]] + + # Print the table + print(tabulate(metrics, headers=["Metric", "Value"], tablefmt="pretty")) + diff --git a/Utils/README.md b/Utils/README.md index 2d85e26..16ffebb 100644 --- a/Utils/README.md +++ b/Utils/README.md @@ -1,15 +1,15 @@ -# Utils: - -## one_cycle_lr and lr_find (by 'benihime91') -- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) - - ### doc link: [1_README.md](docs\1_README.md) - -## Python-color-print-V2 and Python-color-print (by Me) -- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) - - ### doc link: [2_README.md](docs\2_README.md) -- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) - - ### doc link: [3_README.md](docs\3_README.md) - -## Grad_cam (by GPT-4 😁) - -## Other.py (by Me) +# Utils: + +## one_cycle_lr and lr_find (by 'benihime91') +- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) + - ### doc link: [1_README.md](docs\1_README.md) + +## Python-color-print-V2 and Python-color-print (by Me) +- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) + - ### doc link: [2_README.md](docs\2_README.md) +- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) + - ### doc link: [3_README.md](docs\3_README.md) + +## Grad_cam (by GPT-4 😁) + +## Other.py (by Me) diff --git a/backup/V4/TRAIN_LOG.txt b/backup/V4/TRAIN_LOG.txt index 824dc04..49713b9 100644 --- a/backup/V4/TRAIN_LOG.txt +++ b/backup/V4/TRAIN_LOG.txt @@ -1,6693 +1,6693 @@ -Training the model... - -Epoch: 1/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 55s 166ms/step - loss: 20.4375 - accuracy: 0.6216 - val_loss: 15.7907 - val_accuracy: 0.8157 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 10.5470 - accuracy: 0.7383 - val_loss: 6.2110 - val_accuracy: 0.8205 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 4.1127 - accuracy: 0.7842 - val_loss: 2.5556 - val_accuracy: 0.8702 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 1.8795 - accuracy: 0.8096 - val_loss: 1.2612 - val_accuracy: 0.8718 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 1.0468 - accuracy: 0.8398 - val_loss: 0.8444 - val_accuracy: 0.8686 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.7275 - accuracy: 0.8555 - val_loss: 0.6163 - val_accuracy: 0.8766 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.5332 - accuracy: 0.8926 - val_loss: 0.5743 - val_accuracy: 0.8878 -Epoch 8/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.4697 - accuracy: 0.8979 - val_loss: 0.5413 - val_accuracy: 0.8702 -Subset training done. -Improved model accuracy from 0 to 0.870192289352417. Saving model. -Improved model loss from inf to 0.5412302017211914. Saving model. -Time taken for epoch(FULL) 1: 386.46 sec -Time taken for epoch(SUBo) 1: 333.70 sec -<---------------------------------------|Epoch [1] END|---------------------------------------> - -Epoch: 2/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.5620 - accuracy: 0.8359 - val_loss: 0.5067 - val_accuracy: 0.8446 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.5338 - accuracy: 0.8403 - val_loss: 0.6622 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.5047 - accuracy: 0.8418 - val_loss: 0.3689 - val_accuracy: 0.8926 -Epoch 4/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.4192 - accuracy: 0.8638 - val_loss: 0.4566 - val_accuracy: 0.8686 -Epoch 5/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.4020 - accuracy: 0.8677 - val_loss: 0.3214 - val_accuracy: 0.8670 -Epoch 6/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3681 - accuracy: 0.8813 - val_loss: 0.3148 - val_accuracy: 0.9199 -Epoch 7/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3198 - accuracy: 0.8931 - val_loss: 0.2567 - val_accuracy: 0.9279 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2715 - accuracy: 0.9180 - val_loss: 0.2393 - val_accuracy: 0.9311 -Subset training done. -Improved model accuracy from 0.870192289352417 to 0.9310897588729858. Saving model. -Improved model loss from 0.5412302017211914 to 0.23925769329071045. Saving model. -Time taken for epoch(FULL) 2: 381.03 sec -Time taken for epoch(SUBo) 2: 325.77 sec -<---------------------------------------|Epoch [2] END|---------------------------------------> - -Epoch: 3/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 161ms/step - loss: 0.3640 - accuracy: 0.8696 - val_loss: 0.3126 - val_accuracy: 0.9247 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3588 - accuracy: 0.8735 - val_loss: 0.3768 - val_accuracy: 0.9295 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3830 - accuracy: 0.8730 - val_loss: 0.4670 - val_accuracy: 0.9391 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3658 - accuracy: 0.8887 - val_loss: 0.2308 - val_accuracy: 0.9359 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3717 - accuracy: 0.8779 - val_loss: 0.2747 - val_accuracy: 0.9199 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3136 - accuracy: 0.9028 - val_loss: 0.3153 - val_accuracy: 0.9022 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2696 - accuracy: 0.9136 - val_loss: 0.2452 - val_accuracy: 0.9247 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2407 - accuracy: 0.9243 - val_loss: 0.2541 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9310897588729858. Not saving model. -Model loss did not improve from 0.23925769329071045. Not saving model. -Time taken for epoch(FULL) 3: 376.52 sec -Time taken for epoch(SUBo) 3: 324.58 sec -<---------------------------------------|Epoch [3] END|---------------------------------------> - -Epoch: 4/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.3534 - accuracy: 0.8784 - val_loss: 0.2325 - val_accuracy: 0.9215 -Epoch 2/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3861 - accuracy: 0.8584 - val_loss: 0.4468 - val_accuracy: 0.9103 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3696 - accuracy: 0.8765 - val_loss: 0.4794 - val_accuracy: 0.9038 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3680 - accuracy: 0.8828 - val_loss: 0.2781 - val_accuracy: 0.9231 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2897 - accuracy: 0.9165 - val_loss: 0.2823 - val_accuracy: 0.9327 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2801 - accuracy: 0.9165 - val_loss: 0.2447 - val_accuracy: 0.9071 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2460 - accuracy: 0.9326 - val_loss: 0.2840 - val_accuracy: 0.9359 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1982 - accuracy: 0.9414 - val_loss: 0.2283 - val_accuracy: 0.9343 -Subset training done. -Improved model accuracy from 0.9310897588729858 to 0.9342948794364929. Saving model. -Improved model loss from 0.23925769329071045 to 0.22827185690402985. Saving model. -Time taken for epoch(FULL) 4: 379.86 sec -Time taken for epoch(SUBo) 4: 325.33 sec -<---------------------------------------|Epoch [4] END|---------------------------------------> - -Epoch: 5/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.3226 - accuracy: 0.8950 - val_loss: 0.3022 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3538 - accuracy: 0.8862 - val_loss: 0.3310 - val_accuracy: 0.9279 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3201 - accuracy: 0.8892 - val_loss: 0.2884 - val_accuracy: 0.9071 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3229 - accuracy: 0.9009 - val_loss: 0.5201 - val_accuracy: 0.7340 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3079 - accuracy: 0.8926 - val_loss: 0.2863 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2670 - accuracy: 0.9141 - val_loss: 0.2587 - val_accuracy: 0.9151 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2639 - accuracy: 0.9209 - val_loss: 0.2800 - val_accuracy: 0.9054 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1925 - accuracy: 0.9541 - val_loss: 0.2547 - val_accuracy: 0.9087 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 5: 375.38 sec -Time taken for epoch(SUBo) 5: 323.81 sec -<---------------------------------------|Epoch [5] END|---------------------------------------> - -Epoch: 6/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2908 - accuracy: 0.8994 - val_loss: 0.3886 - val_accuracy: 0.9151 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2973 - accuracy: 0.8984 - val_loss: 0.4025 - val_accuracy: 0.7917 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3093 - accuracy: 0.8945 - val_loss: 0.9113 - val_accuracy: 0.6907 -Epoch 4/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2903 - accuracy: 0.9048 - val_loss: 0.3253 - val_accuracy: 0.8766 -Epoch 5/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2965 - accuracy: 0.9131 - val_loss: 0.3971 - val_accuracy: 0.8798 -Epoch 6/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2341 - accuracy: 0.9238 - val_loss: 0.3240 - val_accuracy: 0.9071 -Epoch 7/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2202 - accuracy: 0.9316 - val_loss: 0.3072 - val_accuracy: 0.9151 -Epoch 8/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.1613 - accuracy: 0.9614 - val_loss: 0.3554 - val_accuracy: 0.9167 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 6: 377.12 sec -Time taken for epoch(SUBo) 6: 325.55 sec -<---------------------------------------|Epoch [6] END|---------------------------------------> - -Epoch: 7/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 161ms/step - loss: 0.2840 - accuracy: 0.9102 - val_loss: 0.3625 - val_accuracy: 0.8878 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3095 - accuracy: 0.9033 - val_loss: 0.3963 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3532 - accuracy: 0.8887 - val_loss: 0.2555 - val_accuracy: 0.9263 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3018 - accuracy: 0.8979 - val_loss: 0.2644 - val_accuracy: 0.9375 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3154 - accuracy: 0.9048 - val_loss: 0.4598 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2509 - accuracy: 0.9312 - val_loss: 0.2478 - val_accuracy: 0.9295 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1902 - accuracy: 0.9478 - val_loss: 0.2697 - val_accuracy: 0.9311 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1628 - accuracy: 0.9531 - val_loss: 0.2426 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 7: 376.09 sec -Time taken for epoch(SUBo) 7: 324.32 sec -<---------------------------------------|Epoch [7] END|---------------------------------------> - -Epoch: 8/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2691 - accuracy: 0.9106 - val_loss: 0.4805 - val_accuracy: 0.9071 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3014 - accuracy: 0.8994 - val_loss: 0.2715 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3387 - accuracy: 0.8818 - val_loss: 0.3345 - val_accuracy: 0.8542 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3069 - accuracy: 0.9072 - val_loss: 0.3671 - val_accuracy: 0.9359 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2874 - accuracy: 0.9058 - val_loss: 0.2579 - val_accuracy: 0.9343 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2255 - accuracy: 0.9399 - val_loss: 0.3501 - val_accuracy: 0.9375 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1835 - accuracy: 0.9492 - val_loss: 0.2757 - val_accuracy: 0.9407 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1739 - accuracy: 0.9492 - val_loss: 0.2712 - val_accuracy: 0.9391 -Subset training done. -Improved model accuracy from 0.9342948794364929 to 0.9391025900840759. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 8: 377.68 sec -Time taken for epoch(SUBo) 8: 324.30 sec -<---------------------------------------|Epoch [8] END|---------------------------------------> - -Epoch: 9/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2921 - accuracy: 0.9077 - val_loss: 0.3537 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2866 - accuracy: 0.9082 - val_loss: 0.3213 - val_accuracy: 0.9359 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2978 - accuracy: 0.8999 - val_loss: 0.3623 - val_accuracy: 0.9199 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2635 - accuracy: 0.9209 - val_loss: 0.4593 - val_accuracy: 0.8942 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2444 - accuracy: 0.9287 - val_loss: 0.3207 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2366 - accuracy: 0.9277 - val_loss: 0.3259 - val_accuracy: 0.9167 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1802 - accuracy: 0.9478 - val_loss: 0.3234 - val_accuracy: 0.9231 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9648 - val_loss: 0.2856 - val_accuracy: 0.9247 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 9: 373.86 sec -Time taken for epoch(SUBo) 9: 323.15 sec -<---------------------------------------|Epoch [9] END|---------------------------------------> - -Epoch: 10/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2630 - accuracy: 0.9165 - val_loss: 0.2739 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3113 - accuracy: 0.9082 - val_loss: 0.3775 - val_accuracy: 0.9054 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2991 - accuracy: 0.9102 - val_loss: 0.4075 - val_accuracy: 0.9247 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2560 - accuracy: 0.9365 - val_loss: 0.3893 - val_accuracy: 0.9103 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2622 - accuracy: 0.9360 - val_loss: 0.3810 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2561 - accuracy: 0.9360 - val_loss: 0.3800 - val_accuracy: 0.9215 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1804 - accuracy: 0.9561 - val_loss: 0.2602 - val_accuracy: 0.9295 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1792 - accuracy: 0.9565 - val_loss: 0.3396 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 10: 375.11 sec -Time taken for epoch(SUBo) 10: 323.65 sec -<---------------------------------------|Epoch [10] END|---------------------------------------> - -Epoch: 11/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2826 - accuracy: 0.9058 - val_loss: 0.2663 - val_accuracy: 0.9183 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3059 - accuracy: 0.9033 - val_loss: 0.3297 - val_accuracy: 0.9199 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3283 - accuracy: 0.9102 - val_loss: 0.3599 - val_accuracy: 0.9375 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2491 - accuracy: 0.9375 - val_loss: 0.3099 - val_accuracy: 0.9327 -Epoch 5/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.2357 - accuracy: 0.9336 - val_loss: 0.4078 - val_accuracy: 0.9167 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2435 - accuracy: 0.9365 - val_loss: 0.2847 - val_accuracy: 0.9359 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1802 - accuracy: 0.9575 - val_loss: 0.3534 - val_accuracy: 0.9295 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1446 - accuracy: 0.9644 - val_loss: 0.3434 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 11: 374.31 sec -Time taken for epoch(SUBo) 11: 322.89 sec -<---------------------------------------|Epoch [11] END|---------------------------------------> - -Epoch: 12/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2543 - accuracy: 0.9263 - val_loss: 0.4121 - val_accuracy: 0.9327 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2741 - accuracy: 0.9258 - val_loss: 0.3493 - val_accuracy: 0.9359 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2763 - accuracy: 0.9307 - val_loss: 0.3661 - val_accuracy: 0.9359 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2352 - accuracy: 0.9414 - val_loss: 0.3281 - val_accuracy: 0.9215 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2251 - accuracy: 0.9453 - val_loss: 0.2411 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1783 - accuracy: 0.9595 - val_loss: 0.3297 - val_accuracy: 0.9247 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1812 - accuracy: 0.9619 - val_loss: 0.2638 - val_accuracy: 0.9087 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9702 - val_loss: 0.2747 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 12: 375.91 sec -Time taken for epoch(SUBo) 12: 324.60 sec -<---------------------------------------|Epoch [12] END|---------------------------------------> - -Epoch: 13/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2384 - accuracy: 0.9326 - val_loss: 0.2895 - val_accuracy: 0.9231 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2609 - accuracy: 0.9287 - val_loss: 0.2950 - val_accuracy: 0.9119 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2872 - accuracy: 0.9277 - val_loss: 0.3571 - val_accuracy: 0.9087 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2855 - accuracy: 0.9229 - val_loss: 0.5538 - val_accuracy: 0.9087 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2314 - accuracy: 0.9478 - val_loss: 0.2693 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1893 - accuracy: 0.9546 - val_loss: 0.2341 - val_accuracy: 0.9343 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1685 - accuracy: 0.9600 - val_loss: 0.2727 - val_accuracy: 0.9439 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1422 - accuracy: 0.9736 - val_loss: 0.2968 - val_accuracy: 0.9407 -Subset training done. -Improved model accuracy from 0.9391025900840759 to 0.9407051205635071. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 13: 376.01 sec -Time taken for epoch(SUBo) 13: 323.00 sec -<---------------------------------------|Epoch [13] END|---------------------------------------> - -Epoch: 14/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 159ms/step - loss: 0.2536 - accuracy: 0.9341 - val_loss: 0.3728 - val_accuracy: 0.9295 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2549 - accuracy: 0.9272 - val_loss: 0.2704 - val_accuracy: 0.9279 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2345 - accuracy: 0.9419 - val_loss: 0.3342 - val_accuracy: 0.9327 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2225 - accuracy: 0.9541 - val_loss: 0.3081 - val_accuracy: 0.9151 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2233 - accuracy: 0.9443 - val_loss: 0.2983 - val_accuracy: 0.9263 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1875 - accuracy: 0.9521 - val_loss: 0.2882 - val_accuracy: 0.9327 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1461 - accuracy: 0.9673 - val_loss: 0.2289 - val_accuracy: 0.9359 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1285 - accuracy: 0.9717 - val_loss: 0.2355 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9407051205635071. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 14: 376.09 sec -Time taken for epoch(SUBo) 14: 324.46 sec -<---------------------------------------|Epoch [14] END|---------------------------------------> - -Epoch: 15/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 158ms/step - loss: 0.2348 - accuracy: 0.9341 - val_loss: 0.3880 - val_accuracy: 0.9263 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2625 - accuracy: 0.9224 - val_loss: 0.3617 - val_accuracy: 0.9327 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2578 - accuracy: 0.9292 - val_loss: 0.3288 - val_accuracy: 0.9263 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2543 - accuracy: 0.9302 - val_loss: 0.3120 - val_accuracy: 0.9038 -Epoch 5/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.3444 - accuracy: 0.9067 - val_loss: 0.2470 - val_accuracy: 0.9391 -Epoch 6/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.2173 - accuracy: 0.9424 - val_loss: 0.3219 - val_accuracy: 0.9343 -Epoch 7/8 -256/256 [==============================] - 39s 154ms/step - loss: 0.1908 - accuracy: 0.9526 - val_loss: 0.2278 - val_accuracy: 0.9407 -Epoch 8/8 -256/256 [==============================] - 39s 154ms/step - loss: 0.1584 - accuracy: 0.9600 - val_loss: 0.2384 - val_accuracy: 0.9439 -Subset training done. -Improved model accuracy from 0.9407051205635071 to 0.9439102411270142. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 15: 374.28 sec -Time taken for epoch(SUBo) 15: 321.58 sec -<---------------------------------------|Epoch [15] END|---------------------------------------> - -Epoch: 16/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.2960 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.3020 - accuracy: 0.9111 - val_loss: 0.3527 - val_accuracy: 0.8622 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2673 - accuracy: 0.9238 - val_loss: 0.5715 - val_accuracy: 0.7340 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2500 - accuracy: 0.9277 - val_loss: 0.5034 - val_accuracy: 0.7484 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2083 - accuracy: 0.9478 - val_loss: 0.2478 - val_accuracy: 0.9071 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1548 - accuracy: 0.9624 - val_loss: 0.2110 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Improved model loss from 0.22827185690402985 to 0.21101020276546478. Saving model. -Time taken for epoch(FULL) 16: 297.03 sec -Time taken for epoch(SUBo) 16: 244.35 sec -<---------------------------------------|Epoch [16] END|---------------------------------------> - -Epoch: 17/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.009500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.2628 - accuracy: 0.9282 - val_loss: 0.2316 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2760 - accuracy: 0.9209 - val_loss: 0.3350 - val_accuracy: 0.9279 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2708 - accuracy: 0.9189 - val_loss: 0.3418 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2240 - accuracy: 0.9419 - val_loss: 0.2829 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1962 - accuracy: 0.9526 - val_loss: 0.2832 - val_accuracy: 0.8926 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1605 - accuracy: 0.9609 - val_loss: 0.2716 - val_accuracy: 0.8974 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 17: 294.63 sec -Time taken for epoch(SUBo) 17: 243.78 sec -<---------------------------------------|Epoch [17] END|---------------------------------------> - -Epoch: 18/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.009000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2515 - accuracy: 0.9263 - val_loss: 0.2493 - val_accuracy: 0.9199 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3009 - accuracy: 0.9219 - val_loss: 0.3727 - val_accuracy: 0.8894 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2677 - accuracy: 0.9224 - val_loss: 0.3309 - val_accuracy: 0.9151 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.2292 - accuracy: 0.9395 - val_loss: 0.2943 - val_accuracy: 0.8910 -Epoch 5/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.1811 - accuracy: 0.9556 - val_loss: 0.2777 - val_accuracy: 0.9087 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1560 - accuracy: 0.9663 - val_loss: 0.2857 - val_accuracy: 0.9215 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 18: 293.64 sec -Time taken for epoch(SUBo) 18: 242.80 sec -<---------------------------------------|Epoch [18] END|---------------------------------------> - -Epoch: 19/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.008500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.2747 - accuracy: 0.9248 - val_loss: 0.2540 - val_accuracy: 0.9038 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.3029 - accuracy: 0.9082 - val_loss: 0.2379 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2496 - accuracy: 0.9297 - val_loss: 0.2431 - val_accuracy: 0.9103 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2807 - accuracy: 0.9087 - val_loss: 0.2517 - val_accuracy: 0.8958 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1925 - accuracy: 0.9463 - val_loss: 0.2512 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9639 - val_loss: 0.2388 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 19: 287.17 sec -Time taken for epoch(SUBo) 19: 237.22 sec -<---------------------------------------|Epoch [19] END|---------------------------------------> - -Epoch: 20/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.008000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2361 - accuracy: 0.9326 - val_loss: 0.3213 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2437 - accuracy: 0.9282 - val_loss: 0.3130 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2265 - accuracy: 0.9390 - val_loss: 0.7231 - val_accuracy: 0.5817 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2058 - accuracy: 0.9463 - val_loss: 0.2048 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1747 - accuracy: 0.9585 - val_loss: 0.2309 - val_accuracy: 0.9135 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1581 - accuracy: 0.9624 - val_loss: 0.2022 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Improved model loss from 0.21101020276546478 to 0.20221146941184998. Saving model. -Time taken for epoch(FULL) 20: 287.49 sec -Time taken for epoch(SUBo) 20: 236.52 sec -<---------------------------------------|Epoch [20] END|---------------------------------------> - -Epoch: 21/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.007500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2639 - accuracy: 0.9204 - val_loss: 0.3842 - val_accuracy: 0.8542 -Epoch 2/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.2602 - accuracy: 0.9224 - val_loss: 0.2024 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2491 - accuracy: 0.9204 - val_loss: 0.3014 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2034 - accuracy: 0.9521 - val_loss: 0.2709 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2075 - accuracy: 0.9429 - val_loss: 0.3214 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1429 - accuracy: 0.9648 - val_loss: 0.2890 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 21: 285.90 sec -Time taken for epoch(SUBo) 21: 236.69 sec -<---------------------------------------|Epoch [21] END|---------------------------------------> - -Epoch: 22/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.007000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2293 - accuracy: 0.9360 - val_loss: 0.1936 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2278 - accuracy: 0.9341 - val_loss: 0.2616 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2114 - accuracy: 0.9438 - val_loss: 0.2647 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2235 - accuracy: 0.9453 - val_loss: 0.2567 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1777 - accuracy: 0.9541 - val_loss: 0.2569 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1626 - accuracy: 0.9575 - val_loss: 0.2484 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 22: 285.94 sec -Time taken for epoch(SUBo) 22: 236.66 sec -<---------------------------------------|Epoch [22] END|---------------------------------------> - -Epoch: 23/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.006500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2132 - accuracy: 0.9385 - val_loss: 0.2144 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2413 - accuracy: 0.9360 - val_loss: 0.5426 - val_accuracy: 0.8750 -Epoch 3/6 -256/256 [==============================] - 38s 149ms/step - loss: 0.2458 - accuracy: 0.9375 - val_loss: 0.2533 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1869 - accuracy: 0.9453 - val_loss: 0.2258 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9663 - val_loss: 0.2642 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 38s 149ms/step - loss: 0.1227 - accuracy: 0.9746 - val_loss: 0.2471 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 23: 284.47 sec -Time taken for epoch(SUBo) 23: 235.20 sec -<---------------------------------------|Epoch [23] END|---------------------------------------> - -Epoch: 24/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.006000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2147 - accuracy: 0.9365 - val_loss: 0.2431 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2151 - accuracy: 0.9385 - val_loss: 0.2308 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2120 - accuracy: 0.9380 - val_loss: 0.2704 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1936 - accuracy: 0.9453 - val_loss: 0.2529 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1086 - accuracy: 0.9756 - val_loss: 0.1858 - val_accuracy: 0.9471 -Subset training done. -Improved model accuracy from 0.9439102411270142 to 0.9471153616905212. Saving model. -Improved model loss from 0.20221146941184998 to 0.1857679933309555. Saving model. -Time taken for epoch(FULL) 24: 288.85 sec -Time taken for epoch(SUBo) 24: 236.73 sec -<---------------------------------------|Epoch [24] END|---------------------------------------> - -Epoch: 25/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.005500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2106 - accuracy: 0.9414 - val_loss: 0.2085 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2304 - accuracy: 0.9326 - val_loss: 0.2498 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2059 - accuracy: 0.9482 - val_loss: 0.3972 - val_accuracy: 0.9247 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1980 - accuracy: 0.9458 - val_loss: 0.2653 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9731 - val_loss: 0.2222 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1402 - accuracy: 0.9604 - val_loss: 0.2944 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 25: 285.39 sec -Time taken for epoch(SUBo) 25: 236.55 sec -<---------------------------------------|Epoch [25] END|---------------------------------------> - -Epoch: 26/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.005000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2292 - accuracy: 0.9341 - val_loss: 0.2645 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2017 - accuracy: 0.9414 - val_loss: 0.2456 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2125 - accuracy: 0.9341 - val_loss: 0.3309 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1715 - accuracy: 0.9536 - val_loss: 0.2653 - val_accuracy: 0.9183 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9658 - val_loss: 0.2156 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9741 - val_loss: 0.2134 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 26: 285.12 sec -Time taken for epoch(SUBo) 26: 236.07 sec -<---------------------------------------|Epoch [26] END|---------------------------------------> - -Epoch: 27/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.004500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1868 - accuracy: 0.9463 - val_loss: 0.1853 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2043 - accuracy: 0.9351 - val_loss: 0.3479 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1911 - accuracy: 0.9453 - val_loss: 0.2130 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1510 - accuracy: 0.9600 - val_loss: 0.2097 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1655 - accuracy: 0.9561 - val_loss: 0.1885 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1346 - accuracy: 0.9692 - val_loss: 0.1939 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 27: 285.71 sec -Time taken for epoch(SUBo) 27: 236.48 sec -<---------------------------------------|Epoch [27] END|---------------------------------------> - -Epoch: 28/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.004000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2180 - accuracy: 0.9360 - val_loss: 0.1893 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2060 - accuracy: 0.9385 - val_loss: 0.1826 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1867 - accuracy: 0.9448 - val_loss: 0.1701 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1611 - accuracy: 0.9614 - val_loss: 0.1821 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1444 - accuracy: 0.9609 - val_loss: 0.1652 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1409 - accuracy: 0.9644 - val_loss: 0.1546 - val_accuracy: 0.9567 -Subset training done. -Improved model accuracy from 0.9471153616905212 to 0.9567307829856873. Saving model. -Improved model loss from 0.1857679933309555 to 0.15460731089115143. Saving model. -Time taken for epoch(FULL) 28: 288.65 sec -Time taken for epoch(SUBo) 28: 236.43 sec -<---------------------------------------|Epoch [28] END|---------------------------------------> - -Epoch: 29/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.003500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1936 - accuracy: 0.9404 - val_loss: 0.1560 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1892 - accuracy: 0.9390 - val_loss: 0.1654 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1752 - accuracy: 0.9541 - val_loss: 0.2738 - val_accuracy: 0.8926 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1570 - accuracy: 0.9561 - val_loss: 0.1721 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1441 - accuracy: 0.9639 - val_loss: 0.1639 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1111 - accuracy: 0.9692 - val_loss: 0.1661 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 29: 285.51 sec -Time taken for epoch(SUBo) 29: 236.35 sec -<---------------------------------------|Epoch [29] END|---------------------------------------> - -Epoch: 30/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.003000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1650 - accuracy: 0.9531 - val_loss: 0.1881 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1823 - accuracy: 0.9468 - val_loss: 0.2431 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1812 - accuracy: 0.9473 - val_loss: 0.1803 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1608 - accuracy: 0.9546 - val_loss: 0.1606 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9609 - val_loss: 0.1624 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1155 - accuracy: 0.9702 - val_loss: 0.1665 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 30: 285.48 sec -Time taken for epoch(SUBo) 30: 236.40 sec -<---------------------------------------|Epoch [30] END|---------------------------------------> - -Epoch: 31/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.002500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1981 - accuracy: 0.9370 - val_loss: 0.1560 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1661 - accuracy: 0.9482 - val_loss: 0.1612 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1624 - accuracy: 0.9517 - val_loss: 0.1743 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1685 - accuracy: 0.9517 - val_loss: 0.1903 - val_accuracy: 0.9247 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1254 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9231 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9707 - val_loss: 0.1807 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 31: 285.66 sec -Time taken for epoch(SUBo) 31: 236.69 sec -<---------------------------------------|Epoch [31] END|---------------------------------------> - -Epoch: 32/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.002000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1669 - accuracy: 0.9502 - val_loss: 0.1911 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1540 - accuracy: 0.9531 - val_loss: 0.1633 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9624 - val_loss: 0.1597 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1643 - accuracy: 0.9551 - val_loss: 0.1712 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9585 - val_loss: 0.1951 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1076 - accuracy: 0.9658 - val_loss: 0.1953 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 32: 285.69 sec -Time taken for epoch(SUBo) 32: 236.38 sec -<---------------------------------------|Epoch [32] END|---------------------------------------> - -Epoch: 33/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1745 - accuracy: 0.9463 - val_loss: 0.1852 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1641 - accuracy: 0.9512 - val_loss: 0.1889 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1578 - accuracy: 0.9512 - val_loss: 0.1950 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1493 - accuracy: 0.9507 - val_loss: 0.1669 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.1736 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1185 - accuracy: 0.9658 - val_loss: 0.1680 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 33: 286.31 sec -Time taken for epoch(SUBo) 33: 236.60 sec -<---------------------------------------|Epoch [33] END|---------------------------------------> - -Epoch: 34/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1615 - accuracy: 0.9521 - val_loss: 0.1627 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1649 - accuracy: 0.9521 - val_loss: 0.2083 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1949 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1419 - accuracy: 0.9517 - val_loss: 0.1563 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1317 - accuracy: 0.9565 - val_loss: 0.1606 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1158 - accuracy: 0.9688 - val_loss: 0.1512 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Improved model loss from 0.15460731089115143 to 0.15118563175201416. Saving model. -Time taken for epoch(FULL) 34: 287.71 sec -Time taken for epoch(SUBo) 34: 236.71 sec -<---------------------------------------|Epoch [34] END|---------------------------------------> - -Epoch: 35/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1739 - accuracy: 0.9443 - val_loss: 0.1441 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2022 - accuracy: 0.9336 - val_loss: 0.1491 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1754 - accuracy: 0.9458 - val_loss: 0.1782 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1629 - accuracy: 0.9458 - val_loss: 0.1656 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1582 - accuracy: 0.9546 - val_loss: 0.1640 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9531 - val_loss: 0.1650 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 35: 287.73 sec -Time taken for epoch(SUBo) 35: 237.55 sec -<---------------------------------------|Epoch [35] END|---------------------------------------> - -Epoch: 36/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9526 - val_loss: 0.1498 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1602 - accuracy: 0.9468 - val_loss: 0.1686 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9521 - val_loss: 0.1585 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1683 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9604 - val_loss: 0.1843 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1206 - accuracy: 0.9644 - val_loss: 0.1951 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 36: 287.50 sec -Time taken for epoch(SUBo) 36: 237.39 sec -<---------------------------------------|Epoch [36] END|---------------------------------------> - -Epoch: 37/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1843 - accuracy: 0.9414 - val_loss: 0.1578 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1683 - accuracy: 0.9497 - val_loss: 0.1731 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1612 - accuracy: 0.9463 - val_loss: 0.2032 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1507 - accuracy: 0.9521 - val_loss: 0.1985 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9590 - val_loss: 0.1618 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9595 - val_loss: 0.1653 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 37: 287.80 sec -Time taken for epoch(SUBo) 37: 237.45 sec -<---------------------------------------|Epoch [37] END|---------------------------------------> - -Epoch: 38/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1649 - accuracy: 0.9487 - val_loss: 0.1677 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1775 - accuracy: 0.9438 - val_loss: 0.1582 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1564 - accuracy: 0.9526 - val_loss: 0.1516 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1513 - accuracy: 0.9541 - val_loss: 0.1526 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1408 - accuracy: 0.9595 - val_loss: 0.1522 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1186 - accuracy: 0.9634 - val_loss: 0.1668 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 38: 287.81 sec -Time taken for epoch(SUBo) 38: 237.65 sec -<---------------------------------------|Epoch [38] END|---------------------------------------> - -Epoch: 39/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1748 - accuracy: 0.9414 - val_loss: 0.1468 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9521 - val_loss: 0.1940 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1527 - accuracy: 0.9536 - val_loss: 0.1679 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1440 - accuracy: 0.9521 - val_loss: 0.2192 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1304 - accuracy: 0.9570 - val_loss: 0.1655 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1088 - accuracy: 0.9697 - val_loss: 0.1865 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 39: 288.44 sec -Time taken for epoch(SUBo) 39: 237.93 sec -<---------------------------------------|Epoch [39] END|---------------------------------------> - -Epoch: 40/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1613 - accuracy: 0.9502 - val_loss: 0.1476 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1465 - accuracy: 0.9590 - val_loss: 0.1613 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1391 - accuracy: 0.9609 - val_loss: 0.1533 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1231 - accuracy: 0.9648 - val_loss: 0.1602 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1393 - accuracy: 0.9609 - val_loss: 0.1537 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1065 - accuracy: 0.9727 - val_loss: 0.1562 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 40: 287.78 sec -Time taken for epoch(SUBo) 40: 237.78 sec -<---------------------------------------|Epoch [40] END|---------------------------------------> - -Epoch: 41/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1631 - accuracy: 0.9478 - val_loss: 0.1572 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1542 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1441 - accuracy: 0.9531 - val_loss: 0.1653 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9614 - val_loss: 0.1968 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1599 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1870 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 41: 287.23 sec -Time taken for epoch(SUBo) 41: 237.14 sec -<---------------------------------------|Epoch [41] END|---------------------------------------> - -Epoch: 42/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1298 - accuracy: 0.9531 - val_loss: 0.2101 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9565 - val_loss: 0.1721 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1334 - accuracy: 0.9614 - val_loss: 0.1705 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9678 - val_loss: 0.1819 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1071 - accuracy: 0.9678 - val_loss: 0.1882 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0998 - accuracy: 0.9712 - val_loss: 0.2143 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 42: 287.96 sec -Time taken for epoch(SUBo) 42: 237.29 sec -<---------------------------------------|Epoch [42] END|---------------------------------------> - -Epoch: 43/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1517 - accuracy: 0.9556 - val_loss: 0.1814 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.2212 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2157 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2624 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1266 - accuracy: 0.9648 - val_loss: 0.2113 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2185 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 43: 288.58 sec -Time taken for epoch(SUBo) 43: 237.71 sec -<---------------------------------------|Epoch [43] END|---------------------------------------> - -Epoch: 44/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1549 - accuracy: 0.9507 - val_loss: 0.1907 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1406 - accuracy: 0.9561 - val_loss: 0.1945 - val_accuracy: 0.9279 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9556 - val_loss: 0.2094 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1300 - accuracy: 0.9619 - val_loss: 0.2000 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1146 - accuracy: 0.9678 - val_loss: 0.2591 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1207 - accuracy: 0.9648 - val_loss: 0.2343 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 44: 288.18 sec -Time taken for epoch(SUBo) 44: 237.53 sec -<---------------------------------------|Epoch [44] END|---------------------------------------> - -Epoch: 45/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1691 - accuracy: 0.9507 - val_loss: 0.1829 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9570 - val_loss: 0.1635 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1363 - accuracy: 0.9609 - val_loss: 0.2010 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1235 - accuracy: 0.9624 - val_loss: 0.1995 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9600 - val_loss: 0.2820 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1434 - accuracy: 0.9512 - val_loss: 0.2766 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 45: 288.43 sec -Time taken for epoch(SUBo) 45: 237.92 sec -<---------------------------------------|Epoch [45] END|---------------------------------------> - -Epoch: 46/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1684 - accuracy: 0.9468 - val_loss: 0.3024 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1606 - accuracy: 0.9478 - val_loss: 0.3133 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1545 - accuracy: 0.9585 - val_loss: 0.2165 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1639 - accuracy: 0.9468 - val_loss: 0.2465 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.2787 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9551 - val_loss: 0.2559 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 46: 288.00 sec -Time taken for epoch(SUBo) 46: 237.42 sec -<---------------------------------------|Epoch [46] END|---------------------------------------> - -Epoch: 47/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1874 - accuracy: 0.9414 - val_loss: 0.2024 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1816 - accuracy: 0.9487 - val_loss: 0.2076 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1674 - accuracy: 0.9434 - val_loss: 0.3245 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1442 - accuracy: 0.9604 - val_loss: 0.2564 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1221 - accuracy: 0.9609 - val_loss: 0.3057 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1317 - accuracy: 0.9556 - val_loss: 0.2604 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 47: 287.69 sec -Time taken for epoch(SUBo) 47: 236.59 sec -<---------------------------------------|Epoch [47] END|---------------------------------------> - -Epoch: 48/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1541 - accuracy: 0.9453 - val_loss: 0.2779 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1480 - accuracy: 0.9526 - val_loss: 0.2490 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1341 - accuracy: 0.9614 - val_loss: 0.2237 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1366 - accuracy: 0.9570 - val_loss: 0.2314 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2416 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1106 - accuracy: 0.9644 - val_loss: 0.2330 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 48: 286.84 sec -Time taken for epoch(SUBo) 48: 236.62 sec -<---------------------------------------|Epoch [48] END|---------------------------------------> - -Epoch: 49/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1551 - accuracy: 0.9561 - val_loss: 0.2252 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9570 - val_loss: 0.2131 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1401 - accuracy: 0.9580 - val_loss: 0.1908 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1271 - accuracy: 0.9639 - val_loss: 0.2179 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1260 - accuracy: 0.9634 - val_loss: 0.2022 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1087 - accuracy: 0.9717 - val_loss: 0.1932 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 49: 286.33 sec -Time taken for epoch(SUBo) 49: 236.60 sec -<---------------------------------------|Epoch [49] END|---------------------------------------> - -Epoch: 50/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1449 - accuracy: 0.9521 - val_loss: 0.1748 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1448 - accuracy: 0.9507 - val_loss: 0.2003 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9521 - val_loss: 0.2190 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9390 - val_loss: 0.2207 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9521 - val_loss: 0.2131 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1572 - accuracy: 0.9478 - val_loss: 0.2142 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 50: 286.59 sec -Time taken for epoch(SUBo) 50: 236.86 sec -<---------------------------------------|Epoch [50] END|---------------------------------------> - -Epoch: 51/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1601 - accuracy: 0.9497 - val_loss: 0.1783 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1519 - accuracy: 0.9517 - val_loss: 0.2485 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1687 - accuracy: 0.9521 - val_loss: 0.2295 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1445 - accuracy: 0.9600 - val_loss: 0.2580 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1283 - accuracy: 0.9619 - val_loss: 0.2596 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1248 - accuracy: 0.9624 - val_loss: 0.2709 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 51: 286.23 sec -Time taken for epoch(SUBo) 51: 236.38 sec -<---------------------------------------|Epoch [51] END|---------------------------------------> - -Epoch: 52/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1478 - accuracy: 0.9512 - val_loss: 0.2317 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1364 - accuracy: 0.9614 - val_loss: 0.2805 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9634 - val_loss: 0.2886 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2800 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1081 - accuracy: 0.9712 - val_loss: 0.2406 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9702 - val_loss: 0.2587 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 52: 286.99 sec -Time taken for epoch(SUBo) 52: 236.83 sec -<---------------------------------------|Epoch [52] END|---------------------------------------> - -Epoch: 53/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2206 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9468 - val_loss: 0.2399 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1522 - accuracy: 0.9546 - val_loss: 0.2213 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9546 - val_loss: 0.1943 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1258 - accuracy: 0.9580 - val_loss: 0.1851 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9541 - val_loss: 0.1898 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 53: 287.29 sec -Time taken for epoch(SUBo) 53: 237.19 sec -<---------------------------------------|Epoch [53] END|---------------------------------------> - -Epoch: 54/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1649 - accuracy: 0.9429 - val_loss: 0.2123 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1791 - accuracy: 0.9424 - val_loss: 0.2041 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1739 - accuracy: 0.9429 - val_loss: 0.2438 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1467 - accuracy: 0.9521 - val_loss: 0.2370 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1384 - accuracy: 0.9541 - val_loss: 0.3072 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1439 - accuracy: 0.9580 - val_loss: 0.2901 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 54: 287.00 sec -Time taken for epoch(SUBo) 54: 236.97 sec -<---------------------------------------|Epoch [54] END|---------------------------------------> - -Epoch: 55/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1734 - accuracy: 0.9438 - val_loss: 0.2456 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1551 - accuracy: 0.9512 - val_loss: 0.2227 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9468 - val_loss: 0.2150 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1365 - accuracy: 0.9600 - val_loss: 0.1964 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9595 - val_loss: 0.2038 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.2228 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 55: 286.51 sec -Time taken for epoch(SUBo) 55: 236.75 sec -<---------------------------------------|Epoch [55] END|---------------------------------------> - -Epoch: 56/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1372 - accuracy: 0.9575 - val_loss: 0.2215 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1534 - accuracy: 0.9541 - val_loss: 0.2516 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9629 - val_loss: 0.2329 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9673 - val_loss: 0.2124 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1028 - accuracy: 0.9727 - val_loss: 0.2299 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0982 - accuracy: 0.9736 - val_loss: 0.2280 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 56: 286.73 sec -Time taken for epoch(SUBo) 56: 237.12 sec -<---------------------------------------|Epoch [56] END|---------------------------------------> - -Epoch: 57/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1279 - accuracy: 0.9604 - val_loss: 0.1954 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9590 - val_loss: 0.2062 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1403 - accuracy: 0.9580 - val_loss: 0.1679 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1308 - accuracy: 0.9570 - val_loss: 0.1776 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1117 - accuracy: 0.9648 - val_loss: 0.1890 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1019 - accuracy: 0.9717 - val_loss: 0.1922 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 57: 286.14 sec -Time taken for epoch(SUBo) 57: 235.92 sec -<---------------------------------------|Epoch [57] END|---------------------------------------> - -Epoch: 58/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1579 - accuracy: 0.9468 - val_loss: 0.1934 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1771 - accuracy: 0.9409 - val_loss: 0.1981 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1471 - accuracy: 0.9561 - val_loss: 0.2460 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9595 - val_loss: 0.1832 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9536 - val_loss: 0.1711 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1317 - accuracy: 0.9609 - val_loss: 0.1742 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 58: 287.08 sec -Time taken for epoch(SUBo) 58: 236.57 sec -<---------------------------------------|Epoch [58] END|---------------------------------------> - -Epoch: 59/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1481 - accuracy: 0.9551 - val_loss: 0.1874 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1438 - accuracy: 0.9546 - val_loss: 0.1799 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1512 - accuracy: 0.9575 - val_loss: 0.1774 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1369 - accuracy: 0.9595 - val_loss: 0.1793 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1269 - accuracy: 0.9663 - val_loss: 0.1713 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9688 - val_loss: 0.1879 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 59: 286.52 sec -Time taken for epoch(SUBo) 59: 237.12 sec -<---------------------------------------|Epoch [59] END|---------------------------------------> - -Epoch: 60/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9531 - val_loss: 0.1852 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1386 - accuracy: 0.9575 - val_loss: 0.1995 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9663 - val_loss: 0.2111 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1169 - accuracy: 0.9663 - val_loss: 0.2195 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1004 - accuracy: 0.9717 - val_loss: 0.2351 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9668 - val_loss: 0.2677 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 60: 287.80 sec -Time taken for epoch(SUBo) 60: 237.20 sec -<---------------------------------------|Epoch [60] END|---------------------------------------> - -Epoch: 61/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1434 - accuracy: 0.9551 - val_loss: 0.2024 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1346 - accuracy: 0.9604 - val_loss: 0.2110 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1218 - accuracy: 0.9644 - val_loss: 0.1917 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9629 - val_loss: 0.2180 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1204 - accuracy: 0.9639 - val_loss: 0.1932 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1012 - accuracy: 0.9683 - val_loss: 0.1964 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 61: 288.27 sec -Time taken for epoch(SUBo) 61: 237.72 sec -<---------------------------------------|Epoch [61] END|---------------------------------------> - -Epoch: 62/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1389 - accuracy: 0.9575 - val_loss: 0.2335 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1295 - accuracy: 0.9561 - val_loss: 0.2828 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1223 - accuracy: 0.9619 - val_loss: 0.2642 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9673 - val_loss: 0.2734 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.2583 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1019 - accuracy: 0.9707 - val_loss: 0.2563 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 62: 288.25 sec -Time taken for epoch(SUBo) 62: 237.52 sec -<---------------------------------------|Epoch [62] END|---------------------------------------> - -Epoch: 63/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1588 - accuracy: 0.9517 - val_loss: 0.2404 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1392 - accuracy: 0.9624 - val_loss: 0.1892 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.1851 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9634 - val_loss: 0.1914 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1298 - accuracy: 0.9619 - val_loss: 0.2004 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9673 - val_loss: 0.1989 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 63: 288.35 sec -Time taken for epoch(SUBo) 63: 237.48 sec -<---------------------------------------|Epoch [63] END|---------------------------------------> - -Epoch: 64/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1376 - accuracy: 0.9556 - val_loss: 0.1802 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1370 - accuracy: 0.9575 - val_loss: 0.2342 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1335 - accuracy: 0.9604 - val_loss: 0.1916 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1462 - accuracy: 0.9580 - val_loss: 0.1591 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1061 - accuracy: 0.9663 - val_loss: 0.2386 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9688 - val_loss: 0.2423 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 64: 288.62 sec -Time taken for epoch(SUBo) 64: 237.68 sec -<---------------------------------------|Epoch [64] END|---------------------------------------> - -Epoch: 65/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1365 - accuracy: 0.9556 - val_loss: 0.2579 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1324 - accuracy: 0.9595 - val_loss: 0.2196 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9619 - val_loss: 0.2640 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1136 - accuracy: 0.9663 - val_loss: 0.2262 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1052 - accuracy: 0.9692 - val_loss: 0.2272 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0993 - accuracy: 0.9697 - val_loss: 0.2402 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 65: 288.68 sec -Time taken for epoch(SUBo) 65: 238.01 sec -<---------------------------------------|Epoch [65] END|---------------------------------------> - -Epoch: 66/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9590 - val_loss: 0.2096 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1272 - accuracy: 0.9604 - val_loss: 0.2505 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1286 - accuracy: 0.9561 - val_loss: 0.2210 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9683 - val_loss: 0.1834 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1793 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1017 - accuracy: 0.9697 - val_loss: 0.2070 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 66: 288.12 sec -Time taken for epoch(SUBo) 66: 237.92 sec -<---------------------------------------|Epoch [66] END|---------------------------------------> - -Epoch: 67/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1517 - accuracy: 0.9565 - val_loss: 0.1927 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1471 - accuracy: 0.9502 - val_loss: 0.2064 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2286 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1262 - accuracy: 0.9619 - val_loss: 0.1877 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1157 - accuracy: 0.9639 - val_loss: 0.1992 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9658 - val_loss: 0.1889 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 67: 288.09 sec -Time taken for epoch(SUBo) 67: 237.40 sec -<---------------------------------------|Epoch [67] END|---------------------------------------> - -Epoch: 68/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1311 - accuracy: 0.9556 - val_loss: 0.1958 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1119 - accuracy: 0.9644 - val_loss: 0.2010 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1263 - accuracy: 0.9595 - val_loss: 0.1595 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9595 - val_loss: 0.1492 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1132 - accuracy: 0.9639 - val_loss: 0.1464 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1003 - accuracy: 0.9712 - val_loss: 0.1529 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 68: 288.54 sec -Time taken for epoch(SUBo) 68: 237.57 sec -<---------------------------------------|Epoch [68] END|---------------------------------------> - -Epoch: 69/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1554 - accuracy: 0.9546 - val_loss: 0.1697 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1428 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1287 - accuracy: 0.9629 - val_loss: 0.2158 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1152 - accuracy: 0.9634 - val_loss: 0.1788 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1029 - accuracy: 0.9697 - val_loss: 0.1732 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0991 - accuracy: 0.9722 - val_loss: 0.1837 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 69: 287.94 sec -Time taken for epoch(SUBo) 69: 237.46 sec -<---------------------------------------|Epoch [69] END|---------------------------------------> - -Epoch: 70/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1386 - accuracy: 0.9648 - val_loss: 0.1742 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.2681 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1782 - accuracy: 0.9482 - val_loss: 0.3058 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1468 - accuracy: 0.9526 - val_loss: 0.2156 - val_accuracy: 0.9327 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1217 - accuracy: 0.9634 - val_loss: 0.1891 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9668 - val_loss: 0.1983 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 70: 288.24 sec -Time taken for epoch(SUBo) 70: 237.80 sec -<---------------------------------------|Epoch [70] END|---------------------------------------> - -Epoch: 71/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.1688 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1528 - accuracy: 0.9546 - val_loss: 0.1514 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9609 - val_loss: 0.1770 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9585 - val_loss: 0.1579 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1195 - accuracy: 0.9653 - val_loss: 0.1543 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1538 - val_accuracy: 0.9599 -Subset training done. -Improved model accuracy from 0.9567307829856873 to 0.9599359035491943. Saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 71: 289.74 sec -Time taken for epoch(SUBo) 71: 237.66 sec -<---------------------------------------|Epoch [71] END|---------------------------------------> - -Epoch: 72/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1505 - accuracy: 0.9521 - val_loss: 0.1529 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1589 - accuracy: 0.9512 - val_loss: 0.1426 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1484 - accuracy: 0.9546 - val_loss: 0.1592 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9658 - val_loss: 0.1791 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1154 - accuracy: 0.9673 - val_loss: 0.1634 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 72: 288.91 sec -Time taken for epoch(SUBo) 72: 237.07 sec -<---------------------------------------|Epoch [72] END|---------------------------------------> - -Epoch: 73/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1304 - accuracy: 0.9609 - val_loss: 0.1894 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1949 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9526 - val_loss: 0.2177 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.2006 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1074 - accuracy: 0.9746 - val_loss: 0.2530 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9692 - val_loss: 0.2516 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 73: 286.60 sec -Time taken for epoch(SUBo) 73: 236.85 sec -<---------------------------------------|Epoch [73] END|---------------------------------------> - -Epoch: 74/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1103 - accuracy: 0.9653 - val_loss: 0.2006 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1242 - accuracy: 0.9600 - val_loss: 0.2702 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1351 - accuracy: 0.9580 - val_loss: 0.2475 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9731 - val_loss: 0.2133 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2043 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.0768 - accuracy: 0.9780 - val_loss: 0.2014 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 74: 287.09 sec -Time taken for epoch(SUBo) 74: 237.29 sec -<---------------------------------------|Epoch [74] END|---------------------------------------> - -Epoch: 75/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1430 - accuracy: 0.9546 - val_loss: 0.2063 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1306 - accuracy: 0.9619 - val_loss: 0.1984 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1205 - accuracy: 0.9663 - val_loss: 0.1844 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1186 - accuracy: 0.9653 - val_loss: 0.1739 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9727 - val_loss: 0.1955 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0930 - accuracy: 0.9731 - val_loss: 0.1780 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 75: 287.36 sec -Time taken for epoch(SUBo) 75: 237.36 sec -<---------------------------------------|Epoch [75] END|---------------------------------------> - -Epoch: 76/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1332 - accuracy: 0.9561 - val_loss: 0.1757 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1358 - accuracy: 0.9590 - val_loss: 0.1649 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1475 - accuracy: 0.9546 - val_loss: 0.1689 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9570 - val_loss: 0.1557 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1127 - accuracy: 0.9619 - val_loss: 0.1633 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9717 - val_loss: 0.1716 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 76: 286.87 sec -Time taken for epoch(SUBo) 76: 237.21 sec -<---------------------------------------|Epoch [76] END|---------------------------------------> - -Epoch: 77/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1613 - accuracy: 0.9429 - val_loss: 0.1702 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1557 - accuracy: 0.9463 - val_loss: 0.1623 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9546 - val_loss: 0.2084 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1245 - accuracy: 0.9619 - val_loss: 0.2221 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1156 - accuracy: 0.9624 - val_loss: 0.2435 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9683 - val_loss: 0.2508 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 77: 286.93 sec -Time taken for epoch(SUBo) 77: 236.81 sec -<---------------------------------------|Epoch [77] END|---------------------------------------> - -Epoch: 78/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1258 - accuracy: 0.9609 - val_loss: 0.1880 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1473 - accuracy: 0.9507 - val_loss: 0.1763 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1170 - accuracy: 0.9658 - val_loss: 0.2302 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9551 - val_loss: 0.1573 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1236 - accuracy: 0.9585 - val_loss: 0.1819 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1150 - accuracy: 0.9639 - val_loss: 0.1925 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 78: 287.03 sec -Time taken for epoch(SUBo) 78: 237.13 sec -<---------------------------------------|Epoch [78] END|---------------------------------------> - -Epoch: 79/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1394 - accuracy: 0.9570 - val_loss: 0.1949 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1345 - accuracy: 0.9604 - val_loss: 0.2434 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.2313 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1145 - accuracy: 0.9648 - val_loss: 0.2336 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1077 - accuracy: 0.9707 - val_loss: 0.2261 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1020 - accuracy: 0.9688 - val_loss: 0.2249 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 79: 286.93 sec -Time taken for epoch(SUBo) 79: 237.31 sec -<---------------------------------------|Epoch [79] END|---------------------------------------> - -Epoch: 80/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9512 - val_loss: 0.2335 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2401 - val_accuracy: 0.9183 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2689 - accuracy: 0.9048 - val_loss: 0.4998 - val_accuracy: 0.7821 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.2924 - accuracy: 0.8955 - val_loss: 0.4549 - val_accuracy: 0.8782 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2427 - accuracy: 0.9136 - val_loss: 0.3899 - val_accuracy: 0.8830 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2071 - accuracy: 0.9292 - val_loss: 0.3938 - val_accuracy: 0.8830 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 80: 287.37 sec -Time taken for epoch(SUBo) 80: 237.50 sec -<---------------------------------------|Epoch [80] END|---------------------------------------> - -Epoch: 81/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2117 - accuracy: 0.9272 - val_loss: 0.3888 - val_accuracy: 0.8942 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.2039 - accuracy: 0.9326 - val_loss: 0.4718 - val_accuracy: 0.9038 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1797 - accuracy: 0.9424 - val_loss: 0.4449 - val_accuracy: 0.9087 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1627 - accuracy: 0.9512 - val_loss: 0.2830 - val_accuracy: 0.9151 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1495 - accuracy: 0.9565 - val_loss: 0.3565 - val_accuracy: 0.9167 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9541 - val_loss: 0.3372 - val_accuracy: 0.9199 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 81: 287.21 sec -Time taken for epoch(SUBo) 81: 237.47 sec -<---------------------------------------|Epoch [81] END|---------------------------------------> - -Epoch: 82/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1753 - accuracy: 0.9424 - val_loss: 0.3639 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1803 - accuracy: 0.9429 - val_loss: 0.3132 - val_accuracy: 0.9215 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1485 - accuracy: 0.9565 - val_loss: 0.2975 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.3335 - val_accuracy: 0.9247 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9561 - val_loss: 0.2650 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9653 - val_loss: 0.2362 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 82: 286.96 sec -Time taken for epoch(SUBo) 82: 236.90 sec -<---------------------------------------|Epoch [82] END|---------------------------------------> - -Epoch: 83/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1623 - accuracy: 0.9492 - val_loss: 0.2152 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1599 - accuracy: 0.9502 - val_loss: 0.2598 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1508 - accuracy: 0.9609 - val_loss: 0.2304 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9517 - val_loss: 0.2164 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9624 - val_loss: 0.2169 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1250 - accuracy: 0.9595 - val_loss: 0.2147 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 83: 287.52 sec -Time taken for epoch(SUBo) 83: 237.43 sec -<---------------------------------------|Epoch [83] END|---------------------------------------> - -Epoch: 84/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9595 - val_loss: 0.2386 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.1926 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1437 - accuracy: 0.9526 - val_loss: 0.2082 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1389 - accuracy: 0.9556 - val_loss: 0.2051 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1211 - accuracy: 0.9634 - val_loss: 0.1852 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9702 - val_loss: 0.1848 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 84: 286.91 sec -Time taken for epoch(SUBo) 84: 237.24 sec -<---------------------------------------|Epoch [84] END|---------------------------------------> - -Epoch: 85/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1612 - accuracy: 0.9478 - val_loss: 0.2066 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1647 - accuracy: 0.9448 - val_loss: 0.1899 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1606 - accuracy: 0.9448 - val_loss: 0.1948 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1336 - accuracy: 0.9561 - val_loss: 0.1954 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.1911 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.1925 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 85: 288.09 sec -Time taken for epoch(SUBo) 85: 237.57 sec -<---------------------------------------|Epoch [85] END|---------------------------------------> - -Epoch: 86/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1545 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1532 - accuracy: 0.9517 - val_loss: 0.2042 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1454 - accuracy: 0.9492 - val_loss: 0.1683 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1330 - accuracy: 0.9604 - val_loss: 0.1693 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1233 - accuracy: 0.9604 - val_loss: 0.1930 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1207 - accuracy: 0.9619 - val_loss: 0.1804 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 86: 288.17 sec -Time taken for epoch(SUBo) 86: 237.64 sec -<---------------------------------------|Epoch [86] END|---------------------------------------> - -Epoch: 87/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9536 - val_loss: 0.1667 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1656 - accuracy: 0.9478 - val_loss: 0.1621 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1384 - accuracy: 0.9595 - val_loss: 0.1620 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9585 - val_loss: 0.1718 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1227 - accuracy: 0.9595 - val_loss: 0.1562 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1226 - accuracy: 0.9653 - val_loss: 0.1679 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 87: 288.63 sec -Time taken for epoch(SUBo) 87: 237.58 sec -<---------------------------------------|Epoch [87] END|---------------------------------------> - -Epoch: 88/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1496 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1700 - accuracy: 0.9399 - val_loss: 0.1543 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1560 - accuracy: 0.9546 - val_loss: 0.1877 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.1802 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1187 - accuracy: 0.9609 - val_loss: 0.1640 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1221 - accuracy: 0.9629 - val_loss: 0.1898 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 88: 289.56 sec -Time taken for epoch(SUBo) 88: 238.18 sec -<---------------------------------------|Epoch [88] END|---------------------------------------> - -Epoch: 89/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1682 - accuracy: 0.9497 - val_loss: 0.1799 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1313 - accuracy: 0.9580 - val_loss: 0.2257 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9585 - val_loss: 0.2209 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1873 - accuracy: 0.9399 - val_loss: 0.1585 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1725 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1436 - accuracy: 0.9580 - val_loss: 0.1682 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 89: 288.75 sec -Time taken for epoch(SUBo) 89: 238.29 sec -<---------------------------------------|Epoch [89] END|---------------------------------------> - -Epoch: 90/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1505 - accuracy: 0.9502 - val_loss: 0.1977 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1613 - accuracy: 0.9478 - val_loss: 0.1510 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1232 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1183 - accuracy: 0.9658 - val_loss: 0.1810 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9717 - val_loss: 0.1728 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9658 - val_loss: 0.1794 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 90: 288.69 sec -Time taken for epoch(SUBo) 90: 237.88 sec -<---------------------------------------|Epoch [90] END|---------------------------------------> - -Epoch: 91/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1654 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1286 - accuracy: 0.9604 - val_loss: 0.2092 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1339 - accuracy: 0.9604 - val_loss: 0.1610 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1881 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9688 - val_loss: 0.2103 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0968 - accuracy: 0.9741 - val_loss: 0.2091 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 91: 290.04 sec -Time taken for epoch(SUBo) 91: 238.32 sec -<---------------------------------------|Epoch [91] END|---------------------------------------> - -Epoch: 92/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1806 - accuracy: 0.9453 - val_loss: 0.1973 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1625 - accuracy: 0.9502 - val_loss: 0.1934 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1476 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9551 - val_loss: 0.1942 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1282 - accuracy: 0.9580 - val_loss: 0.1883 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1260 - accuracy: 0.9619 - val_loss: 0.1955 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 92: 288.96 sec -Time taken for epoch(SUBo) 92: 237.66 sec -<---------------------------------------|Epoch [92] END|---------------------------------------> - -Epoch: 93/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1499 - accuracy: 0.9473 - val_loss: 0.1841 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1426 - accuracy: 0.9507 - val_loss: 0.2240 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1467 - accuracy: 0.9600 - val_loss: 0.1832 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1411 - accuracy: 0.9531 - val_loss: 0.4701 - val_accuracy: 0.8910 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1303 - accuracy: 0.9600 - val_loss: 0.3182 - val_accuracy: 0.9103 -Epoch 6/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.1197 - accuracy: 0.9692 - val_loss: 0.2972 - val_accuracy: 0.9151 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 93: 290.33 sec -Time taken for epoch(SUBo) 93: 239.01 sec -<---------------------------------------|Epoch [93] END|---------------------------------------> - -Epoch: 94/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1449 - accuracy: 0.9536 - val_loss: 0.2477 - val_accuracy: 0.9295 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1876 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9526 - val_loss: 0.2062 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1405 - accuracy: 0.9531 - val_loss: 0.1995 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1120 - accuracy: 0.9692 - val_loss: 0.2110 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9712 - val_loss: 0.2041 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 94: 289.36 sec -Time taken for epoch(SUBo) 94: 238.47 sec -<---------------------------------------|Epoch [94] END|---------------------------------------> - -Epoch: 95/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1489 - accuracy: 0.9580 - val_loss: 0.1769 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1445 - accuracy: 0.9512 - val_loss: 0.1728 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1269 - accuracy: 0.9565 - val_loss: 0.2260 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1205 - accuracy: 0.9624 - val_loss: 0.1696 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1278 - accuracy: 0.9624 - val_loss: 0.1737 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1040 - accuracy: 0.9707 - val_loss: 0.1714 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 95: 289.33 sec -Time taken for epoch(SUBo) 95: 238.40 sec -<---------------------------------------|Epoch [95] END|---------------------------------------> - -Epoch: 96/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1672 - accuracy: 0.9492 - val_loss: 0.1677 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1451 - accuracy: 0.9565 - val_loss: 0.1917 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9614 - val_loss: 0.2296 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.2639 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0987 - accuracy: 0.9717 - val_loss: 0.3081 - val_accuracy: 0.9215 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9653 - val_loss: 0.2600 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 96: 288.89 sec -Time taken for epoch(SUBo) 96: 237.88 sec -<---------------------------------------|Epoch [96] END|---------------------------------------> - -Epoch: 97/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1431 - accuracy: 0.9463 - val_loss: 0.2139 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1526 - accuracy: 0.9492 - val_loss: 0.2200 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1348 - accuracy: 0.9575 - val_loss: 0.2507 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9575 - val_loss: 0.2652 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9683 - val_loss: 0.2767 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1255 - accuracy: 0.9604 - val_loss: 0.2645 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 97: 288.48 sec -Time taken for epoch(SUBo) 97: 237.23 sec -<---------------------------------------|Epoch [97] END|---------------------------------------> - -Epoch: 98/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2275 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1329 - accuracy: 0.9614 - val_loss: 0.2393 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1515 - accuracy: 0.9556 - val_loss: 0.3716 - val_accuracy: 0.9135 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1402 - accuracy: 0.9595 - val_loss: 0.3404 - val_accuracy: 0.9087 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9712 - val_loss: 0.2649 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9648 - val_loss: 0.2462 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 98: 287.65 sec -Time taken for epoch(SUBo) 98: 237.26 sec -<---------------------------------------|Epoch [98] END|---------------------------------------> - -Epoch: 99/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.2086 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1320 - accuracy: 0.9580 - val_loss: 0.2175 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1388 - accuracy: 0.9556 - val_loss: 0.1846 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1222 - accuracy: 0.9658 - val_loss: 0.2280 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1001 - accuracy: 0.9692 - val_loss: 0.2335 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0935 - accuracy: 0.9741 - val_loss: 0.2289 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 99: 287.39 sec -Time taken for epoch(SUBo) 99: 237.52 sec -<---------------------------------------|Epoch [99] END|---------------------------------------> - -Epoch: 100/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1431 - accuracy: 0.9580 - val_loss: 0.2261 - val_accuracy: 0.9247 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1552 - accuracy: 0.9536 - val_loss: 0.1987 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1221 - accuracy: 0.9619 - val_loss: 0.2009 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9604 - val_loss: 0.2111 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1100 - accuracy: 0.9692 - val_loss: 0.2023 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0975 - accuracy: 0.9736 - val_loss: 0.1899 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 100: 287.11 sec -Time taken for epoch(SUBo) 100: 237.35 sec -<---------------------------------------|Epoch [100] END|---------------------------------------> - -Epoch: 101/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9541 - val_loss: 0.2182 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1364 - accuracy: 0.9629 - val_loss: 0.1850 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1349 - accuracy: 0.9600 - val_loss: 0.2381 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.1880 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1042 - accuracy: 0.9692 - val_loss: 0.2007 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0986 - accuracy: 0.9731 - val_loss: 0.2144 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 101: 287.74 sec -Time taken for epoch(SUBo) 101: 237.93 sec -<---------------------------------------|Epoch [101] END|---------------------------------------> - -Epoch: 102/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9570 - val_loss: 0.2415 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1164 - accuracy: 0.9653 - val_loss: 0.2319 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1270 - accuracy: 0.9658 - val_loss: 0.2692 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1342 - accuracy: 0.9629 - val_loss: 0.2067 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.1845 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1135 - accuracy: 0.9688 - val_loss: 0.2075 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 102: 288.00 sec -Time taken for epoch(SUBo) 102: 237.50 sec -<---------------------------------------|Epoch [102] END|---------------------------------------> - -Epoch: 103/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1454 - accuracy: 0.9531 - val_loss: 0.2672 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1464 - accuracy: 0.9556 - val_loss: 0.1568 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9614 - val_loss: 0.2431 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1267 - accuracy: 0.9595 - val_loss: 0.1676 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1114 - accuracy: 0.9648 - val_loss: 0.1947 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1131 - accuracy: 0.9688 - val_loss: 0.1926 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 103: 287.73 sec -Time taken for epoch(SUBo) 103: 237.64 sec -<---------------------------------------|Epoch [103] END|---------------------------------------> - -Epoch: 104/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1319 - accuracy: 0.9551 - val_loss: 0.2187 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1435 - accuracy: 0.9565 - val_loss: 0.2262 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1363 - accuracy: 0.9556 - val_loss: 0.1924 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1133 - accuracy: 0.9678 - val_loss: 0.2607 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9717 - val_loss: 0.2344 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1026 - accuracy: 0.9673 - val_loss: 0.2418 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 104: 286.90 sec -Time taken for epoch(SUBo) 104: 237.53 sec -<---------------------------------------|Epoch [104] END|---------------------------------------> - -Epoch: 105/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9580 - val_loss: 0.2079 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1239 - accuracy: 0.9600 - val_loss: 0.2032 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1005 - accuracy: 0.9722 - val_loss: 0.2134 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9688 - val_loss: 0.1937 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0898 - accuracy: 0.9741 - val_loss: 0.1968 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 105: 287.02 sec -Time taken for epoch(SUBo) 105: 237.52 sec -<---------------------------------------|Epoch [105] END|---------------------------------------> - -Epoch: 106/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1352 - accuracy: 0.9575 - val_loss: 0.1525 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1355 - accuracy: 0.9570 - val_loss: 0.1892 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1163 - accuracy: 0.9692 - val_loss: 0.1639 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1066 - accuracy: 0.9678 - val_loss: 0.1816 - val_accuracy: 0.9583 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0869 - accuracy: 0.9736 - val_loss: 0.1968 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0897 - accuracy: 0.9741 - val_loss: 0.2022 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 106: 287.48 sec -Time taken for epoch(SUBo) 106: 237.69 sec -<---------------------------------------|Epoch [106] END|---------------------------------------> - -Epoch: 107/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1194 - accuracy: 0.9644 - val_loss: 0.1767 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9668 - val_loss: 0.1995 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1046 - accuracy: 0.9663 - val_loss: 0.1818 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0864 - accuracy: 0.9746 - val_loss: 0.1969 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0910 - accuracy: 0.9722 - val_loss: 0.1441 - val_accuracy: 0.9663 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9653 - val_loss: 0.1590 - val_accuracy: 0.9696 -Subset training done. -Improved model accuracy from 0.9599359035491943 to 0.9695512652397156. Saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 107: 289.43 sec -Time taken for epoch(SUBo) 107: 237.56 sec -<---------------------------------------|Epoch [107] END|---------------------------------------> - -Epoch: 108/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1730 - accuracy: 0.9492 - val_loss: 0.1516 - val_accuracy: 0.9679 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1736 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.1854 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1192 - accuracy: 0.9658 - val_loss: 0.2242 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1115 - accuracy: 0.9663 - val_loss: 0.1922 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0976 - accuracy: 0.9722 - val_loss: 0.1996 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 108: 288.48 sec -Time taken for epoch(SUBo) 108: 238.16 sec -<---------------------------------------|Epoch [108] END|---------------------------------------> - -Epoch: 109/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1546 - accuracy: 0.9526 - val_loss: 0.1503 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1529 - accuracy: 0.9551 - val_loss: 0.1752 - val_accuracy: 0.9631 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.1519 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1593 - accuracy: 0.9492 - val_loss: 0.1787 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1744 - accuracy: 0.9434 - val_loss: 0.1705 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1520 - accuracy: 0.9502 - val_loss: 0.1609 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 109: 287.98 sec -Time taken for epoch(SUBo) 109: 238.06 sec -<---------------------------------------|Epoch [109] END|---------------------------------------> - -Epoch: 110/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1470 - accuracy: 0.9482 - val_loss: 0.1651 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1690 - accuracy: 0.9443 - val_loss: 0.2425 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1394 - accuracy: 0.9561 - val_loss: 0.1863 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9619 - val_loss: 0.1728 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1037 - accuracy: 0.9653 - val_loss: 0.1770 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0962 - accuracy: 0.9712 - val_loss: 0.1774 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 110: 288.95 sec -Time taken for epoch(SUBo) 110: 238.41 sec -<---------------------------------------|Epoch [110] END|---------------------------------------> - -Epoch: 111/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1625 - accuracy: 0.9487 - val_loss: 0.1659 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1540 - accuracy: 0.9556 - val_loss: 0.1548 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1736 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1230 - accuracy: 0.9639 - val_loss: 0.2110 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1110 - accuracy: 0.9717 - val_loss: 0.1803 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1079 - accuracy: 0.9688 - val_loss: 0.1742 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 111: 288.76 sec -Time taken for epoch(SUBo) 111: 238.28 sec -<---------------------------------------|Epoch [111] END|---------------------------------------> - -Epoch: 112/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1898 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9473 - val_loss: 0.2439 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1295 - accuracy: 0.9614 - val_loss: 0.2080 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1483 - accuracy: 0.9604 - val_loss: 0.2009 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.2107 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2227 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 112: 288.69 sec -Time taken for epoch(SUBo) 112: 237.84 sec -<---------------------------------------|Epoch [112] END|---------------------------------------> - -Epoch: 113/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1289 - accuracy: 0.9604 - val_loss: 0.1870 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1315 - accuracy: 0.9619 - val_loss: 0.1862 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1271 - accuracy: 0.9604 - val_loss: 0.1778 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9707 - val_loss: 0.1887 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2135 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0856 - accuracy: 0.9741 - val_loss: 0.2159 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 113: 289.27 sec -Time taken for epoch(SUBo) 113: 237.88 sec -<---------------------------------------|Epoch [113] END|---------------------------------------> - -Epoch: 114/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1358 - accuracy: 0.9595 - val_loss: 0.1854 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9644 - val_loss: 0.2141 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2008 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9639 - val_loss: 0.1953 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1022 - accuracy: 0.9663 - val_loss: 0.1951 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0806 - accuracy: 0.9775 - val_loss: 0.1923 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 114: 288.83 sec -Time taken for epoch(SUBo) 114: 237.68 sec -<---------------------------------------|Epoch [114] END|---------------------------------------> - -Epoch: 115/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1186 - accuracy: 0.9600 - val_loss: 0.2549 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1196 - accuracy: 0.9604 - val_loss: 0.2198 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1253 - accuracy: 0.9590 - val_loss: 0.2396 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1043 - accuracy: 0.9736 - val_loss: 0.2314 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0960 - accuracy: 0.9712 - val_loss: 0.2056 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0915 - accuracy: 0.9722 - val_loss: 0.2126 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 115: 289.11 sec -Time taken for epoch(SUBo) 115: 238.53 sec -<---------------------------------------|Epoch [115] END|---------------------------------------> - -Epoch: 116/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2195 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1368 - accuracy: 0.9595 - val_loss: 0.1903 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1198 - accuracy: 0.9614 - val_loss: 0.2051 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1856 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1008 - accuracy: 0.9702 - val_loss: 0.1742 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1027 - accuracy: 0.9717 - val_loss: 0.1697 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 116: 289.60 sec -Time taken for epoch(SUBo) 116: 239.21 sec -<---------------------------------------|Epoch [116] END|---------------------------------------> - -Epoch: 117/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.1718 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9580 - val_loss: 0.2046 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0925 - accuracy: 0.9722 - val_loss: 0.2292 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0834 - accuracy: 0.9751 - val_loss: 0.2023 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9727 - val_loss: 0.2151 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1000 - accuracy: 0.9722 - val_loss: 0.2206 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 117: 294.65 sec -Time taken for epoch(SUBo) 117: 244.16 sec -<---------------------------------------|Epoch [117] END|---------------------------------------> - -Epoch: 118/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1199 - accuracy: 0.9644 - val_loss: 0.2294 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1139 - accuracy: 0.9663 - val_loss: 0.1655 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1037 - accuracy: 0.9707 - val_loss: 0.1589 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0889 - accuracy: 0.9741 - val_loss: 0.2250 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0840 - accuracy: 0.9785 - val_loss: 0.1895 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0828 - accuracy: 0.9727 - val_loss: 0.1852 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 118: 295.73 sec -Time taken for epoch(SUBo) 118: 244.43 sec -<---------------------------------------|Epoch [118] END|---------------------------------------> - -Epoch: 119/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1416 - accuracy: 0.9585 - val_loss: 0.1226 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1682 - accuracy: 0.9434 - val_loss: 0.1301 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1486 - accuracy: 0.9497 - val_loss: 0.1562 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1247 - accuracy: 0.9604 - val_loss: 0.1408 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1476 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1120 - accuracy: 0.9629 - val_loss: 0.1468 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.15118563175201416 to 0.146798238158226. Saving model. -Time taken for epoch(FULL) 119: 296.83 sec -Time taken for epoch(SUBo) 119: 244.81 sec -<---------------------------------------|Epoch [119] END|---------------------------------------> - -Epoch: 120/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1305 - accuracy: 0.9570 - val_loss: 0.1442 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1428 - accuracy: 0.9551 - val_loss: 0.1382 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9653 - val_loss: 0.1388 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1095 - accuracy: 0.9692 - val_loss: 0.1446 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0795 - accuracy: 0.9790 - val_loss: 0.1430 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1469 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 120: 295.05 sec -Time taken for epoch(SUBo) 120: 244.35 sec -<---------------------------------------|Epoch [120] END|---------------------------------------> - -Epoch: 121/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.1539 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1415 - accuracy: 0.9541 - val_loss: 0.1573 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1153 - accuracy: 0.9717 - val_loss: 0.1778 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1108 - accuracy: 0.9683 - val_loss: 0.1774 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9697 - val_loss: 0.1738 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9727 - val_loss: 0.1716 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 121: 294.56 sec -Time taken for epoch(SUBo) 121: 244.29 sec -<---------------------------------------|Epoch [121] END|---------------------------------------> - -Epoch: 122/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1261 - accuracy: 0.9619 - val_loss: 0.1905 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9634 - val_loss: 0.1801 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1278 - accuracy: 0.9580 - val_loss: 0.2058 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9663 - val_loss: 0.2683 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1103 - accuracy: 0.9648 - val_loss: 0.1943 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 122: 295.23 sec -Time taken for epoch(SUBo) 122: 244.50 sec -<---------------------------------------|Epoch [122] END|---------------------------------------> - -Epoch: 123/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1423 - accuracy: 0.9570 - val_loss: 0.1759 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9624 - val_loss: 0.2300 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1347 - accuracy: 0.9600 - val_loss: 0.2434 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1360 - accuracy: 0.9565 - val_loss: 0.2215 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1029 - accuracy: 0.9678 - val_loss: 0.2258 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1030 - accuracy: 0.9658 - val_loss: 0.1975 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 123: 294.95 sec -Time taken for epoch(SUBo) 123: 244.32 sec -<---------------------------------------|Epoch [123] END|---------------------------------------> - -Epoch: 124/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1253 - accuracy: 0.9614 - val_loss: 0.2786 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9600 - val_loss: 0.2731 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1414 - accuracy: 0.9575 - val_loss: 0.2149 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1280 - accuracy: 0.9609 - val_loss: 0.2693 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.2356 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.2349 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 124: 294.95 sec -Time taken for epoch(SUBo) 124: 244.30 sec -<---------------------------------------|Epoch [124] END|---------------------------------------> - -Epoch: 125/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1388 - accuracy: 0.9570 - val_loss: 0.2241 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1322 - accuracy: 0.9595 - val_loss: 0.2067 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1604 - accuracy: 0.9448 - val_loss: 0.2070 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1206 - accuracy: 0.9629 - val_loss: 0.1951 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1370 - accuracy: 0.9556 - val_loss: 0.1795 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9614 - val_loss: 0.1803 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 125: 296.66 sec -Time taken for epoch(SUBo) 125: 245.25 sec -<---------------------------------------|Epoch [125] END|---------------------------------------> - -Epoch: 126/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9443 - val_loss: 0.1636 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1469 - accuracy: 0.9531 - val_loss: 0.1743 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9600 - val_loss: 0.2001 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1122 - accuracy: 0.9634 - val_loss: 0.2148 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9692 - val_loss: 0.1990 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0975 - accuracy: 0.9727 - val_loss: 0.1967 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 126: 296.05 sec -Time taken for epoch(SUBo) 126: 244.69 sec -<---------------------------------------|Epoch [126] END|---------------------------------------> - -Epoch: 127/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1350 - accuracy: 0.9590 - val_loss: 0.2002 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9604 - val_loss: 0.1730 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1136 - accuracy: 0.9658 - val_loss: 0.2452 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0970 - accuracy: 0.9756 - val_loss: 0.2381 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0872 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0813 - accuracy: 0.9761 - val_loss: 0.2530 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 127: 295.58 sec -Time taken for epoch(SUBo) 127: 244.41 sec -<---------------------------------------|Epoch [127] END|---------------------------------------> - -Epoch: 128/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1365 - accuracy: 0.9521 - val_loss: 0.1995 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1957 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1184 - accuracy: 0.9609 - val_loss: 0.1864 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1086 - accuracy: 0.9712 - val_loss: 0.2123 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1137 - accuracy: 0.9653 - val_loss: 0.1765 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1008 - accuracy: 0.9697 - val_loss: 0.1619 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 128: 303.71 sec -Time taken for epoch(SUBo) 128: 244.02 sec -<---------------------------------------|Epoch [128] END|---------------------------------------> - -Epoch: 129/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1492 - accuracy: 0.9492 - val_loss: 0.1890 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1478 - accuracy: 0.9565 - val_loss: 0.1770 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1285 - accuracy: 0.9609 - val_loss: 0.1963 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1629 - val_accuracy: 0.9599 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1027 - accuracy: 0.9722 - val_loss: 0.1720 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0962 - accuracy: 0.9722 - val_loss: 0.1728 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 129: 304.31 sec -Time taken for epoch(SUBo) 129: 243.77 sec -<---------------------------------------|Epoch [129] END|---------------------------------------> - -Epoch: 130/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1344 - accuracy: 0.9595 - val_loss: 0.1606 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1276 - accuracy: 0.9624 - val_loss: 0.1791 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.1730 - val_accuracy: 0.9615 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1088 - accuracy: 0.9683 - val_loss: 0.1984 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1004 - accuracy: 0.9668 - val_loss: 0.2138 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1041 - accuracy: 0.9683 - val_loss: 0.1963 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 130: 301.26 sec -Time taken for epoch(SUBo) 130: 244.07 sec -<---------------------------------------|Epoch [130] END|---------------------------------------> - -Epoch: 131/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1314 - accuracy: 0.9614 - val_loss: 0.1733 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9556 - val_loss: 0.1815 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9639 - val_loss: 0.1522 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1197 - accuracy: 0.9644 - val_loss: 0.1593 - val_accuracy: 0.9615 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1065 - accuracy: 0.9707 - val_loss: 0.1619 - val_accuracy: 0.9615 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0984 - accuracy: 0.9697 - val_loss: 0.1596 - val_accuracy: 0.9631 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 131: 300.73 sec -Time taken for epoch(SUBo) 131: 244.44 sec -<---------------------------------------|Epoch [131] END|---------------------------------------> - -Epoch: 132/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1359 - accuracy: 0.9590 - val_loss: 0.1611 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1136 - accuracy: 0.9644 - val_loss: 0.1692 - val_accuracy: 0.9615 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1270 - accuracy: 0.9629 - val_loss: 0.2881 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1380 - accuracy: 0.9609 - val_loss: 0.1959 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1193 - accuracy: 0.9658 - val_loss: 0.2176 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1125 - accuracy: 0.9648 - val_loss: 0.2147 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 132: 298.91 sec -Time taken for epoch(SUBo) 132: 243.60 sec -<---------------------------------------|Epoch [132] END|---------------------------------------> - -Epoch: 133/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1469 - accuracy: 0.9521 - val_loss: 0.2294 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1442 - accuracy: 0.9580 - val_loss: 0.2275 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1246 - accuracy: 0.9619 - val_loss: 0.2881 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1150 - accuracy: 0.9673 - val_loss: 0.2647 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1132 - accuracy: 0.9648 - val_loss: 0.2474 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0897 - accuracy: 0.9751 - val_loss: 0.2609 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 133: 296.74 sec -Time taken for epoch(SUBo) 133: 242.77 sec -<---------------------------------------|Epoch [133] END|---------------------------------------> - -Epoch: 134/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1280 - accuracy: 0.9604 - val_loss: 0.2374 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1308 - accuracy: 0.9590 - val_loss: 0.2543 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1377 - accuracy: 0.9565 - val_loss: 0.2752 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1032 - accuracy: 0.9736 - val_loss: 0.2675 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9663 - val_loss: 0.2584 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0954 - accuracy: 0.9756 - val_loss: 0.2853 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 134: 297.41 sec -Time taken for epoch(SUBo) 134: 243.12 sec -<---------------------------------------|Epoch [134] END|---------------------------------------> - -Epoch: 135/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1629 - accuracy: 0.9482 - val_loss: 0.2191 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1362 - accuracy: 0.9575 - val_loss: 0.2275 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1400 - accuracy: 0.9570 - val_loss: 0.1914 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1302 - accuracy: 0.9639 - val_loss: 0.1995 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9653 - val_loss: 0.2003 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1085 - accuracy: 0.9697 - val_loss: 0.2064 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 135: 298.46 sec -Time taken for epoch(SUBo) 135: 243.19 sec -<---------------------------------------|Epoch [135] END|---------------------------------------> - -Epoch: 136/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1415 - accuracy: 0.9561 - val_loss: 0.1941 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1323 - accuracy: 0.9648 - val_loss: 0.2252 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.1982 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2166 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9678 - val_loss: 0.2508 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0991 - accuracy: 0.9707 - val_loss: 0.2181 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 136: 300.20 sec -Time taken for epoch(SUBo) 136: 243.49 sec -<---------------------------------------|Epoch [136] END|---------------------------------------> - -Epoch: 137/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1551 - accuracy: 0.9531 - val_loss: 0.2049 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1405 - accuracy: 0.9546 - val_loss: 0.2349 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1254 - accuracy: 0.9595 - val_loss: 0.1758 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1130 - accuracy: 0.9634 - val_loss: 0.2124 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0963 - accuracy: 0.9736 - val_loss: 0.1902 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.1870 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 137: 300.02 sec -Time taken for epoch(SUBo) 137: 243.90 sec -<---------------------------------------|Epoch [137] END|---------------------------------------> - -Epoch: 138/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1243 - accuracy: 0.9644 - val_loss: 0.1907 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1289 - accuracy: 0.9590 - val_loss: 0.1533 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1203 - accuracy: 0.9604 - val_loss: 0.1708 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.1635 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0951 - accuracy: 0.9736 - val_loss: 0.1628 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0872 - accuracy: 0.9756 - val_loss: 0.1781 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 138: 298.57 sec -Time taken for epoch(SUBo) 138: 243.89 sec -<---------------------------------------|Epoch [138] END|---------------------------------------> - -Epoch: 139/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1322 - accuracy: 0.9629 - val_loss: 0.1652 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1569 - accuracy: 0.9458 - val_loss: 0.2143 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2487 - val_accuracy: 0.9231 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1343 - accuracy: 0.9585 - val_loss: 0.1756 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1018 - accuracy: 0.9678 - val_loss: 0.1879 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0864 - accuracy: 0.9751 - val_loss: 0.2002 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 139: 296.96 sec -Time taken for epoch(SUBo) 139: 243.53 sec -<---------------------------------------|Epoch [139] END|---------------------------------------> - -Epoch: 140/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1588 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9595 - val_loss: 0.1786 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1241 - accuracy: 0.9619 - val_loss: 0.1725 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1104 - accuracy: 0.9683 - val_loss: 0.1877 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1057 - accuracy: 0.9702 - val_loss: 0.1923 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1891 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 140: 298.07 sec -Time taken for epoch(SUBo) 140: 243.40 sec -<---------------------------------------|Epoch [140] END|---------------------------------------> - -Epoch: 141/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1314 - accuracy: 0.9541 - val_loss: 0.1613 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.1692 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1292 - accuracy: 0.9580 - val_loss: 0.1645 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9673 - val_loss: 0.1783 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0957 - accuracy: 0.9727 - val_loss: 0.1860 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1725 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 141: 298.52 sec -Time taken for epoch(SUBo) 141: 243.77 sec -<---------------------------------------|Epoch [141] END|---------------------------------------> - -Epoch: 142/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.1811 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1378 - accuracy: 0.9536 - val_loss: 0.1458 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1216 - accuracy: 0.9614 - val_loss: 0.1723 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1112 - accuracy: 0.9683 - val_loss: 0.1895 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1075 - accuracy: 0.9707 - val_loss: 0.1709 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1590 - val_accuracy: 0.9599 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 142: 297.84 sec -Time taken for epoch(SUBo) 142: 243.24 sec -<---------------------------------------|Epoch [142] END|---------------------------------------> - -Epoch: 143/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1446 - accuracy: 0.9512 - val_loss: 0.1575 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9600 - val_loss: 0.1438 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1499 - accuracy: 0.9556 - val_loss: 0.1531 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1312 - accuracy: 0.9575 - val_loss: 0.1520 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9629 - val_loss: 0.1651 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1007 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 143: 296.59 sec -Time taken for epoch(SUBo) 143: 243.29 sec -<---------------------------------------|Epoch [143] END|---------------------------------------> - -Epoch: 144/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 158ms/step - loss: 0.1502 - accuracy: 0.9531 - val_loss: 0.1520 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1484 - accuracy: 0.9536 - val_loss: 0.1554 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1475 - accuracy: 0.9575 - val_loss: 0.1452 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1296 - accuracy: 0.9624 - val_loss: 0.1943 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1104 - accuracy: 0.9648 - val_loss: 0.1803 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0984 - accuracy: 0.9736 - val_loss: 0.1858 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 144: 296.73 sec -Time taken for epoch(SUBo) 144: 242.88 sec -<---------------------------------------|Epoch [144] END|---------------------------------------> - -Epoch: 145/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1262 - accuracy: 0.9600 - val_loss: 0.1634 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1191 - accuracy: 0.9639 - val_loss: 0.1680 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1056 - accuracy: 0.9658 - val_loss: 0.1970 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1031 - accuracy: 0.9707 - val_loss: 0.2054 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0822 - accuracy: 0.9800 - val_loss: 0.2039 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0879 - accuracy: 0.9746 - val_loss: 0.2102 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 145: 298.13 sec -Time taken for epoch(SUBo) 145: 242.21 sec -<---------------------------------------|Epoch [145] END|---------------------------------------> - -Epoch: 146/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1362 - accuracy: 0.9570 - val_loss: 0.1822 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1300 - accuracy: 0.9595 - val_loss: 0.2085 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1156 - accuracy: 0.9629 - val_loss: 0.2197 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0958 - accuracy: 0.9761 - val_loss: 0.2403 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.2088 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0887 - accuracy: 0.9702 - val_loss: 0.2360 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 146: 301.03 sec -Time taken for epoch(SUBo) 146: 242.33 sec -<---------------------------------------|Epoch [146] END|---------------------------------------> - -Epoch: 147/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1234 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9614 - val_loss: 0.1836 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1030 - accuracy: 0.9717 - val_loss: 0.1736 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0980 - accuracy: 0.9707 - val_loss: 0.1931 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0948 - accuracy: 0.9722 - val_loss: 0.1875 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1813 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 147: 303.25 sec -Time taken for epoch(SUBo) 147: 242.94 sec -<---------------------------------------|Epoch [147] END|---------------------------------------> - -Epoch: 148/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1321 - accuracy: 0.9565 - val_loss: 0.2085 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1171 - accuracy: 0.9629 - val_loss: 0.1716 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1633 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1642 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1000 - accuracy: 0.9702 - val_loss: 0.1597 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0804 - accuracy: 0.9756 - val_loss: 0.1575 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 148: 301.98 sec -Time taken for epoch(SUBo) 148: 243.14 sec -<---------------------------------------|Epoch [148] END|---------------------------------------> - -Epoch: 149/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1178 - accuracy: 0.9634 - val_loss: 0.1412 - val_accuracy: 0.9615 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1271 - accuracy: 0.9580 - val_loss: 0.1553 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1074 - accuracy: 0.9658 - val_loss: 0.1972 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0920 - accuracy: 0.9741 - val_loss: 0.1781 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1054 - accuracy: 0.9692 - val_loss: 0.1791 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0850 - accuracy: 0.9761 - val_loss: 0.1786 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 149: 298.73 sec -Time taken for epoch(SUBo) 149: 242.85 sec -<---------------------------------------|Epoch [149] END|---------------------------------------> - -Epoch: 150/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1315 - accuracy: 0.9580 - val_loss: 0.1966 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1324 - accuracy: 0.9551 - val_loss: 0.2153 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1131 - accuracy: 0.9634 - val_loss: 0.2608 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1028 - accuracy: 0.9697 - val_loss: 0.2539 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0900 - accuracy: 0.9707 - val_loss: 0.2782 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1002 - accuracy: 0.9697 - val_loss: 0.2693 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 150: 300.25 sec -Time taken for epoch(SUBo) 150: 243.69 sec -<---------------------------------------|Epoch [150] END|---------------------------------------> - -Epoch: 151/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2125 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1103 - accuracy: 0.9712 - val_loss: 0.2087 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1040 - accuracy: 0.9653 - val_loss: 0.2110 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0983 - accuracy: 0.9727 - val_loss: 0.1971 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1968 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0845 - accuracy: 0.9751 - val_loss: 0.2230 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 151: 298.97 sec -Time taken for epoch(SUBo) 151: 242.93 sec -<---------------------------------------|Epoch [151] END|---------------------------------------> - -Epoch: 152/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1268 - accuracy: 0.9663 - val_loss: 0.2006 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9678 - val_loss: 0.1805 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1365 - accuracy: 0.9565 - val_loss: 0.1432 - val_accuracy: 0.9631 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1488 - accuracy: 0.9517 - val_loss: 0.1688 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1657 - accuracy: 0.9458 - val_loss: 0.1674 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1403 - accuracy: 0.9561 - val_loss: 0.1698 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 152: 302.68 sec -Time taken for epoch(SUBo) 152: 243.68 sec -<---------------------------------------|Epoch [152] END|---------------------------------------> - -Epoch: 153/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1499 - accuracy: 0.9507 - val_loss: 0.1872 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1414 - accuracy: 0.9580 - val_loss: 0.1947 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1562 - accuracy: 0.9463 - val_loss: 0.2135 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9629 - val_loss: 0.1884 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9712 - val_loss: 0.2042 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9712 - val_loss: 0.1936 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 153: 299.14 sec -Time taken for epoch(SUBo) 153: 243.89 sec -<---------------------------------------|Epoch [153] END|---------------------------------------> - -Epoch: 154/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1391 - accuracy: 0.9531 - val_loss: 0.1623 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1460 - accuracy: 0.9497 - val_loss: 0.2164 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1347 - accuracy: 0.9619 - val_loss: 0.4024 - val_accuracy: 0.8686 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2569 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1417 - accuracy: 0.9546 - val_loss: 0.2886 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2901 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 154: 303.43 sec -Time taken for epoch(SUBo) 154: 244.09 sec -<---------------------------------------|Epoch [154] END|---------------------------------------> - -Epoch: 155/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1674 - accuracy: 0.9424 - val_loss: 0.2398 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1466 - accuracy: 0.9556 - val_loss: 0.2424 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1350 - accuracy: 0.9565 - val_loss: 0.2398 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1153 - accuracy: 0.9639 - val_loss: 0.2173 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9692 - val_loss: 0.2637 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2615 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 155: 299.62 sec -Time taken for epoch(SUBo) 155: 243.67 sec -<---------------------------------------|Epoch [155] END|---------------------------------------> - -Epoch: 156/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9434 - val_loss: 0.2209 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1493 - accuracy: 0.9517 - val_loss: 0.2582 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1431 - accuracy: 0.9502 - val_loss: 0.2281 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9551 - val_loss: 0.2542 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1168 - accuracy: 0.9600 - val_loss: 0.1981 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9531 - val_loss: 0.2167 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 156: 301.27 sec -Time taken for epoch(SUBo) 156: 244.46 sec -<---------------------------------------|Epoch [156] END|---------------------------------------> - -Epoch: 157/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1338 - accuracy: 0.9565 - val_loss: 0.2626 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1420 - accuracy: 0.9473 - val_loss: 0.3502 - val_accuracy: 0.9215 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1291 - accuracy: 0.9585 - val_loss: 0.2344 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1022 - accuracy: 0.9683 - val_loss: 0.2722 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1164 - accuracy: 0.9648 - val_loss: 0.2915 - val_accuracy: 0.9215 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2660 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 157: 298.53 sec -Time taken for epoch(SUBo) 157: 243.70 sec -<---------------------------------------|Epoch [157] END|---------------------------------------> - -Epoch: 158/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1569 - accuracy: 0.9517 - val_loss: 0.2548 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1227 - accuracy: 0.9609 - val_loss: 0.3033 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9575 - val_loss: 0.2928 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1117 - accuracy: 0.9663 - val_loss: 0.2713 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9717 - val_loss: 0.2921 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0927 - accuracy: 0.9741 - val_loss: 0.2760 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 158: 305.20 sec -Time taken for epoch(SUBo) 158: 244.85 sec -<---------------------------------------|Epoch [158] END|---------------------------------------> - -Epoch: 159/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1135 - accuracy: 0.9668 - val_loss: 0.2714 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1001 - accuracy: 0.9663 - val_loss: 0.3513 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0937 - accuracy: 0.9712 - val_loss: 0.2725 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0861 - accuracy: 0.9780 - val_loss: 0.2921 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0836 - accuracy: 0.9751 - val_loss: 0.2788 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0809 - accuracy: 0.9780 - val_loss: 0.2651 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 159: 306.51 sec -Time taken for epoch(SUBo) 159: 245.04 sec -<---------------------------------------|Epoch [159] END|---------------------------------------> - -Epoch: 160/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1241 - accuracy: 0.9609 - val_loss: 0.2724 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9570 - val_loss: 0.2510 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1102 - accuracy: 0.9653 - val_loss: 0.2081 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1101 - accuracy: 0.9702 - val_loss: 0.1942 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.2166 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0885 - accuracy: 0.9727 - val_loss: 0.2052 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 160: 305.10 sec -Time taken for epoch(SUBo) 160: 245.83 sec -<---------------------------------------|Epoch [160] END|---------------------------------------> - -Epoch: 161/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1290 - accuracy: 0.9614 - val_loss: 0.1891 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9575 - val_loss: 0.1965 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2083 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1031 - accuracy: 0.9678 - val_loss: 0.2418 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1070 - accuracy: 0.9678 - val_loss: 0.2420 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0859 - accuracy: 0.9761 - val_loss: 0.2691 - val_accuracy: 0.9247 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 161: 299.90 sec -Time taken for epoch(SUBo) 161: 244.85 sec -<---------------------------------------|Epoch [161] END|---------------------------------------> - -Epoch: 162/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1228 - accuracy: 0.9629 - val_loss: 0.2065 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1999 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1606 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1366 - accuracy: 0.9575 - val_loss: 0.2026 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9619 - val_loss: 0.2040 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1096 - accuracy: 0.9673 - val_loss: 0.2063 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 162: 299.56 sec -Time taken for epoch(SUBo) 162: 244.33 sec -<---------------------------------------|Epoch [162] END|---------------------------------------> - -Epoch: 163/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1399 - accuracy: 0.9565 - val_loss: 0.2292 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1215 - accuracy: 0.9585 - val_loss: 0.2450 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1078 - accuracy: 0.9648 - val_loss: 0.2188 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1154 - accuracy: 0.9648 - val_loss: 0.2537 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9619 - val_loss: 0.2278 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9634 - val_loss: 0.2206 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 163: 297.49 sec -Time taken for epoch(SUBo) 163: 243.39 sec -<---------------------------------------|Epoch [163] END|---------------------------------------> - -Epoch: 164/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1580 - accuracy: 0.9507 - val_loss: 0.2399 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9570 - val_loss: 0.2307 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1342 - accuracy: 0.9604 - val_loss: 0.1897 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.2260 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9668 - val_loss: 0.2024 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0980 - accuracy: 0.9673 - val_loss: 0.2013 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 164: 300.36 sec -Time taken for epoch(SUBo) 164: 244.25 sec -<---------------------------------------|Epoch [164] END|---------------------------------------> - -Epoch: 165/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1589 - accuracy: 0.9497 - val_loss: 0.1661 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9575 - val_loss: 0.2048 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1393 - accuracy: 0.9600 - val_loss: 0.1941 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.2079 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1092 - accuracy: 0.9688 - val_loss: 0.2288 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9761 - val_loss: 0.2080 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 165: 307.38 sec -Time taken for epoch(SUBo) 165: 245.49 sec -<---------------------------------------|Epoch [165] END|---------------------------------------> - -Epoch: 166/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1276 - accuracy: 0.9585 - val_loss: 0.2018 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1107 - accuracy: 0.9673 - val_loss: 0.1818 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1782 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9731 - val_loss: 0.1845 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0775 - accuracy: 0.9756 - val_loss: 0.1787 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 166: 306.37 sec -Time taken for epoch(SUBo) 166: 244.99 sec -<---------------------------------------|Epoch [166] END|---------------------------------------> - -Epoch: 167/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 162ms/step - loss: 0.1360 - accuracy: 0.9585 - val_loss: 0.1928 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.1949 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1286 - accuracy: 0.9600 - val_loss: 0.2223 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1548 - accuracy: 0.9487 - val_loss: 0.3237 - val_accuracy: 0.9199 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1733 - accuracy: 0.9395 - val_loss: 0.2911 - val_accuracy: 0.9135 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9565 - val_loss: 0.2720 - val_accuracy: 0.9231 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 167: 302.14 sec -Time taken for epoch(SUBo) 167: 244.91 sec -<---------------------------------------|Epoch [167] END|---------------------------------------> - -Epoch: 168/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1783 - accuracy: 0.9365 - val_loss: 0.3662 - val_accuracy: 0.9006 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1679 - accuracy: 0.9419 - val_loss: 0.2450 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1442 - accuracy: 0.9512 - val_loss: 0.2916 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1321 - accuracy: 0.9575 - val_loss: 0.3255 - val_accuracy: 0.9231 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1195 - accuracy: 0.9624 - val_loss: 0.3551 - val_accuracy: 0.9199 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1084 - accuracy: 0.9668 - val_loss: 0.3794 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 168: 299.21 sec -Time taken for epoch(SUBo) 168: 243.84 sec -<---------------------------------------|Epoch [168] END|---------------------------------------> - -Epoch: 169/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1427 - accuracy: 0.9624 - val_loss: 0.2396 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1854 - accuracy: 0.9336 - val_loss: 0.2213 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1539 - accuracy: 0.9458 - val_loss: 0.2068 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1354 - accuracy: 0.9585 - val_loss: 0.3011 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1135 - accuracy: 0.9629 - val_loss: 0.2591 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1127 - accuracy: 0.9629 - val_loss: 0.2691 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 169: 300.15 sec -Time taken for epoch(SUBo) 169: 243.82 sec -<---------------------------------------|Epoch [169] END|---------------------------------------> - -Epoch: 170/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1595 - accuracy: 0.9438 - val_loss: 0.2370 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1465 - accuracy: 0.9492 - val_loss: 0.1867 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1322 - accuracy: 0.9565 - val_loss: 0.2246 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1295 - accuracy: 0.9609 - val_loss: 0.2039 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1179 - accuracy: 0.9644 - val_loss: 0.1999 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2048 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 170: 299.63 sec -Time taken for epoch(SUBo) 170: 242.91 sec -<---------------------------------------|Epoch [170] END|---------------------------------------> - -Epoch: 171/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1650 - accuracy: 0.9541 - val_loss: 0.1615 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9565 - val_loss: 0.1734 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1397 - accuracy: 0.9570 - val_loss: 0.1680 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1035 - accuracy: 0.9741 - val_loss: 0.1722 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1021 - accuracy: 0.9668 - val_loss: 0.1847 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.1761 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 171: 304.06 sec -Time taken for epoch(SUBo) 171: 243.87 sec -<---------------------------------------|Epoch [171] END|---------------------------------------> - -Epoch: 172/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1201 - accuracy: 0.9629 - val_loss: 0.1739 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9614 - val_loss: 0.1925 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1138 - accuracy: 0.9673 - val_loss: 0.2372 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1000 - accuracy: 0.9697 - val_loss: 0.1883 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0899 - accuracy: 0.9731 - val_loss: 0.2044 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0740 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 172: 304.33 sec -Time taken for epoch(SUBo) 172: 243.65 sec -<---------------------------------------|Epoch [172] END|---------------------------------------> - -Epoch: 173/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1373 - accuracy: 0.9541 - val_loss: 0.1948 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1472 - accuracy: 0.9502 - val_loss: 0.2673 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1669 - accuracy: 0.9453 - val_loss: 0.1954 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.1729 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9629 - val_loss: 0.2251 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1095 - accuracy: 0.9658 - val_loss: 0.2223 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 173: 302.38 sec -Time taken for epoch(SUBo) 173: 244.10 sec -<---------------------------------------|Epoch [173] END|---------------------------------------> - -Epoch: 174/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.2098 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1407 - accuracy: 0.9561 - val_loss: 0.2066 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1279 - accuracy: 0.9609 - val_loss: 0.2408 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1170 - accuracy: 0.9629 - val_loss: 0.2116 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.2266 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0989 - accuracy: 0.9722 - val_loss: 0.2566 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 174: 298.38 sec -Time taken for epoch(SUBo) 174: 242.96 sec -<---------------------------------------|Epoch [174] END|---------------------------------------> - -Epoch: 175/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1366 - accuracy: 0.9546 - val_loss: 0.2196 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1153 - accuracy: 0.9619 - val_loss: 0.2363 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1186 - accuracy: 0.9624 - val_loss: 0.2094 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9683 - val_loss: 0.2792 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0901 - accuracy: 0.9736 - val_loss: 0.2793 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0818 - accuracy: 0.9751 - val_loss: 0.3102 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 175: 298.34 sec -Time taken for epoch(SUBo) 175: 243.27 sec -<---------------------------------------|Epoch [175] END|---------------------------------------> - -Epoch: 176/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1217 - accuracy: 0.9561 - val_loss: 0.3390 - val_accuracy: 0.8894 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1363 - accuracy: 0.9600 - val_loss: 0.3365 - val_accuracy: 0.9151 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9580 - val_loss: 0.2768 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1262 - accuracy: 0.9629 - val_loss: 0.2921 - val_accuracy: 0.9135 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0952 - accuracy: 0.9717 - val_loss: 0.3173 - val_accuracy: 0.9151 -Epoch 6/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0972 - accuracy: 0.9731 - val_loss: 0.3247 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 176: 300.75 sec -Time taken for epoch(SUBo) 176: 244.46 sec -<---------------------------------------|Epoch [176] END|---------------------------------------> - -Epoch: 177/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1301 - accuracy: 0.9600 - val_loss: 0.2746 - val_accuracy: 0.9215 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1191 - accuracy: 0.9658 - val_loss: 0.2657 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1160 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0987 - accuracy: 0.9722 - val_loss: 0.2429 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0863 - accuracy: 0.9756 - val_loss: 0.2320 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0852 - accuracy: 0.9771 - val_loss: 0.2548 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 177: 307.13 sec -Time taken for epoch(SUBo) 177: 245.28 sec -<---------------------------------------|Epoch [177] END|---------------------------------------> - -Epoch: 178/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1285 - accuracy: 0.9634 - val_loss: 0.1938 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1361 - accuracy: 0.9551 - val_loss: 0.2198 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1310 - accuracy: 0.9614 - val_loss: 0.2257 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1178 - accuracy: 0.9658 - val_loss: 0.1883 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1097 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9697 - val_loss: 0.2949 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 178: 307.31 sec -Time taken for epoch(SUBo) 178: 246.17 sec -<---------------------------------------|Epoch [178] END|---------------------------------------> - -Epoch: 179/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1366 - accuracy: 0.9551 - val_loss: 0.2232 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1883 - accuracy: 0.9370 - val_loss: 0.2155 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1590 - accuracy: 0.9492 - val_loss: 0.2392 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1456 - accuracy: 0.9517 - val_loss: 0.2673 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2418 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1098 - accuracy: 0.9658 - val_loss: 0.2398 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 179: 304.66 sec -Time taken for epoch(SUBo) 179: 246.00 sec -<---------------------------------------|Epoch [179] END|---------------------------------------> - -Epoch: 180/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1470 - accuracy: 0.9546 - val_loss: 0.2427 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1592 - accuracy: 0.9521 - val_loss: 0.3052 - val_accuracy: 0.9103 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1297 - accuracy: 0.9629 - val_loss: 0.2849 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9551 - val_loss: 0.2115 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1155 - accuracy: 0.9644 - val_loss: 0.2489 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1184 - accuracy: 0.9648 - val_loss: 0.2458 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 180: 303.24 sec -Time taken for epoch(SUBo) 180: 245.01 sec -<---------------------------------------|Epoch [180] END|---------------------------------------> - -Epoch: 181/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1431 - accuracy: 0.9556 - val_loss: 0.2670 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1354 - accuracy: 0.9580 - val_loss: 0.3152 - val_accuracy: 0.9071 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.2952 - val_accuracy: 0.9054 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.3917 - val_accuracy: 0.8958 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0896 - accuracy: 0.9756 - val_loss: 0.3502 - val_accuracy: 0.8990 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0898 - accuracy: 0.9707 - val_loss: 0.3361 - val_accuracy: 0.9071 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 181: 302.34 sec -Time taken for epoch(SUBo) 181: 244.62 sec -<---------------------------------------|Epoch [181] END|---------------------------------------> - -Epoch: 182/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2772 - val_accuracy: 0.9247 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1336 - accuracy: 0.9600 - val_loss: 0.2250 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9644 - val_loss: 0.3103 - val_accuracy: 0.9135 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9731 - val_loss: 0.3044 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0906 - accuracy: 0.9702 - val_loss: 0.3051 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0863 - accuracy: 0.9731 - val_loss: 0.3318 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 182: 304.09 sec -Time taken for epoch(SUBo) 182: 245.03 sec -<---------------------------------------|Epoch [182] END|---------------------------------------> - -Epoch: 183/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1180 - accuracy: 0.9609 - val_loss: 0.3431 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2777 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1235 - accuracy: 0.9634 - val_loss: 0.1881 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0921 - accuracy: 0.9717 - val_loss: 0.2754 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9712 - val_loss: 0.3383 - val_accuracy: 0.9103 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0866 - accuracy: 0.9751 - val_loss: 0.3123 - val_accuracy: 0.9215 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 183: 304.60 sec -Time taken for epoch(SUBo) 183: 244.97 sec -<---------------------------------------|Epoch [183] END|---------------------------------------> - -Epoch: 184/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1436 - accuracy: 0.9565 - val_loss: 0.2403 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9575 - val_loss: 0.2531 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1325 - accuracy: 0.9531 - val_loss: 0.3488 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.2155 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2753 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1108 - accuracy: 0.9644 - val_loss: 0.2761 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 184: 304.30 sec -Time taken for epoch(SUBo) 184: 244.89 sec -<---------------------------------------|Epoch [184] END|---------------------------------------> - -Epoch: 185/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1250 - accuracy: 0.9619 - val_loss: 0.2633 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.2972 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1252 - accuracy: 0.9639 - val_loss: 0.2754 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1152 - accuracy: 0.9683 - val_loss: 0.2419 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.2478 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0871 - accuracy: 0.9736 - val_loss: 0.2475 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 185: 306.52 sec -Time taken for epoch(SUBo) 185: 245.42 sec -<---------------------------------------|Epoch [185] END|---------------------------------------> - -Epoch: 186/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1323 - accuracy: 0.9585 - val_loss: 0.2456 - val_accuracy: 0.9295 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1374 - accuracy: 0.9639 - val_loss: 0.2509 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1351 - accuracy: 0.9639 - val_loss: 0.2669 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1114 - accuracy: 0.9639 - val_loss: 0.2947 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0944 - accuracy: 0.9766 - val_loss: 0.2886 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0906 - accuracy: 0.9736 - val_loss: 0.2739 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 186: 307.26 sec -Time taken for epoch(SUBo) 186: 246.41 sec -<---------------------------------------|Epoch [186] END|---------------------------------------> - -Epoch: 187/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1226 - accuracy: 0.9644 - val_loss: 0.2625 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1695 - accuracy: 0.9453 - val_loss: 1.2514 - val_accuracy: 0.7115 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1965 - accuracy: 0.9336 - val_loss: 0.5935 - val_accuracy: 0.8429 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1654 - accuracy: 0.9458 - val_loss: 0.4132 - val_accuracy: 0.9054 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9551 - val_loss: 0.4170 - val_accuracy: 0.9038 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1316 - accuracy: 0.9595 - val_loss: 0.4311 - val_accuracy: 0.9022 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 187: 297.48 sec -Time taken for epoch(SUBo) 187: 244.18 sec -<---------------------------------------|Epoch [187] END|---------------------------------------> - -Epoch: 188/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1581 - accuracy: 0.9458 - val_loss: 0.3557 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1425 - accuracy: 0.9561 - val_loss: 0.3358 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1316 - accuracy: 0.9551 - val_loss: 0.3622 - val_accuracy: 0.9231 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1155 - accuracy: 0.9634 - val_loss: 0.3811 - val_accuracy: 0.9119 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1323 - accuracy: 0.9546 - val_loss: 0.3472 - val_accuracy: 0.9167 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1224 - accuracy: 0.9644 - val_loss: 0.3330 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 188: 299.49 sec -Time taken for epoch(SUBo) 188: 244.91 sec -<---------------------------------------|Epoch [188] END|---------------------------------------> - -Epoch: 189/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1436 - accuracy: 0.9517 - val_loss: 0.2752 - val_accuracy: 0.9279 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1421 - accuracy: 0.9531 - val_loss: 0.2516 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1263 - accuracy: 0.9600 - val_loss: 0.2514 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1058 - accuracy: 0.9663 - val_loss: 0.2660 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1131 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 189: 301.75 sec -Time taken for epoch(SUBo) 189: 245.44 sec -<---------------------------------------|Epoch [189] END|---------------------------------------> - -Epoch: 190/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1480 - accuracy: 0.9570 - val_loss: 0.1996 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1882 - accuracy: 0.9380 - val_loss: 0.2167 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1597 - accuracy: 0.9512 - val_loss: 0.2156 - val_accuracy: 0.9247 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1344 - accuracy: 0.9590 - val_loss: 0.2198 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1345 - accuracy: 0.9609 - val_loss: 0.2668 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1128 - accuracy: 0.9644 - val_loss: 0.2396 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 190: 300.24 sec -Time taken for epoch(SUBo) 190: 245.01 sec -<---------------------------------------|Epoch [190] END|---------------------------------------> - -Epoch: 191/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1471 - accuracy: 0.9570 - val_loss: 0.2358 - val_accuracy: 0.9279 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1378 - accuracy: 0.9551 - val_loss: 0.2055 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.1978 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1355 - accuracy: 0.9595 - val_loss: 0.1849 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1076 - accuracy: 0.9727 - val_loss: 0.2088 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1103 - accuracy: 0.9663 - val_loss: 0.1988 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 191: 301.30 sec -Time taken for epoch(SUBo) 191: 245.49 sec -<---------------------------------------|Epoch [191] END|---------------------------------------> - -Epoch: 192/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1421 - accuracy: 0.9575 - val_loss: 0.2050 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.3539 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1398 - accuracy: 0.9551 - val_loss: 0.2728 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1200 - accuracy: 0.9653 - val_loss: 0.2649 - val_accuracy: 0.9103 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9604 - val_loss: 0.2364 - val_accuracy: 0.9247 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1229 - accuracy: 0.9580 - val_loss: 0.2355 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 192: 300.20 sec -Time taken for epoch(SUBo) 192: 245.83 sec -<---------------------------------------|Epoch [192] END|---------------------------------------> - -Epoch: 193/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1544 - accuracy: 0.9478 - val_loss: 0.2400 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1478 - accuracy: 0.9507 - val_loss: 0.2931 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.2789 - val_accuracy: 0.9327 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9585 - val_loss: 0.2220 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1067 - accuracy: 0.9712 - val_loss: 0.2248 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0902 - accuracy: 0.9751 - val_loss: 0.2198 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 193: 298.24 sec -Time taken for epoch(SUBo) 193: 245.22 sec -<---------------------------------------|Epoch [193] END|---------------------------------------> - -Epoch: 194/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.2151 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1429 - accuracy: 0.9595 - val_loss: 0.2100 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.2180 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1083 - accuracy: 0.9683 - val_loss: 0.2342 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1105 - accuracy: 0.9683 - val_loss: 0.2624 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0829 - accuracy: 0.9741 - val_loss: 0.2530 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 194: 299.52 sec -Time taken for epoch(SUBo) 194: 245.69 sec -<---------------------------------------|Epoch [194] END|---------------------------------------> - -Epoch: 195/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1158 - accuracy: 0.9673 - val_loss: 0.2753 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1058 - accuracy: 0.9648 - val_loss: 0.2734 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0861 - accuracy: 0.9756 - val_loss: 0.2831 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0798 - accuracy: 0.9775 - val_loss: 0.2666 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0687 - accuracy: 0.9824 - val_loss: 0.3035 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 195: 299.74 sec -Time taken for epoch(SUBo) 195: 244.18 sec -<---------------------------------------|Epoch [195] END|---------------------------------------> - -Epoch: 196/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1189 - accuracy: 0.9604 - val_loss: 0.2907 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1172 - accuracy: 0.9658 - val_loss: 0.2743 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1052 - accuracy: 0.9663 - val_loss: 0.2412 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1047 - accuracy: 0.9653 - val_loss: 0.4034 - val_accuracy: 0.9006 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1205 - accuracy: 0.9580 - val_loss: 0.3797 - val_accuracy: 0.9199 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1042 - accuracy: 0.9678 - val_loss: 0.3400 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 196: 300.20 sec -Time taken for epoch(SUBo) 196: 246.37 sec -<---------------------------------------|Epoch [196] END|---------------------------------------> - -Epoch: 197/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1318 - accuracy: 0.9595 - val_loss: 0.2433 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1237 - accuracy: 0.9624 - val_loss: 0.2380 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9600 - val_loss: 0.2326 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2398 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0943 - accuracy: 0.9751 - val_loss: 0.2242 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9736 - val_loss: 0.2357 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 197: 297.68 sec -Time taken for epoch(SUBo) 197: 246.24 sec -<---------------------------------------|Epoch [197] END|---------------------------------------> - -Epoch: 198/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1174 - accuracy: 0.9658 - val_loss: 0.2696 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9575 - val_loss: 0.2424 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0977 - accuracy: 0.9707 - val_loss: 0.2852 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0980 - accuracy: 0.9688 - val_loss: 0.2780 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0881 - accuracy: 0.9736 - val_loss: 0.2471 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0809 - accuracy: 0.9751 - val_loss: 0.2606 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 198: 297.77 sec -Time taken for epoch(SUBo) 198: 246.78 sec -<---------------------------------------|Epoch [198] END|---------------------------------------> - -Epoch: 199/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1334 - accuracy: 0.9609 - val_loss: 0.2220 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1240 - accuracy: 0.9604 - val_loss: 0.2392 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1112 - accuracy: 0.9658 - val_loss: 0.2233 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1175 - accuracy: 0.9673 - val_loss: 0.2212 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1032 - accuracy: 0.9678 - val_loss: 0.2742 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1011 - accuracy: 0.9663 - val_loss: 0.2787 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 199: 298.50 sec -Time taken for epoch(SUBo) 199: 246.76 sec -<---------------------------------------|Epoch [199] END|---------------------------------------> - -Epoch: 200/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1285 - accuracy: 0.9580 - val_loss: 0.3062 - val_accuracy: 0.9103 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1324 - accuracy: 0.9570 - val_loss: 0.2178 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1266 - accuracy: 0.9624 - val_loss: 0.2289 - val_accuracy: 0.9327 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1193 - accuracy: 0.9565 - val_loss: 0.2471 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1040 - accuracy: 0.9673 - val_loss: 0.2422 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0873 - accuracy: 0.9741 - val_loss: 0.2505 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 200: 298.67 sec -Time taken for epoch(SUBo) 200: 246.74 sec -<---------------------------------------|Epoch [200] END|---------------------------------------> - -Epoch: 201/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1427 - accuracy: 0.9551 - val_loss: 0.2224 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1369 - accuracy: 0.9541 - val_loss: 0.2401 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1309 - accuracy: 0.9595 - val_loss: 0.2131 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1004 - accuracy: 0.9683 - val_loss: 0.2495 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0969 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0972 - accuracy: 0.9697 - val_loss: 0.2479 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 201: 297.63 sec -Time taken for epoch(SUBo) 201: 245.98 sec -<---------------------------------------|Epoch [201] END|---------------------------------------> - -Epoch: 202/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1129 - accuracy: 0.9663 - val_loss: 0.2707 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1298 - accuracy: 0.9600 - val_loss: 0.2119 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1173 - accuracy: 0.9644 - val_loss: 0.2111 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1074 - accuracy: 0.9712 - val_loss: 0.1881 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0924 - accuracy: 0.9702 - val_loss: 0.2089 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9805 - val_loss: 0.2168 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 202: 298.33 sec -Time taken for epoch(SUBo) 202: 246.64 sec -<---------------------------------------|Epoch [202] END|---------------------------------------> - -Epoch: 203/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1369 - accuracy: 0.9561 - val_loss: 0.2180 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1303 - accuracy: 0.9541 - val_loss: 0.2391 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1245 - accuracy: 0.9634 - val_loss: 0.2390 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1135 - accuracy: 0.9648 - val_loss: 0.2664 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0981 - accuracy: 0.9727 - val_loss: 0.2374 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9722 - val_loss: 0.2165 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 203: 296.86 sec -Time taken for epoch(SUBo) 203: 245.14 sec -<---------------------------------------|Epoch [203] END|---------------------------------------> - -Epoch: 204/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.2079 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1075 - accuracy: 0.9648 - val_loss: 0.2058 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0978 - accuracy: 0.9673 - val_loss: 0.2125 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1015 - accuracy: 0.9722 - val_loss: 0.2370 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0780 - accuracy: 0.9775 - val_loss: 0.2245 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0684 - accuracy: 0.9814 - val_loss: 0.2192 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 204: 298.03 sec -Time taken for epoch(SUBo) 204: 246.30 sec -<---------------------------------------|Epoch [204] END|---------------------------------------> - -Epoch: 205/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1153 - accuracy: 0.9614 - val_loss: 0.2277 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1168 - accuracy: 0.9629 - val_loss: 0.2214 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1209 - accuracy: 0.9629 - val_loss: 0.1874 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1025 - accuracy: 0.9692 - val_loss: 0.2265 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0891 - accuracy: 0.9766 - val_loss: 0.1875 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0753 - accuracy: 0.9805 - val_loss: 0.2138 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 205: 297.87 sec -Time taken for epoch(SUBo) 205: 245.90 sec -<---------------------------------------|Epoch [205] END|---------------------------------------> - -Epoch: 206/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1070 - accuracy: 0.9697 - val_loss: 0.2057 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1039 - accuracy: 0.9673 - val_loss: 0.2215 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0855 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.3037 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9766 - val_loss: 0.2560 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0760 - accuracy: 0.9766 - val_loss: 0.2418 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 206: 297.94 sec -Time taken for epoch(SUBo) 206: 246.21 sec -<---------------------------------------|Epoch [206] END|---------------------------------------> - -Epoch: 207/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1259 - accuracy: 0.9658 - val_loss: 0.2366 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1204 - accuracy: 0.9644 - val_loss: 0.2283 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1144 - accuracy: 0.9624 - val_loss: 0.1889 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0992 - accuracy: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0875 - accuracy: 0.9775 - val_loss: 0.2601 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0808 - accuracy: 0.9800 - val_loss: 0.2478 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 207: 298.66 sec -Time taken for epoch(SUBo) 207: 246.51 sec -<---------------------------------------|Epoch [207] END|---------------------------------------> - -Epoch: 208/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1141 - accuracy: 0.9648 - val_loss: 0.2134 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1996 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0942 - accuracy: 0.9697 - val_loss: 0.1941 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0885 - accuracy: 0.9741 - val_loss: 0.2165 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0837 - accuracy: 0.9741 - val_loss: 0.2150 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0726 - accuracy: 0.9829 - val_loss: 0.2024 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 208: 298.91 sec -Time taken for epoch(SUBo) 208: 246.69 sec -<---------------------------------------|Epoch [208] END|---------------------------------------> - -Epoch: 209/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1141 - accuracy: 0.9639 - val_loss: 0.2234 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9629 - val_loss: 0.2288 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1251 - accuracy: 0.9624 - val_loss: 0.2119 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.2092 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9678 - val_loss: 0.2370 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0761 - accuracy: 0.9771 - val_loss: 0.2383 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 209: 296.92 sec -Time taken for epoch(SUBo) 209: 245.42 sec -<---------------------------------------|Epoch [209] END|---------------------------------------> - -Epoch: 210/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1219 - accuracy: 0.9619 - val_loss: 0.2331 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.2102 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1038 - accuracy: 0.9658 - val_loss: 0.1857 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9727 - val_loss: 0.2113 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1070 - accuracy: 0.9668 - val_loss: 0.2461 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0851 - accuracy: 0.9766 - val_loss: 0.2336 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 210: 295.74 sec -Time taken for epoch(SUBo) 210: 245.46 sec -<---------------------------------------|Epoch [210] END|---------------------------------------> - -Epoch: 211/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1203 - accuracy: 0.9658 - val_loss: 0.1951 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9673 - val_loss: 0.1898 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0830 - accuracy: 0.9751 - val_loss: 0.1828 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0600 - accuracy: 0.9829 - val_loss: 0.2026 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0587 - accuracy: 0.9854 - val_loss: 0.1957 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 211: 295.87 sec -Time taken for epoch(SUBo) 211: 245.59 sec -<---------------------------------------|Epoch [211] END|---------------------------------------> - -Epoch: 212/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9746 - val_loss: 0.1699 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1037 - accuracy: 0.9673 - val_loss: 0.2054 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0907 - accuracy: 0.9731 - val_loss: 0.2072 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0802 - accuracy: 0.9771 - val_loss: 0.1906 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0749 - accuracy: 0.9814 - val_loss: 0.1856 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0661 - accuracy: 0.9824 - val_loss: 0.1860 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 212: 295.86 sec -Time taken for epoch(SUBo) 212: 245.69 sec -<---------------------------------------|Epoch [212] END|---------------------------------------> - -Epoch: 213/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1047 - accuracy: 0.9688 - val_loss: 0.1803 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0977 - accuracy: 0.9746 - val_loss: 0.1586 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9722 - val_loss: 0.1882 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1002 - accuracy: 0.9756 - val_loss: 0.2034 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0865 - accuracy: 0.9766 - val_loss: 0.2175 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0730 - accuracy: 0.9790 - val_loss: 0.2228 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 213: 295.87 sec -Time taken for epoch(SUBo) 213: 245.31 sec -<---------------------------------------|Epoch [213] END|---------------------------------------> - -Epoch: 214/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1225 - accuracy: 0.9619 - val_loss: 0.1941 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1188 - accuracy: 0.9658 - val_loss: 0.1750 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1020 - accuracy: 0.9644 - val_loss: 0.2022 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0990 - accuracy: 0.9668 - val_loss: 0.1984 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0992 - accuracy: 0.9722 - val_loss: 0.2096 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0697 - accuracy: 0.9814 - val_loss: 0.2177 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 214: 295.73 sec -Time taken for epoch(SUBo) 214: 245.40 sec -<---------------------------------------|Epoch [214] END|---------------------------------------> - -Epoch: 215/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2052 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1099 - accuracy: 0.9688 - val_loss: 0.2122 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0904 - accuracy: 0.9712 - val_loss: 0.2057 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0789 - accuracy: 0.9756 - val_loss: 0.2348 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0699 - accuracy: 0.9834 - val_loss: 0.2055 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.2412 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 215: 296.11 sec -Time taken for epoch(SUBo) 215: 245.32 sec -<---------------------------------------|Epoch [215] END|---------------------------------------> - -Epoch: 216/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1354 - accuracy: 0.9619 - val_loss: 1.9127 - val_accuracy: 0.6250 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.6524 - accuracy: 0.6860 - val_loss: 0.5187 - val_accuracy: 0.8253 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.4618 - accuracy: 0.8057 - val_loss: 0.4150 - val_accuracy: 0.9103 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3662 - accuracy: 0.8638 - val_loss: 0.2908 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3131 - accuracy: 0.8921 - val_loss: 0.3339 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2602 - accuracy: 0.9121 - val_loss: 0.3118 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 216: 294.77 sec -Time taken for epoch(SUBo) 216: 244.51 sec -<---------------------------------------|Epoch [216] END|---------------------------------------> - -Epoch: 217/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.3051 - accuracy: 0.8945 - val_loss: 0.2281 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2581 - accuracy: 0.9053 - val_loss: 0.2585 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2153 - accuracy: 0.9385 - val_loss: 0.1958 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1935 - accuracy: 0.9463 - val_loss: 0.1896 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1759 - accuracy: 0.9492 - val_loss: 0.2038 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.2104 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 217: 295.94 sec -Time taken for epoch(SUBo) 217: 245.61 sec -<---------------------------------------|Epoch [217] END|---------------------------------------> - -Epoch: 218/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2018 - accuracy: 0.9331 - val_loss: 0.2546 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1969 - accuracy: 0.9355 - val_loss: 0.2012 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1747 - accuracy: 0.9453 - val_loss: 0.1932 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1722 - accuracy: 0.9507 - val_loss: 0.2019 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1437 - accuracy: 0.9536 - val_loss: 0.2124 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1302 - accuracy: 0.9609 - val_loss: 0.2347 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 218: 295.43 sec -Time taken for epoch(SUBo) 218: 245.14 sec -<---------------------------------------|Epoch [218] END|---------------------------------------> - -Epoch: 219/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1756 - accuracy: 0.9478 - val_loss: 0.1971 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1803 - accuracy: 0.9414 - val_loss: 0.1779 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1618 - accuracy: 0.9424 - val_loss: 0.2014 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1546 - accuracy: 0.9600 - val_loss: 0.2209 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1289 - accuracy: 0.9639 - val_loss: 0.2224 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 219: 293.27 sec -Time taken for epoch(SUBo) 219: 243.09 sec -<---------------------------------------|Epoch [219] END|---------------------------------------> - -Epoch: 220/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1384 - accuracy: 0.9580 - val_loss: 0.1899 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1636 - accuracy: 0.9497 - val_loss: 0.1965 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9561 - val_loss: 0.1807 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1192 - accuracy: 0.9629 - val_loss: 0.2034 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1268 - accuracy: 0.9585 - val_loss: 0.1812 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.1890 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 220: 289.19 sec -Time taken for epoch(SUBo) 220: 239.59 sec -<---------------------------------------|Epoch [220] END|---------------------------------------> - -Epoch: 221/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1293 - accuracy: 0.9604 - val_loss: 0.2001 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1258 - accuracy: 0.9629 - val_loss: 0.2138 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9629 - val_loss: 0.2242 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1100 - accuracy: 0.9712 - val_loss: 0.2425 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1082 - accuracy: 0.9712 - val_loss: 0.2177 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0903 - accuracy: 0.9751 - val_loss: 0.2145 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 221: 303.15 sec -Time taken for epoch(SUBo) 221: 246.93 sec -<---------------------------------------|Epoch [221] END|---------------------------------------> - -Epoch: 222/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2076 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1585 - accuracy: 0.9556 - val_loss: 0.2135 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1446 - accuracy: 0.9575 - val_loss: 0.2137 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1215 - accuracy: 0.9663 - val_loss: 0.2196 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1310 - accuracy: 0.9609 - val_loss: 0.2567 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1038 - accuracy: 0.9727 - val_loss: 0.2416 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 222: 299.03 sec -Time taken for epoch(SUBo) 222: 248.17 sec -<---------------------------------------|Epoch [222] END|---------------------------------------> - -Epoch: 223/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 165ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2650 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 42s 165ms/step - loss: 0.1193 - accuracy: 0.9570 - val_loss: 0.1668 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.1817 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.2031 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 43s 166ms/step - loss: 0.0876 - accuracy: 0.9751 - val_loss: 0.1877 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.0826 - accuracy: 0.9766 - val_loss: 0.1862 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 223: 312.69 sec -Time taken for epoch(SUBo) 223: 256.34 sec -<---------------------------------------|Epoch [223] END|---------------------------------------> - -Epoch: 224/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1238 - accuracy: 0.9668 - val_loss: 0.1797 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1198 - accuracy: 0.9624 - val_loss: 0.1924 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1028 - accuracy: 0.9712 - val_loss: 0.2374 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1065 - accuracy: 0.9722 - val_loss: 0.2279 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0899 - accuracy: 0.9771 - val_loss: 0.1902 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9795 - val_loss: 0.1907 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 224: 301.18 sec -Time taken for epoch(SUBo) 224: 248.32 sec -<---------------------------------------|Epoch [224] END|---------------------------------------> - -Epoch: 225/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1296 - accuracy: 0.9609 - val_loss: 0.1972 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.1699 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1233 - accuracy: 0.9624 - val_loss: 0.2114 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1953 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0820 - accuracy: 0.9780 - val_loss: 0.2077 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0815 - accuracy: 0.9814 - val_loss: 0.2196 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 225: 302.66 sec -Time taken for epoch(SUBo) 225: 248.69 sec -<---------------------------------------|Epoch [225] END|---------------------------------------> - -Epoch: 226/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1353 - accuracy: 0.9604 - val_loss: 0.2359 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.2577 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2211 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1084 - accuracy: 0.9707 - val_loss: 0.1719 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1007 - accuracy: 0.9712 - val_loss: 0.1720 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0895 - accuracy: 0.9751 - val_loss: 0.1756 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 226: 313.16 sec -Time taken for epoch(SUBo) 226: 251.41 sec -<---------------------------------------|Epoch [226] END|---------------------------------------> - -Epoch: 227/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1123 - accuracy: 0.9639 - val_loss: 0.1721 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1115 - accuracy: 0.9653 - val_loss: 0.2263 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1077 - accuracy: 0.9639 - val_loss: 0.1975 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0943 - accuracy: 0.9717 - val_loss: 0.2010 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0949 - accuracy: 0.9736 - val_loss: 0.1780 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0812 - accuracy: 0.9771 - val_loss: 0.1900 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 227: 306.72 sec -Time taken for epoch(SUBo) 227: 248.78 sec -<---------------------------------------|Epoch [227] END|---------------------------------------> - -Epoch: 228/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1398 - accuracy: 0.9546 - val_loss: 0.1847 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1483 - accuracy: 0.9551 - val_loss: 0.1827 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1162 - accuracy: 0.9678 - val_loss: 0.2110 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1037 - accuracy: 0.9639 - val_loss: 0.1890 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0836 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.0876 - accuracy: 0.9746 - val_loss: 0.1758 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 228: 307.11 sec -Time taken for epoch(SUBo) 228: 249.60 sec -<---------------------------------------|Epoch [228] END|---------------------------------------> - -Epoch: 229/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 162ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.1633 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1031 - accuracy: 0.9702 - val_loss: 0.1893 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1045 - accuracy: 0.9717 - val_loss: 0.1849 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0950 - accuracy: 0.9780 - val_loss: 0.1626 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0764 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0747 - accuracy: 0.9795 - val_loss: 0.1604 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 229: 302.46 sec -Time taken for epoch(SUBo) 229: 249.29 sec -<---------------------------------------|Epoch [229] END|---------------------------------------> - -Epoch: 230/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1203 - accuracy: 0.9648 - val_loss: 0.1849 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1080 - accuracy: 0.9639 - val_loss: 0.1861 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0951 - accuracy: 0.9697 - val_loss: 0.2135 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1004 - accuracy: 0.9712 - val_loss: 0.2054 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0709 - accuracy: 0.9771 - val_loss: 0.2113 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0771 - accuracy: 0.9766 - val_loss: 0.2083 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 230: 299.92 sec -Time taken for epoch(SUBo) 230: 248.71 sec -<---------------------------------------|Epoch [230] END|---------------------------------------> - -Epoch: 231/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 167ms/step - loss: 0.0909 - accuracy: 0.9707 - val_loss: 0.1829 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0896 - accuracy: 0.9746 - val_loss: 0.1859 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0809 - accuracy: 0.9766 - val_loss: 0.1953 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0791 - accuracy: 0.9780 - val_loss: 0.1783 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0703 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0497 - accuracy: 0.9863 - val_loss: 0.1703 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 231: 304.17 sec -Time taken for epoch(SUBo) 231: 253.03 sec -<---------------------------------------|Epoch [231] END|---------------------------------------> - -Epoch: 232/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 165ms/step - loss: 0.1107 - accuracy: 0.9683 - val_loss: 0.1718 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.1952 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1595 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0998 - accuracy: 0.9722 - val_loss: 0.1685 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.0773 - accuracy: 0.9795 - val_loss: 0.2042 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0897 - accuracy: 0.9780 - val_loss: 0.1887 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 232: 312.43 sec -Time taken for epoch(SUBo) 232: 254.46 sec -<---------------------------------------|Epoch [232] END|---------------------------------------> - -Epoch: 233/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 48s 167ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.1891 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.1052 - accuracy: 0.9688 - val_loss: 0.1659 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.1140 - accuracy: 0.9688 - val_loss: 0.1445 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0954 - accuracy: 0.9717 - val_loss: 0.1710 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.0933 - accuracy: 0.9761 - val_loss: 0.1612 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0744 - accuracy: 0.9814 - val_loss: 0.1741 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 233: 313.69 sec -Time taken for epoch(SUBo) 233: 256.06 sec -<---------------------------------------|Epoch [233] END|---------------------------------------> - -Epoch: 234/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 167ms/step - loss: 0.1023 - accuracy: 0.9683 - val_loss: 0.1438 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 42s 162ms/step - loss: 0.0962 - accuracy: 0.9707 - val_loss: 0.2408 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 42s 162ms/step - loss: 0.0875 - accuracy: 0.9736 - val_loss: 0.1795 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.0846 - accuracy: 0.9722 - val_loss: 0.1669 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0591 - accuracy: 0.9844 - val_loss: 0.1704 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0565 - accuracy: 0.9873 - val_loss: 0.1818 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 234: 311.13 sec -Time taken for epoch(SUBo) 234: 255.01 sec -<---------------------------------------|Epoch [234] END|---------------------------------------> - -Epoch: 235/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1210 - accuracy: 0.9629 - val_loss: 0.1778 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1125 - accuracy: 0.9663 - val_loss: 0.1453 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.1608 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0843 - accuracy: 0.9775 - val_loss: 0.1615 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.1832 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0672 - accuracy: 0.9800 - val_loss: 0.1808 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 235: 300.21 sec -Time taken for epoch(SUBo) 235: 251.11 sec -<---------------------------------------|Epoch [235] END|---------------------------------------> - -Epoch: 236/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1133 - accuracy: 0.9639 - val_loss: 0.1626 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0993 - accuracy: 0.9648 - val_loss: 0.1585 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0924 - accuracy: 0.9717 - val_loss: 0.1581 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1336 - val_accuracy: 0.9583 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0724 - accuracy: 0.9790 - val_loss: 0.1694 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0585 - accuracy: 0.9839 - val_loss: 0.1735 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 236: 302.45 sec -Time taken for epoch(SUBo) 236: 251.67 sec -<---------------------------------------|Epoch [236] END|---------------------------------------> - -Epoch: 237/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1015 - accuracy: 0.9663 - val_loss: 0.1594 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0919 - accuracy: 0.9736 - val_loss: 0.1593 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0886 - accuracy: 0.9746 - val_loss: 0.1714 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0809 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0690 - accuracy: 0.9829 - val_loss: 0.2800 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0600 - accuracy: 0.9873 - val_loss: 0.2560 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 237: 301.88 sec -Time taken for epoch(SUBo) 237: 251.53 sec -<---------------------------------------|Epoch [237] END|---------------------------------------> - -Epoch: 238/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 166ms/step - loss: 0.1051 - accuracy: 0.9663 - val_loss: 0.2133 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0934 - accuracy: 0.9717 - val_loss: 0.2560 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0785 - accuracy: 0.9790 - val_loss: 0.2045 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0702 - accuracy: 0.9790 - val_loss: 0.2433 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0706 - accuracy: 0.9800 - val_loss: 0.1769 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0689 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 238: 307.10 sec -Time taken for epoch(SUBo) 238: 252.62 sec -<---------------------------------------|Epoch [238] END|---------------------------------------> - -Epoch: 239/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1147 - accuracy: 0.9673 - val_loss: 0.1823 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0958 - accuracy: 0.9751 - val_loss: 0.2081 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0865 - accuracy: 0.9775 - val_loss: 0.2058 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0716 - accuracy: 0.9795 - val_loss: 0.2068 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2146 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.0562 - accuracy: 0.9834 - val_loss: 0.2186 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 239: 303.13 sec -Time taken for epoch(SUBo) 239: 249.48 sec -<---------------------------------------|Epoch [239] END|---------------------------------------> - -Epoch: 240/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1219 - accuracy: 0.9595 - val_loss: 0.1957 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1010 - accuracy: 0.9717 - val_loss: 0.2189 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0829 - accuracy: 0.9756 - val_loss: 0.2015 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0715 - accuracy: 0.9780 - val_loss: 0.2191 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0614 - accuracy: 0.9839 - val_loss: 0.2335 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0522 - accuracy: 0.9858 - val_loss: 0.2491 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 240: 296.96 sec -Time taken for epoch(SUBo) 240: 247.49 sec -<---------------------------------------|Epoch [240] END|---------------------------------------> - -Epoch: 241/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0874 - accuracy: 0.9731 - val_loss: 0.2011 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0942 - accuracy: 0.9731 - val_loss: 0.1900 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0867 - accuracy: 0.9731 - val_loss: 0.2119 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0814 - accuracy: 0.9727 - val_loss: 0.2344 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0619 - accuracy: 0.9834 - val_loss: 0.2379 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0528 - accuracy: 0.9868 - val_loss: 0.2390 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 241: 301.50 sec -Time taken for epoch(SUBo) 241: 244.85 sec -<---------------------------------------|Epoch [241] END|---------------------------------------> - -Epoch: 242/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 157ms/step - loss: 0.1068 - accuracy: 0.9692 - val_loss: 0.2088 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.0962 - accuracy: 0.9692 - val_loss: 0.2827 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.2028 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0831 - accuracy: 0.9761 - val_loss: 0.2217 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0908 - accuracy: 0.9775 - val_loss: 0.2048 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0678 - accuracy: 0.9814 - val_loss: 0.1931 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 242: 289.32 sec -Time taken for epoch(SUBo) 242: 241.25 sec -<---------------------------------------|Epoch [242] END|---------------------------------------> - -Epoch: 243/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 158ms/step - loss: 0.1125 - accuracy: 0.9692 - val_loss: 0.1588 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0962 - accuracy: 0.9668 - val_loss: 0.1660 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0947 - accuracy: 0.9717 - val_loss: 0.2053 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0780 - accuracy: 0.9756 - val_loss: 0.1659 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0762 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0544 - accuracy: 0.9844 - val_loss: 0.1827 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 243: 289.77 sec -Time taken for epoch(SUBo) 243: 242.82 sec -<---------------------------------------|Epoch [243] END|---------------------------------------> - -Epoch: 244/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1976 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0864 - accuracy: 0.9775 - val_loss: 0.2101 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1914 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2286 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0735 - accuracy: 0.9819 - val_loss: 0.2039 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0471 - accuracy: 0.9897 - val_loss: 0.2055 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 244: 292.32 sec -Time taken for epoch(SUBo) 244: 245.77 sec -<---------------------------------------|Epoch [244] END|---------------------------------------> - -Epoch: 245/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1215 - accuracy: 0.9648 - val_loss: 0.1895 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1283 - accuracy: 0.9629 - val_loss: 0.1734 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0933 - accuracy: 0.9731 - val_loss: 0.1550 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1631 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0857 - accuracy: 0.9731 - val_loss: 0.1576 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0706 - accuracy: 0.9824 - val_loss: 0.1603 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 245: 293.35 sec -Time taken for epoch(SUBo) 245: 246.05 sec -<---------------------------------------|Epoch [245] END|---------------------------------------> - -Epoch: 246/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0914 - accuracy: 0.9771 - val_loss: 0.1657 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9697 - val_loss: 0.1844 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9756 - val_loss: 0.1675 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0649 - accuracy: 0.9800 - val_loss: 0.1947 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0551 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0458 - accuracy: 0.9897 - val_loss: 0.1977 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 246: 292.03 sec -Time taken for epoch(SUBo) 246: 245.80 sec -<---------------------------------------|Epoch [246] END|---------------------------------------> - -Epoch: 247/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.0973 - accuracy: 0.9727 - val_loss: 0.1630 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0989 - accuracy: 0.9702 - val_loss: 0.1590 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0671 - accuracy: 0.9800 - val_loss: 0.1650 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0731 - accuracy: 0.9805 - val_loss: 0.1396 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9854 - val_loss: 0.1649 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0560 - accuracy: 0.9883 - val_loss: 0.1677 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 247: 294.16 sec -Time taken for epoch(SUBo) 247: 247.01 sec -<---------------------------------------|Epoch [247] END|---------------------------------------> - -Epoch: 248/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0900 - accuracy: 0.9756 - val_loss: 0.1515 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9761 - val_loss: 0.1617 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0630 - accuracy: 0.9858 - val_loss: 0.1660 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0626 - accuracy: 0.9834 - val_loss: 0.1958 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0587 - accuracy: 0.9849 - val_loss: 0.1824 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 248: 293.13 sec -Time taken for epoch(SUBo) 248: 246.15 sec -<---------------------------------------|Epoch [248] END|---------------------------------------> - -Epoch: 249/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.1567 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1036 - accuracy: 0.9717 - val_loss: 0.1435 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0755 - accuracy: 0.9780 - val_loss: 0.1969 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0742 - accuracy: 0.9775 - val_loss: 0.1623 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0557 - accuracy: 0.9844 - val_loss: 0.1914 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 249: 293.33 sec -Time taken for epoch(SUBo) 249: 245.80 sec -<---------------------------------------|Epoch [249] END|---------------------------------------> - -Epoch: 250/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1097 - accuracy: 0.9658 - val_loss: 0.1761 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0999 - accuracy: 0.9697 - val_loss: 0.1736 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0943 - accuracy: 0.9673 - val_loss: 0.1766 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.1743 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0821 - accuracy: 0.9727 - val_loss: 0.1941 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0683 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 250: 292.13 sec -Time taken for epoch(SUBo) 250: 244.59 sec -<---------------------------------------|Epoch [250] END|---------------------------------------> - -Epoch: 251/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9707 - val_loss: 0.1764 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0835 - accuracy: 0.9736 - val_loss: 0.1675 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9785 - val_loss: 0.1513 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0704 - accuracy: 0.9800 - val_loss: 0.1564 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0581 - accuracy: 0.9839 - val_loss: 0.1602 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0579 - accuracy: 0.9849 - val_loss: 0.1547 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 251: 292.96 sec -Time taken for epoch(SUBo) 251: 246.01 sec -<---------------------------------------|Epoch [251] END|---------------------------------------> - -Epoch: 252/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0999 - accuracy: 0.9707 - val_loss: 0.1387 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0826 - accuracy: 0.9756 - val_loss: 0.1897 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0722 - accuracy: 0.9775 - val_loss: 0.1514 - val_accuracy: 0.9615 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.1432 - val_accuracy: 0.9599 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0720 - accuracy: 0.9814 - val_loss: 0.1414 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0650 - accuracy: 0.9795 - val_loss: 0.1418 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.146798238158226 to 0.14178654551506042. Saving model. -Time taken for epoch(FULL) 252: 295.30 sec -Time taken for epoch(SUBo) 252: 246.41 sec -<---------------------------------------|Epoch [252] END|---------------------------------------> - -Epoch: 253/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1538 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0866 - accuracy: 0.9761 - val_loss: 0.1447 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0777 - accuracy: 0.9800 - val_loss: 0.1519 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0644 - accuracy: 0.9829 - val_loss: 0.1863 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0585 - accuracy: 0.9868 - val_loss: 0.1939 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0511 - accuracy: 0.9878 - val_loss: 0.1766 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 253: 293.56 sec -Time taken for epoch(SUBo) 253: 246.59 sec -<---------------------------------------|Epoch [253] END|---------------------------------------> - -Epoch: 254/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1089 - accuracy: 0.9673 - val_loss: 0.1512 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0968 - accuracy: 0.9653 - val_loss: 0.1482 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0950 - accuracy: 0.9658 - val_loss: 0.1955 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0852 - accuracy: 0.9756 - val_loss: 0.1505 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0796 - accuracy: 0.9795 - val_loss: 0.1484 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0683 - accuracy: 0.9810 - val_loss: 0.1534 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 254: 293.79 sec -Time taken for epoch(SUBo) 254: 246.40 sec -<---------------------------------------|Epoch [254] END|---------------------------------------> - -Epoch: 255/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0860 - accuracy: 0.9746 - val_loss: 0.1747 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9727 - val_loss: 0.1806 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0816 - accuracy: 0.9756 - val_loss: 0.1677 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9834 - val_loss: 0.1808 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0564 - accuracy: 0.9844 - val_loss: 0.2127 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0513 - accuracy: 0.9883 - val_loss: 0.1953 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 255: 293.85 sec -Time taken for epoch(SUBo) 255: 246.35 sec -<---------------------------------------|Epoch [255] END|---------------------------------------> -Training done. - +Training the model... + +Epoch: 1/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 55s 166ms/step - loss: 20.4375 - accuracy: 0.6216 - val_loss: 15.7907 - val_accuracy: 0.8157 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 10.5470 - accuracy: 0.7383 - val_loss: 6.2110 - val_accuracy: 0.8205 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 4.1127 - accuracy: 0.7842 - val_loss: 2.5556 - val_accuracy: 0.8702 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 1.8795 - accuracy: 0.8096 - val_loss: 1.2612 - val_accuracy: 0.8718 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 1.0468 - accuracy: 0.8398 - val_loss: 0.8444 - val_accuracy: 0.8686 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.7275 - accuracy: 0.8555 - val_loss: 0.6163 - val_accuracy: 0.8766 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.5332 - accuracy: 0.8926 - val_loss: 0.5743 - val_accuracy: 0.8878 +Epoch 8/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.4697 - accuracy: 0.8979 - val_loss: 0.5413 - val_accuracy: 0.8702 +Subset training done. +Improved model accuracy from 0 to 0.870192289352417. Saving model. +Improved model loss from inf to 0.5412302017211914. Saving model. +Time taken for epoch(FULL) 1: 386.46 sec +Time taken for epoch(SUBo) 1: 333.70 sec +<---------------------------------------|Epoch [1] END|---------------------------------------> + +Epoch: 2/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.5620 - accuracy: 0.8359 - val_loss: 0.5067 - val_accuracy: 0.8446 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.5338 - accuracy: 0.8403 - val_loss: 0.6622 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.5047 - accuracy: 0.8418 - val_loss: 0.3689 - val_accuracy: 0.8926 +Epoch 4/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.4192 - accuracy: 0.8638 - val_loss: 0.4566 - val_accuracy: 0.8686 +Epoch 5/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.4020 - accuracy: 0.8677 - val_loss: 0.3214 - val_accuracy: 0.8670 +Epoch 6/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3681 - accuracy: 0.8813 - val_loss: 0.3148 - val_accuracy: 0.9199 +Epoch 7/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3198 - accuracy: 0.8931 - val_loss: 0.2567 - val_accuracy: 0.9279 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2715 - accuracy: 0.9180 - val_loss: 0.2393 - val_accuracy: 0.9311 +Subset training done. +Improved model accuracy from 0.870192289352417 to 0.9310897588729858. Saving model. +Improved model loss from 0.5412302017211914 to 0.23925769329071045. Saving model. +Time taken for epoch(FULL) 2: 381.03 sec +Time taken for epoch(SUBo) 2: 325.77 sec +<---------------------------------------|Epoch [2] END|---------------------------------------> + +Epoch: 3/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 161ms/step - loss: 0.3640 - accuracy: 0.8696 - val_loss: 0.3126 - val_accuracy: 0.9247 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3588 - accuracy: 0.8735 - val_loss: 0.3768 - val_accuracy: 0.9295 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3830 - accuracy: 0.8730 - val_loss: 0.4670 - val_accuracy: 0.9391 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3658 - accuracy: 0.8887 - val_loss: 0.2308 - val_accuracy: 0.9359 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3717 - accuracy: 0.8779 - val_loss: 0.2747 - val_accuracy: 0.9199 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3136 - accuracy: 0.9028 - val_loss: 0.3153 - val_accuracy: 0.9022 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2696 - accuracy: 0.9136 - val_loss: 0.2452 - val_accuracy: 0.9247 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2407 - accuracy: 0.9243 - val_loss: 0.2541 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9310897588729858. Not saving model. +Model loss did not improve from 0.23925769329071045. Not saving model. +Time taken for epoch(FULL) 3: 376.52 sec +Time taken for epoch(SUBo) 3: 324.58 sec +<---------------------------------------|Epoch [3] END|---------------------------------------> + +Epoch: 4/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.3534 - accuracy: 0.8784 - val_loss: 0.2325 - val_accuracy: 0.9215 +Epoch 2/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3861 - accuracy: 0.8584 - val_loss: 0.4468 - val_accuracy: 0.9103 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3696 - accuracy: 0.8765 - val_loss: 0.4794 - val_accuracy: 0.9038 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3680 - accuracy: 0.8828 - val_loss: 0.2781 - val_accuracy: 0.9231 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2897 - accuracy: 0.9165 - val_loss: 0.2823 - val_accuracy: 0.9327 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2801 - accuracy: 0.9165 - val_loss: 0.2447 - val_accuracy: 0.9071 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2460 - accuracy: 0.9326 - val_loss: 0.2840 - val_accuracy: 0.9359 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1982 - accuracy: 0.9414 - val_loss: 0.2283 - val_accuracy: 0.9343 +Subset training done. +Improved model accuracy from 0.9310897588729858 to 0.9342948794364929. Saving model. +Improved model loss from 0.23925769329071045 to 0.22827185690402985. Saving model. +Time taken for epoch(FULL) 4: 379.86 sec +Time taken for epoch(SUBo) 4: 325.33 sec +<---------------------------------------|Epoch [4] END|---------------------------------------> + +Epoch: 5/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.3226 - accuracy: 0.8950 - val_loss: 0.3022 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3538 - accuracy: 0.8862 - val_loss: 0.3310 - val_accuracy: 0.9279 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3201 - accuracy: 0.8892 - val_loss: 0.2884 - val_accuracy: 0.9071 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3229 - accuracy: 0.9009 - val_loss: 0.5201 - val_accuracy: 0.7340 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3079 - accuracy: 0.8926 - val_loss: 0.2863 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2670 - accuracy: 0.9141 - val_loss: 0.2587 - val_accuracy: 0.9151 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2639 - accuracy: 0.9209 - val_loss: 0.2800 - val_accuracy: 0.9054 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1925 - accuracy: 0.9541 - val_loss: 0.2547 - val_accuracy: 0.9087 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 5: 375.38 sec +Time taken for epoch(SUBo) 5: 323.81 sec +<---------------------------------------|Epoch [5] END|---------------------------------------> + +Epoch: 6/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2908 - accuracy: 0.8994 - val_loss: 0.3886 - val_accuracy: 0.9151 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2973 - accuracy: 0.8984 - val_loss: 0.4025 - val_accuracy: 0.7917 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3093 - accuracy: 0.8945 - val_loss: 0.9113 - val_accuracy: 0.6907 +Epoch 4/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2903 - accuracy: 0.9048 - val_loss: 0.3253 - val_accuracy: 0.8766 +Epoch 5/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2965 - accuracy: 0.9131 - val_loss: 0.3971 - val_accuracy: 0.8798 +Epoch 6/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2341 - accuracy: 0.9238 - val_loss: 0.3240 - val_accuracy: 0.9071 +Epoch 7/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2202 - accuracy: 0.9316 - val_loss: 0.3072 - val_accuracy: 0.9151 +Epoch 8/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.1613 - accuracy: 0.9614 - val_loss: 0.3554 - val_accuracy: 0.9167 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 6: 377.12 sec +Time taken for epoch(SUBo) 6: 325.55 sec +<---------------------------------------|Epoch [6] END|---------------------------------------> + +Epoch: 7/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 161ms/step - loss: 0.2840 - accuracy: 0.9102 - val_loss: 0.3625 - val_accuracy: 0.8878 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3095 - accuracy: 0.9033 - val_loss: 0.3963 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3532 - accuracy: 0.8887 - val_loss: 0.2555 - val_accuracy: 0.9263 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3018 - accuracy: 0.8979 - val_loss: 0.2644 - val_accuracy: 0.9375 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3154 - accuracy: 0.9048 - val_loss: 0.4598 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2509 - accuracy: 0.9312 - val_loss: 0.2478 - val_accuracy: 0.9295 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1902 - accuracy: 0.9478 - val_loss: 0.2697 - val_accuracy: 0.9311 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1628 - accuracy: 0.9531 - val_loss: 0.2426 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 7: 376.09 sec +Time taken for epoch(SUBo) 7: 324.32 sec +<---------------------------------------|Epoch [7] END|---------------------------------------> + +Epoch: 8/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2691 - accuracy: 0.9106 - val_loss: 0.4805 - val_accuracy: 0.9071 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3014 - accuracy: 0.8994 - val_loss: 0.2715 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3387 - accuracy: 0.8818 - val_loss: 0.3345 - val_accuracy: 0.8542 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3069 - accuracy: 0.9072 - val_loss: 0.3671 - val_accuracy: 0.9359 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2874 - accuracy: 0.9058 - val_loss: 0.2579 - val_accuracy: 0.9343 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2255 - accuracy: 0.9399 - val_loss: 0.3501 - val_accuracy: 0.9375 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1835 - accuracy: 0.9492 - val_loss: 0.2757 - val_accuracy: 0.9407 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1739 - accuracy: 0.9492 - val_loss: 0.2712 - val_accuracy: 0.9391 +Subset training done. +Improved model accuracy from 0.9342948794364929 to 0.9391025900840759. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 8: 377.68 sec +Time taken for epoch(SUBo) 8: 324.30 sec +<---------------------------------------|Epoch [8] END|---------------------------------------> + +Epoch: 9/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2921 - accuracy: 0.9077 - val_loss: 0.3537 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2866 - accuracy: 0.9082 - val_loss: 0.3213 - val_accuracy: 0.9359 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2978 - accuracy: 0.8999 - val_loss: 0.3623 - val_accuracy: 0.9199 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2635 - accuracy: 0.9209 - val_loss: 0.4593 - val_accuracy: 0.8942 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2444 - accuracy: 0.9287 - val_loss: 0.3207 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2366 - accuracy: 0.9277 - val_loss: 0.3259 - val_accuracy: 0.9167 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1802 - accuracy: 0.9478 - val_loss: 0.3234 - val_accuracy: 0.9231 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9648 - val_loss: 0.2856 - val_accuracy: 0.9247 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 9: 373.86 sec +Time taken for epoch(SUBo) 9: 323.15 sec +<---------------------------------------|Epoch [9] END|---------------------------------------> + +Epoch: 10/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2630 - accuracy: 0.9165 - val_loss: 0.2739 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3113 - accuracy: 0.9082 - val_loss: 0.3775 - val_accuracy: 0.9054 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2991 - accuracy: 0.9102 - val_loss: 0.4075 - val_accuracy: 0.9247 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2560 - accuracy: 0.9365 - val_loss: 0.3893 - val_accuracy: 0.9103 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2622 - accuracy: 0.9360 - val_loss: 0.3810 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2561 - accuracy: 0.9360 - val_loss: 0.3800 - val_accuracy: 0.9215 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1804 - accuracy: 0.9561 - val_loss: 0.2602 - val_accuracy: 0.9295 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1792 - accuracy: 0.9565 - val_loss: 0.3396 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 10: 375.11 sec +Time taken for epoch(SUBo) 10: 323.65 sec +<---------------------------------------|Epoch [10] END|---------------------------------------> + +Epoch: 11/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2826 - accuracy: 0.9058 - val_loss: 0.2663 - val_accuracy: 0.9183 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3059 - accuracy: 0.9033 - val_loss: 0.3297 - val_accuracy: 0.9199 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3283 - accuracy: 0.9102 - val_loss: 0.3599 - val_accuracy: 0.9375 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2491 - accuracy: 0.9375 - val_loss: 0.3099 - val_accuracy: 0.9327 +Epoch 5/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.2357 - accuracy: 0.9336 - val_loss: 0.4078 - val_accuracy: 0.9167 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2435 - accuracy: 0.9365 - val_loss: 0.2847 - val_accuracy: 0.9359 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1802 - accuracy: 0.9575 - val_loss: 0.3534 - val_accuracy: 0.9295 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1446 - accuracy: 0.9644 - val_loss: 0.3434 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 11: 374.31 sec +Time taken for epoch(SUBo) 11: 322.89 sec +<---------------------------------------|Epoch [11] END|---------------------------------------> + +Epoch: 12/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2543 - accuracy: 0.9263 - val_loss: 0.4121 - val_accuracy: 0.9327 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2741 - accuracy: 0.9258 - val_loss: 0.3493 - val_accuracy: 0.9359 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2763 - accuracy: 0.9307 - val_loss: 0.3661 - val_accuracy: 0.9359 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2352 - accuracy: 0.9414 - val_loss: 0.3281 - val_accuracy: 0.9215 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2251 - accuracy: 0.9453 - val_loss: 0.2411 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1783 - accuracy: 0.9595 - val_loss: 0.3297 - val_accuracy: 0.9247 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1812 - accuracy: 0.9619 - val_loss: 0.2638 - val_accuracy: 0.9087 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9702 - val_loss: 0.2747 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 12: 375.91 sec +Time taken for epoch(SUBo) 12: 324.60 sec +<---------------------------------------|Epoch [12] END|---------------------------------------> + +Epoch: 13/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2384 - accuracy: 0.9326 - val_loss: 0.2895 - val_accuracy: 0.9231 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2609 - accuracy: 0.9287 - val_loss: 0.2950 - val_accuracy: 0.9119 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2872 - accuracy: 0.9277 - val_loss: 0.3571 - val_accuracy: 0.9087 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2855 - accuracy: 0.9229 - val_loss: 0.5538 - val_accuracy: 0.9087 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2314 - accuracy: 0.9478 - val_loss: 0.2693 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1893 - accuracy: 0.9546 - val_loss: 0.2341 - val_accuracy: 0.9343 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1685 - accuracy: 0.9600 - val_loss: 0.2727 - val_accuracy: 0.9439 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1422 - accuracy: 0.9736 - val_loss: 0.2968 - val_accuracy: 0.9407 +Subset training done. +Improved model accuracy from 0.9391025900840759 to 0.9407051205635071. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 13: 376.01 sec +Time taken for epoch(SUBo) 13: 323.00 sec +<---------------------------------------|Epoch [13] END|---------------------------------------> + +Epoch: 14/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 159ms/step - loss: 0.2536 - accuracy: 0.9341 - val_loss: 0.3728 - val_accuracy: 0.9295 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2549 - accuracy: 0.9272 - val_loss: 0.2704 - val_accuracy: 0.9279 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2345 - accuracy: 0.9419 - val_loss: 0.3342 - val_accuracy: 0.9327 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2225 - accuracy: 0.9541 - val_loss: 0.3081 - val_accuracy: 0.9151 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2233 - accuracy: 0.9443 - val_loss: 0.2983 - val_accuracy: 0.9263 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1875 - accuracy: 0.9521 - val_loss: 0.2882 - val_accuracy: 0.9327 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1461 - accuracy: 0.9673 - val_loss: 0.2289 - val_accuracy: 0.9359 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1285 - accuracy: 0.9717 - val_loss: 0.2355 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9407051205635071. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 14: 376.09 sec +Time taken for epoch(SUBo) 14: 324.46 sec +<---------------------------------------|Epoch [14] END|---------------------------------------> + +Epoch: 15/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 158ms/step - loss: 0.2348 - accuracy: 0.9341 - val_loss: 0.3880 - val_accuracy: 0.9263 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2625 - accuracy: 0.9224 - val_loss: 0.3617 - val_accuracy: 0.9327 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2578 - accuracy: 0.9292 - val_loss: 0.3288 - val_accuracy: 0.9263 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2543 - accuracy: 0.9302 - val_loss: 0.3120 - val_accuracy: 0.9038 +Epoch 5/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.3444 - accuracy: 0.9067 - val_loss: 0.2470 - val_accuracy: 0.9391 +Epoch 6/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.2173 - accuracy: 0.9424 - val_loss: 0.3219 - val_accuracy: 0.9343 +Epoch 7/8 +256/256 [==============================] - 39s 154ms/step - loss: 0.1908 - accuracy: 0.9526 - val_loss: 0.2278 - val_accuracy: 0.9407 +Epoch 8/8 +256/256 [==============================] - 39s 154ms/step - loss: 0.1584 - accuracy: 0.9600 - val_loss: 0.2384 - val_accuracy: 0.9439 +Subset training done. +Improved model accuracy from 0.9407051205635071 to 0.9439102411270142. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 15: 374.28 sec +Time taken for epoch(SUBo) 15: 321.58 sec +<---------------------------------------|Epoch [15] END|---------------------------------------> + +Epoch: 16/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.2960 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.3020 - accuracy: 0.9111 - val_loss: 0.3527 - val_accuracy: 0.8622 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2673 - accuracy: 0.9238 - val_loss: 0.5715 - val_accuracy: 0.7340 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2500 - accuracy: 0.9277 - val_loss: 0.5034 - val_accuracy: 0.7484 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2083 - accuracy: 0.9478 - val_loss: 0.2478 - val_accuracy: 0.9071 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1548 - accuracy: 0.9624 - val_loss: 0.2110 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Improved model loss from 0.22827185690402985 to 0.21101020276546478. Saving model. +Time taken for epoch(FULL) 16: 297.03 sec +Time taken for epoch(SUBo) 16: 244.35 sec +<---------------------------------------|Epoch [16] END|---------------------------------------> + +Epoch: 17/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.009500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.2628 - accuracy: 0.9282 - val_loss: 0.2316 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2760 - accuracy: 0.9209 - val_loss: 0.3350 - val_accuracy: 0.9279 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2708 - accuracy: 0.9189 - val_loss: 0.3418 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2240 - accuracy: 0.9419 - val_loss: 0.2829 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1962 - accuracy: 0.9526 - val_loss: 0.2832 - val_accuracy: 0.8926 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1605 - accuracy: 0.9609 - val_loss: 0.2716 - val_accuracy: 0.8974 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 17: 294.63 sec +Time taken for epoch(SUBo) 17: 243.78 sec +<---------------------------------------|Epoch [17] END|---------------------------------------> + +Epoch: 18/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.009000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2515 - accuracy: 0.9263 - val_loss: 0.2493 - val_accuracy: 0.9199 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3009 - accuracy: 0.9219 - val_loss: 0.3727 - val_accuracy: 0.8894 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2677 - accuracy: 0.9224 - val_loss: 0.3309 - val_accuracy: 0.9151 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.2292 - accuracy: 0.9395 - val_loss: 0.2943 - val_accuracy: 0.8910 +Epoch 5/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.1811 - accuracy: 0.9556 - val_loss: 0.2777 - val_accuracy: 0.9087 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1560 - accuracy: 0.9663 - val_loss: 0.2857 - val_accuracy: 0.9215 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 18: 293.64 sec +Time taken for epoch(SUBo) 18: 242.80 sec +<---------------------------------------|Epoch [18] END|---------------------------------------> + +Epoch: 19/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.008500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.2747 - accuracy: 0.9248 - val_loss: 0.2540 - val_accuracy: 0.9038 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.3029 - accuracy: 0.9082 - val_loss: 0.2379 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2496 - accuracy: 0.9297 - val_loss: 0.2431 - val_accuracy: 0.9103 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2807 - accuracy: 0.9087 - val_loss: 0.2517 - val_accuracy: 0.8958 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1925 - accuracy: 0.9463 - val_loss: 0.2512 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9639 - val_loss: 0.2388 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 19: 287.17 sec +Time taken for epoch(SUBo) 19: 237.22 sec +<---------------------------------------|Epoch [19] END|---------------------------------------> + +Epoch: 20/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.008000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2361 - accuracy: 0.9326 - val_loss: 0.3213 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2437 - accuracy: 0.9282 - val_loss: 0.3130 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2265 - accuracy: 0.9390 - val_loss: 0.7231 - val_accuracy: 0.5817 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2058 - accuracy: 0.9463 - val_loss: 0.2048 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1747 - accuracy: 0.9585 - val_loss: 0.2309 - val_accuracy: 0.9135 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1581 - accuracy: 0.9624 - val_loss: 0.2022 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Improved model loss from 0.21101020276546478 to 0.20221146941184998. Saving model. +Time taken for epoch(FULL) 20: 287.49 sec +Time taken for epoch(SUBo) 20: 236.52 sec +<---------------------------------------|Epoch [20] END|---------------------------------------> + +Epoch: 21/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.007500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2639 - accuracy: 0.9204 - val_loss: 0.3842 - val_accuracy: 0.8542 +Epoch 2/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.2602 - accuracy: 0.9224 - val_loss: 0.2024 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2491 - accuracy: 0.9204 - val_loss: 0.3014 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2034 - accuracy: 0.9521 - val_loss: 0.2709 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2075 - accuracy: 0.9429 - val_loss: 0.3214 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1429 - accuracy: 0.9648 - val_loss: 0.2890 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 21: 285.90 sec +Time taken for epoch(SUBo) 21: 236.69 sec +<---------------------------------------|Epoch [21] END|---------------------------------------> + +Epoch: 22/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.007000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2293 - accuracy: 0.9360 - val_loss: 0.1936 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2278 - accuracy: 0.9341 - val_loss: 0.2616 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2114 - accuracy: 0.9438 - val_loss: 0.2647 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2235 - accuracy: 0.9453 - val_loss: 0.2567 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1777 - accuracy: 0.9541 - val_loss: 0.2569 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1626 - accuracy: 0.9575 - val_loss: 0.2484 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 22: 285.94 sec +Time taken for epoch(SUBo) 22: 236.66 sec +<---------------------------------------|Epoch [22] END|---------------------------------------> + +Epoch: 23/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.006500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2132 - accuracy: 0.9385 - val_loss: 0.2144 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2413 - accuracy: 0.9360 - val_loss: 0.5426 - val_accuracy: 0.8750 +Epoch 3/6 +256/256 [==============================] - 38s 149ms/step - loss: 0.2458 - accuracy: 0.9375 - val_loss: 0.2533 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1869 - accuracy: 0.9453 - val_loss: 0.2258 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9663 - val_loss: 0.2642 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 38s 149ms/step - loss: 0.1227 - accuracy: 0.9746 - val_loss: 0.2471 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 23: 284.47 sec +Time taken for epoch(SUBo) 23: 235.20 sec +<---------------------------------------|Epoch [23] END|---------------------------------------> + +Epoch: 24/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.006000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2147 - accuracy: 0.9365 - val_loss: 0.2431 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2151 - accuracy: 0.9385 - val_loss: 0.2308 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2120 - accuracy: 0.9380 - val_loss: 0.2704 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1936 - accuracy: 0.9453 - val_loss: 0.2529 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1086 - accuracy: 0.9756 - val_loss: 0.1858 - val_accuracy: 0.9471 +Subset training done. +Improved model accuracy from 0.9439102411270142 to 0.9471153616905212. Saving model. +Improved model loss from 0.20221146941184998 to 0.1857679933309555. Saving model. +Time taken for epoch(FULL) 24: 288.85 sec +Time taken for epoch(SUBo) 24: 236.73 sec +<---------------------------------------|Epoch [24] END|---------------------------------------> + +Epoch: 25/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.005500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2106 - accuracy: 0.9414 - val_loss: 0.2085 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2304 - accuracy: 0.9326 - val_loss: 0.2498 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2059 - accuracy: 0.9482 - val_loss: 0.3972 - val_accuracy: 0.9247 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1980 - accuracy: 0.9458 - val_loss: 0.2653 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9731 - val_loss: 0.2222 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1402 - accuracy: 0.9604 - val_loss: 0.2944 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 25: 285.39 sec +Time taken for epoch(SUBo) 25: 236.55 sec +<---------------------------------------|Epoch [25] END|---------------------------------------> + +Epoch: 26/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.005000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2292 - accuracy: 0.9341 - val_loss: 0.2645 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2017 - accuracy: 0.9414 - val_loss: 0.2456 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2125 - accuracy: 0.9341 - val_loss: 0.3309 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1715 - accuracy: 0.9536 - val_loss: 0.2653 - val_accuracy: 0.9183 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9658 - val_loss: 0.2156 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9741 - val_loss: 0.2134 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 26: 285.12 sec +Time taken for epoch(SUBo) 26: 236.07 sec +<---------------------------------------|Epoch [26] END|---------------------------------------> + +Epoch: 27/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.004500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1868 - accuracy: 0.9463 - val_loss: 0.1853 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2043 - accuracy: 0.9351 - val_loss: 0.3479 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1911 - accuracy: 0.9453 - val_loss: 0.2130 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1510 - accuracy: 0.9600 - val_loss: 0.2097 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1655 - accuracy: 0.9561 - val_loss: 0.1885 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1346 - accuracy: 0.9692 - val_loss: 0.1939 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 27: 285.71 sec +Time taken for epoch(SUBo) 27: 236.48 sec +<---------------------------------------|Epoch [27] END|---------------------------------------> + +Epoch: 28/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.004000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2180 - accuracy: 0.9360 - val_loss: 0.1893 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2060 - accuracy: 0.9385 - val_loss: 0.1826 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1867 - accuracy: 0.9448 - val_loss: 0.1701 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1611 - accuracy: 0.9614 - val_loss: 0.1821 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1444 - accuracy: 0.9609 - val_loss: 0.1652 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1409 - accuracy: 0.9644 - val_loss: 0.1546 - val_accuracy: 0.9567 +Subset training done. +Improved model accuracy from 0.9471153616905212 to 0.9567307829856873. Saving model. +Improved model loss from 0.1857679933309555 to 0.15460731089115143. Saving model. +Time taken for epoch(FULL) 28: 288.65 sec +Time taken for epoch(SUBo) 28: 236.43 sec +<---------------------------------------|Epoch [28] END|---------------------------------------> + +Epoch: 29/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.003500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1936 - accuracy: 0.9404 - val_loss: 0.1560 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1892 - accuracy: 0.9390 - val_loss: 0.1654 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1752 - accuracy: 0.9541 - val_loss: 0.2738 - val_accuracy: 0.8926 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1570 - accuracy: 0.9561 - val_loss: 0.1721 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1441 - accuracy: 0.9639 - val_loss: 0.1639 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1111 - accuracy: 0.9692 - val_loss: 0.1661 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 29: 285.51 sec +Time taken for epoch(SUBo) 29: 236.35 sec +<---------------------------------------|Epoch [29] END|---------------------------------------> + +Epoch: 30/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.003000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1650 - accuracy: 0.9531 - val_loss: 0.1881 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1823 - accuracy: 0.9468 - val_loss: 0.2431 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1812 - accuracy: 0.9473 - val_loss: 0.1803 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1608 - accuracy: 0.9546 - val_loss: 0.1606 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9609 - val_loss: 0.1624 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1155 - accuracy: 0.9702 - val_loss: 0.1665 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 30: 285.48 sec +Time taken for epoch(SUBo) 30: 236.40 sec +<---------------------------------------|Epoch [30] END|---------------------------------------> + +Epoch: 31/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.002500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1981 - accuracy: 0.9370 - val_loss: 0.1560 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1661 - accuracy: 0.9482 - val_loss: 0.1612 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1624 - accuracy: 0.9517 - val_loss: 0.1743 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1685 - accuracy: 0.9517 - val_loss: 0.1903 - val_accuracy: 0.9247 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1254 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9231 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9707 - val_loss: 0.1807 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 31: 285.66 sec +Time taken for epoch(SUBo) 31: 236.69 sec +<---------------------------------------|Epoch [31] END|---------------------------------------> + +Epoch: 32/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.002000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1669 - accuracy: 0.9502 - val_loss: 0.1911 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1540 - accuracy: 0.9531 - val_loss: 0.1633 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9624 - val_loss: 0.1597 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1643 - accuracy: 0.9551 - val_loss: 0.1712 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9585 - val_loss: 0.1951 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1076 - accuracy: 0.9658 - val_loss: 0.1953 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 32: 285.69 sec +Time taken for epoch(SUBo) 32: 236.38 sec +<---------------------------------------|Epoch [32] END|---------------------------------------> + +Epoch: 33/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1745 - accuracy: 0.9463 - val_loss: 0.1852 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1641 - accuracy: 0.9512 - val_loss: 0.1889 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1578 - accuracy: 0.9512 - val_loss: 0.1950 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1493 - accuracy: 0.9507 - val_loss: 0.1669 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.1736 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1185 - accuracy: 0.9658 - val_loss: 0.1680 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 33: 286.31 sec +Time taken for epoch(SUBo) 33: 236.60 sec +<---------------------------------------|Epoch [33] END|---------------------------------------> + +Epoch: 34/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1615 - accuracy: 0.9521 - val_loss: 0.1627 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1649 - accuracy: 0.9521 - val_loss: 0.2083 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1949 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1419 - accuracy: 0.9517 - val_loss: 0.1563 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1317 - accuracy: 0.9565 - val_loss: 0.1606 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1158 - accuracy: 0.9688 - val_loss: 0.1512 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Improved model loss from 0.15460731089115143 to 0.15118563175201416. Saving model. +Time taken for epoch(FULL) 34: 287.71 sec +Time taken for epoch(SUBo) 34: 236.71 sec +<---------------------------------------|Epoch [34] END|---------------------------------------> + +Epoch: 35/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1739 - accuracy: 0.9443 - val_loss: 0.1441 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2022 - accuracy: 0.9336 - val_loss: 0.1491 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1754 - accuracy: 0.9458 - val_loss: 0.1782 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1629 - accuracy: 0.9458 - val_loss: 0.1656 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1582 - accuracy: 0.9546 - val_loss: 0.1640 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9531 - val_loss: 0.1650 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 35: 287.73 sec +Time taken for epoch(SUBo) 35: 237.55 sec +<---------------------------------------|Epoch [35] END|---------------------------------------> + +Epoch: 36/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9526 - val_loss: 0.1498 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1602 - accuracy: 0.9468 - val_loss: 0.1686 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9521 - val_loss: 0.1585 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1683 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9604 - val_loss: 0.1843 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1206 - accuracy: 0.9644 - val_loss: 0.1951 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 36: 287.50 sec +Time taken for epoch(SUBo) 36: 237.39 sec +<---------------------------------------|Epoch [36] END|---------------------------------------> + +Epoch: 37/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1843 - accuracy: 0.9414 - val_loss: 0.1578 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1683 - accuracy: 0.9497 - val_loss: 0.1731 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1612 - accuracy: 0.9463 - val_loss: 0.2032 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1507 - accuracy: 0.9521 - val_loss: 0.1985 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9590 - val_loss: 0.1618 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9595 - val_loss: 0.1653 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 37: 287.80 sec +Time taken for epoch(SUBo) 37: 237.45 sec +<---------------------------------------|Epoch [37] END|---------------------------------------> + +Epoch: 38/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1649 - accuracy: 0.9487 - val_loss: 0.1677 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1775 - accuracy: 0.9438 - val_loss: 0.1582 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1564 - accuracy: 0.9526 - val_loss: 0.1516 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1513 - accuracy: 0.9541 - val_loss: 0.1526 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1408 - accuracy: 0.9595 - val_loss: 0.1522 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1186 - accuracy: 0.9634 - val_loss: 0.1668 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 38: 287.81 sec +Time taken for epoch(SUBo) 38: 237.65 sec +<---------------------------------------|Epoch [38] END|---------------------------------------> + +Epoch: 39/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1748 - accuracy: 0.9414 - val_loss: 0.1468 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9521 - val_loss: 0.1940 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1527 - accuracy: 0.9536 - val_loss: 0.1679 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1440 - accuracy: 0.9521 - val_loss: 0.2192 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1304 - accuracy: 0.9570 - val_loss: 0.1655 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1088 - accuracy: 0.9697 - val_loss: 0.1865 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 39: 288.44 sec +Time taken for epoch(SUBo) 39: 237.93 sec +<---------------------------------------|Epoch [39] END|---------------------------------------> + +Epoch: 40/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1613 - accuracy: 0.9502 - val_loss: 0.1476 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1465 - accuracy: 0.9590 - val_loss: 0.1613 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1391 - accuracy: 0.9609 - val_loss: 0.1533 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1231 - accuracy: 0.9648 - val_loss: 0.1602 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1393 - accuracy: 0.9609 - val_loss: 0.1537 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1065 - accuracy: 0.9727 - val_loss: 0.1562 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 40: 287.78 sec +Time taken for epoch(SUBo) 40: 237.78 sec +<---------------------------------------|Epoch [40] END|---------------------------------------> + +Epoch: 41/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1631 - accuracy: 0.9478 - val_loss: 0.1572 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1542 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1441 - accuracy: 0.9531 - val_loss: 0.1653 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9614 - val_loss: 0.1968 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1599 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1870 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 41: 287.23 sec +Time taken for epoch(SUBo) 41: 237.14 sec +<---------------------------------------|Epoch [41] END|---------------------------------------> + +Epoch: 42/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1298 - accuracy: 0.9531 - val_loss: 0.2101 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9565 - val_loss: 0.1721 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1334 - accuracy: 0.9614 - val_loss: 0.1705 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9678 - val_loss: 0.1819 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1071 - accuracy: 0.9678 - val_loss: 0.1882 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0998 - accuracy: 0.9712 - val_loss: 0.2143 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 42: 287.96 sec +Time taken for epoch(SUBo) 42: 237.29 sec +<---------------------------------------|Epoch [42] END|---------------------------------------> + +Epoch: 43/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1517 - accuracy: 0.9556 - val_loss: 0.1814 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.2212 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2157 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2624 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1266 - accuracy: 0.9648 - val_loss: 0.2113 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2185 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 43: 288.58 sec +Time taken for epoch(SUBo) 43: 237.71 sec +<---------------------------------------|Epoch [43] END|---------------------------------------> + +Epoch: 44/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1549 - accuracy: 0.9507 - val_loss: 0.1907 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1406 - accuracy: 0.9561 - val_loss: 0.1945 - val_accuracy: 0.9279 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9556 - val_loss: 0.2094 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1300 - accuracy: 0.9619 - val_loss: 0.2000 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1146 - accuracy: 0.9678 - val_loss: 0.2591 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1207 - accuracy: 0.9648 - val_loss: 0.2343 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 44: 288.18 sec +Time taken for epoch(SUBo) 44: 237.53 sec +<---------------------------------------|Epoch [44] END|---------------------------------------> + +Epoch: 45/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1691 - accuracy: 0.9507 - val_loss: 0.1829 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9570 - val_loss: 0.1635 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1363 - accuracy: 0.9609 - val_loss: 0.2010 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1235 - accuracy: 0.9624 - val_loss: 0.1995 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9600 - val_loss: 0.2820 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1434 - accuracy: 0.9512 - val_loss: 0.2766 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 45: 288.43 sec +Time taken for epoch(SUBo) 45: 237.92 sec +<---------------------------------------|Epoch [45] END|---------------------------------------> + +Epoch: 46/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1684 - accuracy: 0.9468 - val_loss: 0.3024 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1606 - accuracy: 0.9478 - val_loss: 0.3133 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1545 - accuracy: 0.9585 - val_loss: 0.2165 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1639 - accuracy: 0.9468 - val_loss: 0.2465 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.2787 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9551 - val_loss: 0.2559 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 46: 288.00 sec +Time taken for epoch(SUBo) 46: 237.42 sec +<---------------------------------------|Epoch [46] END|---------------------------------------> + +Epoch: 47/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1874 - accuracy: 0.9414 - val_loss: 0.2024 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1816 - accuracy: 0.9487 - val_loss: 0.2076 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1674 - accuracy: 0.9434 - val_loss: 0.3245 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1442 - accuracy: 0.9604 - val_loss: 0.2564 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1221 - accuracy: 0.9609 - val_loss: 0.3057 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1317 - accuracy: 0.9556 - val_loss: 0.2604 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 47: 287.69 sec +Time taken for epoch(SUBo) 47: 236.59 sec +<---------------------------------------|Epoch [47] END|---------------------------------------> + +Epoch: 48/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1541 - accuracy: 0.9453 - val_loss: 0.2779 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1480 - accuracy: 0.9526 - val_loss: 0.2490 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1341 - accuracy: 0.9614 - val_loss: 0.2237 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1366 - accuracy: 0.9570 - val_loss: 0.2314 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2416 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1106 - accuracy: 0.9644 - val_loss: 0.2330 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 48: 286.84 sec +Time taken for epoch(SUBo) 48: 236.62 sec +<---------------------------------------|Epoch [48] END|---------------------------------------> + +Epoch: 49/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1551 - accuracy: 0.9561 - val_loss: 0.2252 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9570 - val_loss: 0.2131 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1401 - accuracy: 0.9580 - val_loss: 0.1908 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1271 - accuracy: 0.9639 - val_loss: 0.2179 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1260 - accuracy: 0.9634 - val_loss: 0.2022 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1087 - accuracy: 0.9717 - val_loss: 0.1932 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 49: 286.33 sec +Time taken for epoch(SUBo) 49: 236.60 sec +<---------------------------------------|Epoch [49] END|---------------------------------------> + +Epoch: 50/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1449 - accuracy: 0.9521 - val_loss: 0.1748 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1448 - accuracy: 0.9507 - val_loss: 0.2003 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9521 - val_loss: 0.2190 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9390 - val_loss: 0.2207 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9521 - val_loss: 0.2131 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1572 - accuracy: 0.9478 - val_loss: 0.2142 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 50: 286.59 sec +Time taken for epoch(SUBo) 50: 236.86 sec +<---------------------------------------|Epoch [50] END|---------------------------------------> + +Epoch: 51/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1601 - accuracy: 0.9497 - val_loss: 0.1783 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1519 - accuracy: 0.9517 - val_loss: 0.2485 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1687 - accuracy: 0.9521 - val_loss: 0.2295 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1445 - accuracy: 0.9600 - val_loss: 0.2580 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1283 - accuracy: 0.9619 - val_loss: 0.2596 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1248 - accuracy: 0.9624 - val_loss: 0.2709 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 51: 286.23 sec +Time taken for epoch(SUBo) 51: 236.38 sec +<---------------------------------------|Epoch [51] END|---------------------------------------> + +Epoch: 52/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1478 - accuracy: 0.9512 - val_loss: 0.2317 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1364 - accuracy: 0.9614 - val_loss: 0.2805 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9634 - val_loss: 0.2886 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2800 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1081 - accuracy: 0.9712 - val_loss: 0.2406 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9702 - val_loss: 0.2587 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 52: 286.99 sec +Time taken for epoch(SUBo) 52: 236.83 sec +<---------------------------------------|Epoch [52] END|---------------------------------------> + +Epoch: 53/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2206 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9468 - val_loss: 0.2399 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1522 - accuracy: 0.9546 - val_loss: 0.2213 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9546 - val_loss: 0.1943 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1258 - accuracy: 0.9580 - val_loss: 0.1851 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9541 - val_loss: 0.1898 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 53: 287.29 sec +Time taken for epoch(SUBo) 53: 237.19 sec +<---------------------------------------|Epoch [53] END|---------------------------------------> + +Epoch: 54/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1649 - accuracy: 0.9429 - val_loss: 0.2123 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1791 - accuracy: 0.9424 - val_loss: 0.2041 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1739 - accuracy: 0.9429 - val_loss: 0.2438 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1467 - accuracy: 0.9521 - val_loss: 0.2370 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1384 - accuracy: 0.9541 - val_loss: 0.3072 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1439 - accuracy: 0.9580 - val_loss: 0.2901 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 54: 287.00 sec +Time taken for epoch(SUBo) 54: 236.97 sec +<---------------------------------------|Epoch [54] END|---------------------------------------> + +Epoch: 55/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1734 - accuracy: 0.9438 - val_loss: 0.2456 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1551 - accuracy: 0.9512 - val_loss: 0.2227 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9468 - val_loss: 0.2150 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1365 - accuracy: 0.9600 - val_loss: 0.1964 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9595 - val_loss: 0.2038 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.2228 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 55: 286.51 sec +Time taken for epoch(SUBo) 55: 236.75 sec +<---------------------------------------|Epoch [55] END|---------------------------------------> + +Epoch: 56/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1372 - accuracy: 0.9575 - val_loss: 0.2215 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1534 - accuracy: 0.9541 - val_loss: 0.2516 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9629 - val_loss: 0.2329 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9673 - val_loss: 0.2124 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1028 - accuracy: 0.9727 - val_loss: 0.2299 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0982 - accuracy: 0.9736 - val_loss: 0.2280 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 56: 286.73 sec +Time taken for epoch(SUBo) 56: 237.12 sec +<---------------------------------------|Epoch [56] END|---------------------------------------> + +Epoch: 57/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1279 - accuracy: 0.9604 - val_loss: 0.1954 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9590 - val_loss: 0.2062 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1403 - accuracy: 0.9580 - val_loss: 0.1679 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1308 - accuracy: 0.9570 - val_loss: 0.1776 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1117 - accuracy: 0.9648 - val_loss: 0.1890 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1019 - accuracy: 0.9717 - val_loss: 0.1922 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 57: 286.14 sec +Time taken for epoch(SUBo) 57: 235.92 sec +<---------------------------------------|Epoch [57] END|---------------------------------------> + +Epoch: 58/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1579 - accuracy: 0.9468 - val_loss: 0.1934 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1771 - accuracy: 0.9409 - val_loss: 0.1981 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1471 - accuracy: 0.9561 - val_loss: 0.2460 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9595 - val_loss: 0.1832 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9536 - val_loss: 0.1711 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1317 - accuracy: 0.9609 - val_loss: 0.1742 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 58: 287.08 sec +Time taken for epoch(SUBo) 58: 236.57 sec +<---------------------------------------|Epoch [58] END|---------------------------------------> + +Epoch: 59/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1481 - accuracy: 0.9551 - val_loss: 0.1874 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1438 - accuracy: 0.9546 - val_loss: 0.1799 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1512 - accuracy: 0.9575 - val_loss: 0.1774 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1369 - accuracy: 0.9595 - val_loss: 0.1793 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1269 - accuracy: 0.9663 - val_loss: 0.1713 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9688 - val_loss: 0.1879 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 59: 286.52 sec +Time taken for epoch(SUBo) 59: 237.12 sec +<---------------------------------------|Epoch [59] END|---------------------------------------> + +Epoch: 60/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9531 - val_loss: 0.1852 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1386 - accuracy: 0.9575 - val_loss: 0.1995 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9663 - val_loss: 0.2111 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1169 - accuracy: 0.9663 - val_loss: 0.2195 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1004 - accuracy: 0.9717 - val_loss: 0.2351 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9668 - val_loss: 0.2677 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 60: 287.80 sec +Time taken for epoch(SUBo) 60: 237.20 sec +<---------------------------------------|Epoch [60] END|---------------------------------------> + +Epoch: 61/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1434 - accuracy: 0.9551 - val_loss: 0.2024 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1346 - accuracy: 0.9604 - val_loss: 0.2110 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1218 - accuracy: 0.9644 - val_loss: 0.1917 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9629 - val_loss: 0.2180 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1204 - accuracy: 0.9639 - val_loss: 0.1932 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1012 - accuracy: 0.9683 - val_loss: 0.1964 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 61: 288.27 sec +Time taken for epoch(SUBo) 61: 237.72 sec +<---------------------------------------|Epoch [61] END|---------------------------------------> + +Epoch: 62/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1389 - accuracy: 0.9575 - val_loss: 0.2335 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1295 - accuracy: 0.9561 - val_loss: 0.2828 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1223 - accuracy: 0.9619 - val_loss: 0.2642 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9673 - val_loss: 0.2734 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.2583 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1019 - accuracy: 0.9707 - val_loss: 0.2563 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 62: 288.25 sec +Time taken for epoch(SUBo) 62: 237.52 sec +<---------------------------------------|Epoch [62] END|---------------------------------------> + +Epoch: 63/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1588 - accuracy: 0.9517 - val_loss: 0.2404 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1392 - accuracy: 0.9624 - val_loss: 0.1892 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.1851 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9634 - val_loss: 0.1914 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1298 - accuracy: 0.9619 - val_loss: 0.2004 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9673 - val_loss: 0.1989 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 63: 288.35 sec +Time taken for epoch(SUBo) 63: 237.48 sec +<---------------------------------------|Epoch [63] END|---------------------------------------> + +Epoch: 64/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1376 - accuracy: 0.9556 - val_loss: 0.1802 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1370 - accuracy: 0.9575 - val_loss: 0.2342 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1335 - accuracy: 0.9604 - val_loss: 0.1916 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1462 - accuracy: 0.9580 - val_loss: 0.1591 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1061 - accuracy: 0.9663 - val_loss: 0.2386 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9688 - val_loss: 0.2423 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 64: 288.62 sec +Time taken for epoch(SUBo) 64: 237.68 sec +<---------------------------------------|Epoch [64] END|---------------------------------------> + +Epoch: 65/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1365 - accuracy: 0.9556 - val_loss: 0.2579 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1324 - accuracy: 0.9595 - val_loss: 0.2196 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9619 - val_loss: 0.2640 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1136 - accuracy: 0.9663 - val_loss: 0.2262 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1052 - accuracy: 0.9692 - val_loss: 0.2272 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0993 - accuracy: 0.9697 - val_loss: 0.2402 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 65: 288.68 sec +Time taken for epoch(SUBo) 65: 238.01 sec +<---------------------------------------|Epoch [65] END|---------------------------------------> + +Epoch: 66/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9590 - val_loss: 0.2096 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1272 - accuracy: 0.9604 - val_loss: 0.2505 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1286 - accuracy: 0.9561 - val_loss: 0.2210 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9683 - val_loss: 0.1834 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1793 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1017 - accuracy: 0.9697 - val_loss: 0.2070 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 66: 288.12 sec +Time taken for epoch(SUBo) 66: 237.92 sec +<---------------------------------------|Epoch [66] END|---------------------------------------> + +Epoch: 67/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1517 - accuracy: 0.9565 - val_loss: 0.1927 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1471 - accuracy: 0.9502 - val_loss: 0.2064 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2286 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1262 - accuracy: 0.9619 - val_loss: 0.1877 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1157 - accuracy: 0.9639 - val_loss: 0.1992 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9658 - val_loss: 0.1889 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 67: 288.09 sec +Time taken for epoch(SUBo) 67: 237.40 sec +<---------------------------------------|Epoch [67] END|---------------------------------------> + +Epoch: 68/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1311 - accuracy: 0.9556 - val_loss: 0.1958 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1119 - accuracy: 0.9644 - val_loss: 0.2010 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1263 - accuracy: 0.9595 - val_loss: 0.1595 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9595 - val_loss: 0.1492 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1132 - accuracy: 0.9639 - val_loss: 0.1464 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1003 - accuracy: 0.9712 - val_loss: 0.1529 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 68: 288.54 sec +Time taken for epoch(SUBo) 68: 237.57 sec +<---------------------------------------|Epoch [68] END|---------------------------------------> + +Epoch: 69/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1554 - accuracy: 0.9546 - val_loss: 0.1697 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1428 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1287 - accuracy: 0.9629 - val_loss: 0.2158 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1152 - accuracy: 0.9634 - val_loss: 0.1788 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1029 - accuracy: 0.9697 - val_loss: 0.1732 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0991 - accuracy: 0.9722 - val_loss: 0.1837 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 69: 287.94 sec +Time taken for epoch(SUBo) 69: 237.46 sec +<---------------------------------------|Epoch [69] END|---------------------------------------> + +Epoch: 70/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1386 - accuracy: 0.9648 - val_loss: 0.1742 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.2681 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1782 - accuracy: 0.9482 - val_loss: 0.3058 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1468 - accuracy: 0.9526 - val_loss: 0.2156 - val_accuracy: 0.9327 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1217 - accuracy: 0.9634 - val_loss: 0.1891 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9668 - val_loss: 0.1983 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 70: 288.24 sec +Time taken for epoch(SUBo) 70: 237.80 sec +<---------------------------------------|Epoch [70] END|---------------------------------------> + +Epoch: 71/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.1688 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1528 - accuracy: 0.9546 - val_loss: 0.1514 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9609 - val_loss: 0.1770 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9585 - val_loss: 0.1579 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1195 - accuracy: 0.9653 - val_loss: 0.1543 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1538 - val_accuracy: 0.9599 +Subset training done. +Improved model accuracy from 0.9567307829856873 to 0.9599359035491943. Saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 71: 289.74 sec +Time taken for epoch(SUBo) 71: 237.66 sec +<---------------------------------------|Epoch [71] END|---------------------------------------> + +Epoch: 72/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1505 - accuracy: 0.9521 - val_loss: 0.1529 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1589 - accuracy: 0.9512 - val_loss: 0.1426 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1484 - accuracy: 0.9546 - val_loss: 0.1592 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9658 - val_loss: 0.1791 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1154 - accuracy: 0.9673 - val_loss: 0.1634 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 72: 288.91 sec +Time taken for epoch(SUBo) 72: 237.07 sec +<---------------------------------------|Epoch [72] END|---------------------------------------> + +Epoch: 73/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1304 - accuracy: 0.9609 - val_loss: 0.1894 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1949 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9526 - val_loss: 0.2177 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.2006 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1074 - accuracy: 0.9746 - val_loss: 0.2530 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9692 - val_loss: 0.2516 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 73: 286.60 sec +Time taken for epoch(SUBo) 73: 236.85 sec +<---------------------------------------|Epoch [73] END|---------------------------------------> + +Epoch: 74/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1103 - accuracy: 0.9653 - val_loss: 0.2006 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1242 - accuracy: 0.9600 - val_loss: 0.2702 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1351 - accuracy: 0.9580 - val_loss: 0.2475 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9731 - val_loss: 0.2133 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2043 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.0768 - accuracy: 0.9780 - val_loss: 0.2014 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 74: 287.09 sec +Time taken for epoch(SUBo) 74: 237.29 sec +<---------------------------------------|Epoch [74] END|---------------------------------------> + +Epoch: 75/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1430 - accuracy: 0.9546 - val_loss: 0.2063 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1306 - accuracy: 0.9619 - val_loss: 0.1984 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1205 - accuracy: 0.9663 - val_loss: 0.1844 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1186 - accuracy: 0.9653 - val_loss: 0.1739 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9727 - val_loss: 0.1955 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0930 - accuracy: 0.9731 - val_loss: 0.1780 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 75: 287.36 sec +Time taken for epoch(SUBo) 75: 237.36 sec +<---------------------------------------|Epoch [75] END|---------------------------------------> + +Epoch: 76/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1332 - accuracy: 0.9561 - val_loss: 0.1757 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1358 - accuracy: 0.9590 - val_loss: 0.1649 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1475 - accuracy: 0.9546 - val_loss: 0.1689 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9570 - val_loss: 0.1557 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1127 - accuracy: 0.9619 - val_loss: 0.1633 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9717 - val_loss: 0.1716 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 76: 286.87 sec +Time taken for epoch(SUBo) 76: 237.21 sec +<---------------------------------------|Epoch [76] END|---------------------------------------> + +Epoch: 77/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1613 - accuracy: 0.9429 - val_loss: 0.1702 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1557 - accuracy: 0.9463 - val_loss: 0.1623 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9546 - val_loss: 0.2084 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1245 - accuracy: 0.9619 - val_loss: 0.2221 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1156 - accuracy: 0.9624 - val_loss: 0.2435 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9683 - val_loss: 0.2508 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 77: 286.93 sec +Time taken for epoch(SUBo) 77: 236.81 sec +<---------------------------------------|Epoch [77] END|---------------------------------------> + +Epoch: 78/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1258 - accuracy: 0.9609 - val_loss: 0.1880 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1473 - accuracy: 0.9507 - val_loss: 0.1763 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1170 - accuracy: 0.9658 - val_loss: 0.2302 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9551 - val_loss: 0.1573 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1236 - accuracy: 0.9585 - val_loss: 0.1819 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1150 - accuracy: 0.9639 - val_loss: 0.1925 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 78: 287.03 sec +Time taken for epoch(SUBo) 78: 237.13 sec +<---------------------------------------|Epoch [78] END|---------------------------------------> + +Epoch: 79/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1394 - accuracy: 0.9570 - val_loss: 0.1949 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1345 - accuracy: 0.9604 - val_loss: 0.2434 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.2313 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1145 - accuracy: 0.9648 - val_loss: 0.2336 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1077 - accuracy: 0.9707 - val_loss: 0.2261 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1020 - accuracy: 0.9688 - val_loss: 0.2249 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 79: 286.93 sec +Time taken for epoch(SUBo) 79: 237.31 sec +<---------------------------------------|Epoch [79] END|---------------------------------------> + +Epoch: 80/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9512 - val_loss: 0.2335 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2401 - val_accuracy: 0.9183 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2689 - accuracy: 0.9048 - val_loss: 0.4998 - val_accuracy: 0.7821 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.2924 - accuracy: 0.8955 - val_loss: 0.4549 - val_accuracy: 0.8782 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2427 - accuracy: 0.9136 - val_loss: 0.3899 - val_accuracy: 0.8830 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2071 - accuracy: 0.9292 - val_loss: 0.3938 - val_accuracy: 0.8830 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 80: 287.37 sec +Time taken for epoch(SUBo) 80: 237.50 sec +<---------------------------------------|Epoch [80] END|---------------------------------------> + +Epoch: 81/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2117 - accuracy: 0.9272 - val_loss: 0.3888 - val_accuracy: 0.8942 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.2039 - accuracy: 0.9326 - val_loss: 0.4718 - val_accuracy: 0.9038 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1797 - accuracy: 0.9424 - val_loss: 0.4449 - val_accuracy: 0.9087 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1627 - accuracy: 0.9512 - val_loss: 0.2830 - val_accuracy: 0.9151 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1495 - accuracy: 0.9565 - val_loss: 0.3565 - val_accuracy: 0.9167 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9541 - val_loss: 0.3372 - val_accuracy: 0.9199 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 81: 287.21 sec +Time taken for epoch(SUBo) 81: 237.47 sec +<---------------------------------------|Epoch [81] END|---------------------------------------> + +Epoch: 82/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1753 - accuracy: 0.9424 - val_loss: 0.3639 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1803 - accuracy: 0.9429 - val_loss: 0.3132 - val_accuracy: 0.9215 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1485 - accuracy: 0.9565 - val_loss: 0.2975 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.3335 - val_accuracy: 0.9247 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9561 - val_loss: 0.2650 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9653 - val_loss: 0.2362 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 82: 286.96 sec +Time taken for epoch(SUBo) 82: 236.90 sec +<---------------------------------------|Epoch [82] END|---------------------------------------> + +Epoch: 83/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1623 - accuracy: 0.9492 - val_loss: 0.2152 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1599 - accuracy: 0.9502 - val_loss: 0.2598 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1508 - accuracy: 0.9609 - val_loss: 0.2304 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9517 - val_loss: 0.2164 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9624 - val_loss: 0.2169 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1250 - accuracy: 0.9595 - val_loss: 0.2147 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 83: 287.52 sec +Time taken for epoch(SUBo) 83: 237.43 sec +<---------------------------------------|Epoch [83] END|---------------------------------------> + +Epoch: 84/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9595 - val_loss: 0.2386 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.1926 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1437 - accuracy: 0.9526 - val_loss: 0.2082 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1389 - accuracy: 0.9556 - val_loss: 0.2051 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1211 - accuracy: 0.9634 - val_loss: 0.1852 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9702 - val_loss: 0.1848 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 84: 286.91 sec +Time taken for epoch(SUBo) 84: 237.24 sec +<---------------------------------------|Epoch [84] END|---------------------------------------> + +Epoch: 85/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1612 - accuracy: 0.9478 - val_loss: 0.2066 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1647 - accuracy: 0.9448 - val_loss: 0.1899 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1606 - accuracy: 0.9448 - val_loss: 0.1948 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1336 - accuracy: 0.9561 - val_loss: 0.1954 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.1911 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.1925 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 85: 288.09 sec +Time taken for epoch(SUBo) 85: 237.57 sec +<---------------------------------------|Epoch [85] END|---------------------------------------> + +Epoch: 86/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1545 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1532 - accuracy: 0.9517 - val_loss: 0.2042 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1454 - accuracy: 0.9492 - val_loss: 0.1683 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1330 - accuracy: 0.9604 - val_loss: 0.1693 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1233 - accuracy: 0.9604 - val_loss: 0.1930 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1207 - accuracy: 0.9619 - val_loss: 0.1804 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 86: 288.17 sec +Time taken for epoch(SUBo) 86: 237.64 sec +<---------------------------------------|Epoch [86] END|---------------------------------------> + +Epoch: 87/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9536 - val_loss: 0.1667 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1656 - accuracy: 0.9478 - val_loss: 0.1621 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1384 - accuracy: 0.9595 - val_loss: 0.1620 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9585 - val_loss: 0.1718 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1227 - accuracy: 0.9595 - val_loss: 0.1562 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1226 - accuracy: 0.9653 - val_loss: 0.1679 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 87: 288.63 sec +Time taken for epoch(SUBo) 87: 237.58 sec +<---------------------------------------|Epoch [87] END|---------------------------------------> + +Epoch: 88/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1496 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1700 - accuracy: 0.9399 - val_loss: 0.1543 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1560 - accuracy: 0.9546 - val_loss: 0.1877 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.1802 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1187 - accuracy: 0.9609 - val_loss: 0.1640 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1221 - accuracy: 0.9629 - val_loss: 0.1898 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 88: 289.56 sec +Time taken for epoch(SUBo) 88: 238.18 sec +<---------------------------------------|Epoch [88] END|---------------------------------------> + +Epoch: 89/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1682 - accuracy: 0.9497 - val_loss: 0.1799 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1313 - accuracy: 0.9580 - val_loss: 0.2257 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9585 - val_loss: 0.2209 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1873 - accuracy: 0.9399 - val_loss: 0.1585 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1725 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1436 - accuracy: 0.9580 - val_loss: 0.1682 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 89: 288.75 sec +Time taken for epoch(SUBo) 89: 238.29 sec +<---------------------------------------|Epoch [89] END|---------------------------------------> + +Epoch: 90/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1505 - accuracy: 0.9502 - val_loss: 0.1977 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1613 - accuracy: 0.9478 - val_loss: 0.1510 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1232 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1183 - accuracy: 0.9658 - val_loss: 0.1810 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9717 - val_loss: 0.1728 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9658 - val_loss: 0.1794 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 90: 288.69 sec +Time taken for epoch(SUBo) 90: 237.88 sec +<---------------------------------------|Epoch [90] END|---------------------------------------> + +Epoch: 91/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1654 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1286 - accuracy: 0.9604 - val_loss: 0.2092 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1339 - accuracy: 0.9604 - val_loss: 0.1610 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1881 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9688 - val_loss: 0.2103 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0968 - accuracy: 0.9741 - val_loss: 0.2091 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 91: 290.04 sec +Time taken for epoch(SUBo) 91: 238.32 sec +<---------------------------------------|Epoch [91] END|---------------------------------------> + +Epoch: 92/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1806 - accuracy: 0.9453 - val_loss: 0.1973 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1625 - accuracy: 0.9502 - val_loss: 0.1934 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1476 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9551 - val_loss: 0.1942 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1282 - accuracy: 0.9580 - val_loss: 0.1883 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1260 - accuracy: 0.9619 - val_loss: 0.1955 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 92: 288.96 sec +Time taken for epoch(SUBo) 92: 237.66 sec +<---------------------------------------|Epoch [92] END|---------------------------------------> + +Epoch: 93/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1499 - accuracy: 0.9473 - val_loss: 0.1841 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1426 - accuracy: 0.9507 - val_loss: 0.2240 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1467 - accuracy: 0.9600 - val_loss: 0.1832 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1411 - accuracy: 0.9531 - val_loss: 0.4701 - val_accuracy: 0.8910 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1303 - accuracy: 0.9600 - val_loss: 0.3182 - val_accuracy: 0.9103 +Epoch 6/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.1197 - accuracy: 0.9692 - val_loss: 0.2972 - val_accuracy: 0.9151 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 93: 290.33 sec +Time taken for epoch(SUBo) 93: 239.01 sec +<---------------------------------------|Epoch [93] END|---------------------------------------> + +Epoch: 94/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1449 - accuracy: 0.9536 - val_loss: 0.2477 - val_accuracy: 0.9295 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1876 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9526 - val_loss: 0.2062 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1405 - accuracy: 0.9531 - val_loss: 0.1995 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1120 - accuracy: 0.9692 - val_loss: 0.2110 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9712 - val_loss: 0.2041 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 94: 289.36 sec +Time taken for epoch(SUBo) 94: 238.47 sec +<---------------------------------------|Epoch [94] END|---------------------------------------> + +Epoch: 95/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1489 - accuracy: 0.9580 - val_loss: 0.1769 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1445 - accuracy: 0.9512 - val_loss: 0.1728 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1269 - accuracy: 0.9565 - val_loss: 0.2260 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1205 - accuracy: 0.9624 - val_loss: 0.1696 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1278 - accuracy: 0.9624 - val_loss: 0.1737 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1040 - accuracy: 0.9707 - val_loss: 0.1714 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 95: 289.33 sec +Time taken for epoch(SUBo) 95: 238.40 sec +<---------------------------------------|Epoch [95] END|---------------------------------------> + +Epoch: 96/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1672 - accuracy: 0.9492 - val_loss: 0.1677 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1451 - accuracy: 0.9565 - val_loss: 0.1917 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9614 - val_loss: 0.2296 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.2639 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0987 - accuracy: 0.9717 - val_loss: 0.3081 - val_accuracy: 0.9215 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9653 - val_loss: 0.2600 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 96: 288.89 sec +Time taken for epoch(SUBo) 96: 237.88 sec +<---------------------------------------|Epoch [96] END|---------------------------------------> + +Epoch: 97/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1431 - accuracy: 0.9463 - val_loss: 0.2139 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1526 - accuracy: 0.9492 - val_loss: 0.2200 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1348 - accuracy: 0.9575 - val_loss: 0.2507 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9575 - val_loss: 0.2652 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9683 - val_loss: 0.2767 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1255 - accuracy: 0.9604 - val_loss: 0.2645 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 97: 288.48 sec +Time taken for epoch(SUBo) 97: 237.23 sec +<---------------------------------------|Epoch [97] END|---------------------------------------> + +Epoch: 98/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2275 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1329 - accuracy: 0.9614 - val_loss: 0.2393 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1515 - accuracy: 0.9556 - val_loss: 0.3716 - val_accuracy: 0.9135 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1402 - accuracy: 0.9595 - val_loss: 0.3404 - val_accuracy: 0.9087 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9712 - val_loss: 0.2649 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9648 - val_loss: 0.2462 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 98: 287.65 sec +Time taken for epoch(SUBo) 98: 237.26 sec +<---------------------------------------|Epoch [98] END|---------------------------------------> + +Epoch: 99/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.2086 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1320 - accuracy: 0.9580 - val_loss: 0.2175 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1388 - accuracy: 0.9556 - val_loss: 0.1846 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1222 - accuracy: 0.9658 - val_loss: 0.2280 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1001 - accuracy: 0.9692 - val_loss: 0.2335 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0935 - accuracy: 0.9741 - val_loss: 0.2289 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 99: 287.39 sec +Time taken for epoch(SUBo) 99: 237.52 sec +<---------------------------------------|Epoch [99] END|---------------------------------------> + +Epoch: 100/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1431 - accuracy: 0.9580 - val_loss: 0.2261 - val_accuracy: 0.9247 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1552 - accuracy: 0.9536 - val_loss: 0.1987 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1221 - accuracy: 0.9619 - val_loss: 0.2009 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9604 - val_loss: 0.2111 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1100 - accuracy: 0.9692 - val_loss: 0.2023 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0975 - accuracy: 0.9736 - val_loss: 0.1899 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 100: 287.11 sec +Time taken for epoch(SUBo) 100: 237.35 sec +<---------------------------------------|Epoch [100] END|---------------------------------------> + +Epoch: 101/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9541 - val_loss: 0.2182 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1364 - accuracy: 0.9629 - val_loss: 0.1850 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1349 - accuracy: 0.9600 - val_loss: 0.2381 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.1880 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1042 - accuracy: 0.9692 - val_loss: 0.2007 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0986 - accuracy: 0.9731 - val_loss: 0.2144 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 101: 287.74 sec +Time taken for epoch(SUBo) 101: 237.93 sec +<---------------------------------------|Epoch [101] END|---------------------------------------> + +Epoch: 102/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9570 - val_loss: 0.2415 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1164 - accuracy: 0.9653 - val_loss: 0.2319 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1270 - accuracy: 0.9658 - val_loss: 0.2692 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1342 - accuracy: 0.9629 - val_loss: 0.2067 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.1845 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1135 - accuracy: 0.9688 - val_loss: 0.2075 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 102: 288.00 sec +Time taken for epoch(SUBo) 102: 237.50 sec +<---------------------------------------|Epoch [102] END|---------------------------------------> + +Epoch: 103/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1454 - accuracy: 0.9531 - val_loss: 0.2672 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1464 - accuracy: 0.9556 - val_loss: 0.1568 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9614 - val_loss: 0.2431 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1267 - accuracy: 0.9595 - val_loss: 0.1676 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1114 - accuracy: 0.9648 - val_loss: 0.1947 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1131 - accuracy: 0.9688 - val_loss: 0.1926 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 103: 287.73 sec +Time taken for epoch(SUBo) 103: 237.64 sec +<---------------------------------------|Epoch [103] END|---------------------------------------> + +Epoch: 104/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1319 - accuracy: 0.9551 - val_loss: 0.2187 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1435 - accuracy: 0.9565 - val_loss: 0.2262 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1363 - accuracy: 0.9556 - val_loss: 0.1924 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1133 - accuracy: 0.9678 - val_loss: 0.2607 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9717 - val_loss: 0.2344 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1026 - accuracy: 0.9673 - val_loss: 0.2418 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 104: 286.90 sec +Time taken for epoch(SUBo) 104: 237.53 sec +<---------------------------------------|Epoch [104] END|---------------------------------------> + +Epoch: 105/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9580 - val_loss: 0.2079 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1239 - accuracy: 0.9600 - val_loss: 0.2032 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1005 - accuracy: 0.9722 - val_loss: 0.2134 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9688 - val_loss: 0.1937 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0898 - accuracy: 0.9741 - val_loss: 0.1968 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 105: 287.02 sec +Time taken for epoch(SUBo) 105: 237.52 sec +<---------------------------------------|Epoch [105] END|---------------------------------------> + +Epoch: 106/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1352 - accuracy: 0.9575 - val_loss: 0.1525 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1355 - accuracy: 0.9570 - val_loss: 0.1892 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1163 - accuracy: 0.9692 - val_loss: 0.1639 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1066 - accuracy: 0.9678 - val_loss: 0.1816 - val_accuracy: 0.9583 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0869 - accuracy: 0.9736 - val_loss: 0.1968 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0897 - accuracy: 0.9741 - val_loss: 0.2022 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 106: 287.48 sec +Time taken for epoch(SUBo) 106: 237.69 sec +<---------------------------------------|Epoch [106] END|---------------------------------------> + +Epoch: 107/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1194 - accuracy: 0.9644 - val_loss: 0.1767 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9668 - val_loss: 0.1995 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1046 - accuracy: 0.9663 - val_loss: 0.1818 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0864 - accuracy: 0.9746 - val_loss: 0.1969 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0910 - accuracy: 0.9722 - val_loss: 0.1441 - val_accuracy: 0.9663 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9653 - val_loss: 0.1590 - val_accuracy: 0.9696 +Subset training done. +Improved model accuracy from 0.9599359035491943 to 0.9695512652397156. Saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 107: 289.43 sec +Time taken for epoch(SUBo) 107: 237.56 sec +<---------------------------------------|Epoch [107] END|---------------------------------------> + +Epoch: 108/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1730 - accuracy: 0.9492 - val_loss: 0.1516 - val_accuracy: 0.9679 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1736 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.1854 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1192 - accuracy: 0.9658 - val_loss: 0.2242 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1115 - accuracy: 0.9663 - val_loss: 0.1922 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0976 - accuracy: 0.9722 - val_loss: 0.1996 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 108: 288.48 sec +Time taken for epoch(SUBo) 108: 238.16 sec +<---------------------------------------|Epoch [108] END|---------------------------------------> + +Epoch: 109/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1546 - accuracy: 0.9526 - val_loss: 0.1503 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1529 - accuracy: 0.9551 - val_loss: 0.1752 - val_accuracy: 0.9631 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.1519 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1593 - accuracy: 0.9492 - val_loss: 0.1787 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1744 - accuracy: 0.9434 - val_loss: 0.1705 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1520 - accuracy: 0.9502 - val_loss: 0.1609 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 109: 287.98 sec +Time taken for epoch(SUBo) 109: 238.06 sec +<---------------------------------------|Epoch [109] END|---------------------------------------> + +Epoch: 110/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1470 - accuracy: 0.9482 - val_loss: 0.1651 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1690 - accuracy: 0.9443 - val_loss: 0.2425 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1394 - accuracy: 0.9561 - val_loss: 0.1863 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9619 - val_loss: 0.1728 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1037 - accuracy: 0.9653 - val_loss: 0.1770 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0962 - accuracy: 0.9712 - val_loss: 0.1774 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 110: 288.95 sec +Time taken for epoch(SUBo) 110: 238.41 sec +<---------------------------------------|Epoch [110] END|---------------------------------------> + +Epoch: 111/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1625 - accuracy: 0.9487 - val_loss: 0.1659 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1540 - accuracy: 0.9556 - val_loss: 0.1548 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1736 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1230 - accuracy: 0.9639 - val_loss: 0.2110 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1110 - accuracy: 0.9717 - val_loss: 0.1803 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1079 - accuracy: 0.9688 - val_loss: 0.1742 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 111: 288.76 sec +Time taken for epoch(SUBo) 111: 238.28 sec +<---------------------------------------|Epoch [111] END|---------------------------------------> + +Epoch: 112/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1898 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9473 - val_loss: 0.2439 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1295 - accuracy: 0.9614 - val_loss: 0.2080 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1483 - accuracy: 0.9604 - val_loss: 0.2009 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.2107 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2227 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 112: 288.69 sec +Time taken for epoch(SUBo) 112: 237.84 sec +<---------------------------------------|Epoch [112] END|---------------------------------------> + +Epoch: 113/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1289 - accuracy: 0.9604 - val_loss: 0.1870 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1315 - accuracy: 0.9619 - val_loss: 0.1862 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1271 - accuracy: 0.9604 - val_loss: 0.1778 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9707 - val_loss: 0.1887 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2135 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0856 - accuracy: 0.9741 - val_loss: 0.2159 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 113: 289.27 sec +Time taken for epoch(SUBo) 113: 237.88 sec +<---------------------------------------|Epoch [113] END|---------------------------------------> + +Epoch: 114/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1358 - accuracy: 0.9595 - val_loss: 0.1854 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9644 - val_loss: 0.2141 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2008 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9639 - val_loss: 0.1953 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1022 - accuracy: 0.9663 - val_loss: 0.1951 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0806 - accuracy: 0.9775 - val_loss: 0.1923 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 114: 288.83 sec +Time taken for epoch(SUBo) 114: 237.68 sec +<---------------------------------------|Epoch [114] END|---------------------------------------> + +Epoch: 115/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1186 - accuracy: 0.9600 - val_loss: 0.2549 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1196 - accuracy: 0.9604 - val_loss: 0.2198 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1253 - accuracy: 0.9590 - val_loss: 0.2396 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1043 - accuracy: 0.9736 - val_loss: 0.2314 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0960 - accuracy: 0.9712 - val_loss: 0.2056 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0915 - accuracy: 0.9722 - val_loss: 0.2126 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 115: 289.11 sec +Time taken for epoch(SUBo) 115: 238.53 sec +<---------------------------------------|Epoch [115] END|---------------------------------------> + +Epoch: 116/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2195 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1368 - accuracy: 0.9595 - val_loss: 0.1903 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1198 - accuracy: 0.9614 - val_loss: 0.2051 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1856 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1008 - accuracy: 0.9702 - val_loss: 0.1742 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1027 - accuracy: 0.9717 - val_loss: 0.1697 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 116: 289.60 sec +Time taken for epoch(SUBo) 116: 239.21 sec +<---------------------------------------|Epoch [116] END|---------------------------------------> + +Epoch: 117/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.1718 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9580 - val_loss: 0.2046 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0925 - accuracy: 0.9722 - val_loss: 0.2292 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0834 - accuracy: 0.9751 - val_loss: 0.2023 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9727 - val_loss: 0.2151 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1000 - accuracy: 0.9722 - val_loss: 0.2206 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 117: 294.65 sec +Time taken for epoch(SUBo) 117: 244.16 sec +<---------------------------------------|Epoch [117] END|---------------------------------------> + +Epoch: 118/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1199 - accuracy: 0.9644 - val_loss: 0.2294 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1139 - accuracy: 0.9663 - val_loss: 0.1655 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1037 - accuracy: 0.9707 - val_loss: 0.1589 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0889 - accuracy: 0.9741 - val_loss: 0.2250 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0840 - accuracy: 0.9785 - val_loss: 0.1895 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0828 - accuracy: 0.9727 - val_loss: 0.1852 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 118: 295.73 sec +Time taken for epoch(SUBo) 118: 244.43 sec +<---------------------------------------|Epoch [118] END|---------------------------------------> + +Epoch: 119/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1416 - accuracy: 0.9585 - val_loss: 0.1226 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1682 - accuracy: 0.9434 - val_loss: 0.1301 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1486 - accuracy: 0.9497 - val_loss: 0.1562 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1247 - accuracy: 0.9604 - val_loss: 0.1408 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1476 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1120 - accuracy: 0.9629 - val_loss: 0.1468 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.15118563175201416 to 0.146798238158226. Saving model. +Time taken for epoch(FULL) 119: 296.83 sec +Time taken for epoch(SUBo) 119: 244.81 sec +<---------------------------------------|Epoch [119] END|---------------------------------------> + +Epoch: 120/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1305 - accuracy: 0.9570 - val_loss: 0.1442 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1428 - accuracy: 0.9551 - val_loss: 0.1382 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9653 - val_loss: 0.1388 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1095 - accuracy: 0.9692 - val_loss: 0.1446 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0795 - accuracy: 0.9790 - val_loss: 0.1430 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1469 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 120: 295.05 sec +Time taken for epoch(SUBo) 120: 244.35 sec +<---------------------------------------|Epoch [120] END|---------------------------------------> + +Epoch: 121/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.1539 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1415 - accuracy: 0.9541 - val_loss: 0.1573 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1153 - accuracy: 0.9717 - val_loss: 0.1778 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1108 - accuracy: 0.9683 - val_loss: 0.1774 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9697 - val_loss: 0.1738 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9727 - val_loss: 0.1716 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 121: 294.56 sec +Time taken for epoch(SUBo) 121: 244.29 sec +<---------------------------------------|Epoch [121] END|---------------------------------------> + +Epoch: 122/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1261 - accuracy: 0.9619 - val_loss: 0.1905 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9634 - val_loss: 0.1801 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1278 - accuracy: 0.9580 - val_loss: 0.2058 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9663 - val_loss: 0.2683 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1103 - accuracy: 0.9648 - val_loss: 0.1943 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 122: 295.23 sec +Time taken for epoch(SUBo) 122: 244.50 sec +<---------------------------------------|Epoch [122] END|---------------------------------------> + +Epoch: 123/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1423 - accuracy: 0.9570 - val_loss: 0.1759 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9624 - val_loss: 0.2300 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1347 - accuracy: 0.9600 - val_loss: 0.2434 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1360 - accuracy: 0.9565 - val_loss: 0.2215 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1029 - accuracy: 0.9678 - val_loss: 0.2258 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1030 - accuracy: 0.9658 - val_loss: 0.1975 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 123: 294.95 sec +Time taken for epoch(SUBo) 123: 244.32 sec +<---------------------------------------|Epoch [123] END|---------------------------------------> + +Epoch: 124/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1253 - accuracy: 0.9614 - val_loss: 0.2786 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9600 - val_loss: 0.2731 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1414 - accuracy: 0.9575 - val_loss: 0.2149 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1280 - accuracy: 0.9609 - val_loss: 0.2693 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.2356 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.2349 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 124: 294.95 sec +Time taken for epoch(SUBo) 124: 244.30 sec +<---------------------------------------|Epoch [124] END|---------------------------------------> + +Epoch: 125/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1388 - accuracy: 0.9570 - val_loss: 0.2241 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1322 - accuracy: 0.9595 - val_loss: 0.2067 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1604 - accuracy: 0.9448 - val_loss: 0.2070 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1206 - accuracy: 0.9629 - val_loss: 0.1951 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1370 - accuracy: 0.9556 - val_loss: 0.1795 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9614 - val_loss: 0.1803 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 125: 296.66 sec +Time taken for epoch(SUBo) 125: 245.25 sec +<---------------------------------------|Epoch [125] END|---------------------------------------> + +Epoch: 126/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9443 - val_loss: 0.1636 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1469 - accuracy: 0.9531 - val_loss: 0.1743 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9600 - val_loss: 0.2001 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1122 - accuracy: 0.9634 - val_loss: 0.2148 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9692 - val_loss: 0.1990 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0975 - accuracy: 0.9727 - val_loss: 0.1967 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 126: 296.05 sec +Time taken for epoch(SUBo) 126: 244.69 sec +<---------------------------------------|Epoch [126] END|---------------------------------------> + +Epoch: 127/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1350 - accuracy: 0.9590 - val_loss: 0.2002 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9604 - val_loss: 0.1730 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1136 - accuracy: 0.9658 - val_loss: 0.2452 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0970 - accuracy: 0.9756 - val_loss: 0.2381 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0872 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0813 - accuracy: 0.9761 - val_loss: 0.2530 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 127: 295.58 sec +Time taken for epoch(SUBo) 127: 244.41 sec +<---------------------------------------|Epoch [127] END|---------------------------------------> + +Epoch: 128/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1365 - accuracy: 0.9521 - val_loss: 0.1995 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1957 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1184 - accuracy: 0.9609 - val_loss: 0.1864 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1086 - accuracy: 0.9712 - val_loss: 0.2123 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1137 - accuracy: 0.9653 - val_loss: 0.1765 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1008 - accuracy: 0.9697 - val_loss: 0.1619 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 128: 303.71 sec +Time taken for epoch(SUBo) 128: 244.02 sec +<---------------------------------------|Epoch [128] END|---------------------------------------> + +Epoch: 129/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1492 - accuracy: 0.9492 - val_loss: 0.1890 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1478 - accuracy: 0.9565 - val_loss: 0.1770 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1285 - accuracy: 0.9609 - val_loss: 0.1963 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1629 - val_accuracy: 0.9599 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1027 - accuracy: 0.9722 - val_loss: 0.1720 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0962 - accuracy: 0.9722 - val_loss: 0.1728 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 129: 304.31 sec +Time taken for epoch(SUBo) 129: 243.77 sec +<---------------------------------------|Epoch [129] END|---------------------------------------> + +Epoch: 130/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1344 - accuracy: 0.9595 - val_loss: 0.1606 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1276 - accuracy: 0.9624 - val_loss: 0.1791 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.1730 - val_accuracy: 0.9615 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1088 - accuracy: 0.9683 - val_loss: 0.1984 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1004 - accuracy: 0.9668 - val_loss: 0.2138 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1041 - accuracy: 0.9683 - val_loss: 0.1963 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 130: 301.26 sec +Time taken for epoch(SUBo) 130: 244.07 sec +<---------------------------------------|Epoch [130] END|---------------------------------------> + +Epoch: 131/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1314 - accuracy: 0.9614 - val_loss: 0.1733 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9556 - val_loss: 0.1815 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9639 - val_loss: 0.1522 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1197 - accuracy: 0.9644 - val_loss: 0.1593 - val_accuracy: 0.9615 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1065 - accuracy: 0.9707 - val_loss: 0.1619 - val_accuracy: 0.9615 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0984 - accuracy: 0.9697 - val_loss: 0.1596 - val_accuracy: 0.9631 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 131: 300.73 sec +Time taken for epoch(SUBo) 131: 244.44 sec +<---------------------------------------|Epoch [131] END|---------------------------------------> + +Epoch: 132/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1359 - accuracy: 0.9590 - val_loss: 0.1611 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1136 - accuracy: 0.9644 - val_loss: 0.1692 - val_accuracy: 0.9615 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1270 - accuracy: 0.9629 - val_loss: 0.2881 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1380 - accuracy: 0.9609 - val_loss: 0.1959 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1193 - accuracy: 0.9658 - val_loss: 0.2176 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1125 - accuracy: 0.9648 - val_loss: 0.2147 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 132: 298.91 sec +Time taken for epoch(SUBo) 132: 243.60 sec +<---------------------------------------|Epoch [132] END|---------------------------------------> + +Epoch: 133/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1469 - accuracy: 0.9521 - val_loss: 0.2294 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1442 - accuracy: 0.9580 - val_loss: 0.2275 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1246 - accuracy: 0.9619 - val_loss: 0.2881 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1150 - accuracy: 0.9673 - val_loss: 0.2647 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1132 - accuracy: 0.9648 - val_loss: 0.2474 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0897 - accuracy: 0.9751 - val_loss: 0.2609 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 133: 296.74 sec +Time taken for epoch(SUBo) 133: 242.77 sec +<---------------------------------------|Epoch [133] END|---------------------------------------> + +Epoch: 134/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1280 - accuracy: 0.9604 - val_loss: 0.2374 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1308 - accuracy: 0.9590 - val_loss: 0.2543 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1377 - accuracy: 0.9565 - val_loss: 0.2752 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1032 - accuracy: 0.9736 - val_loss: 0.2675 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9663 - val_loss: 0.2584 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0954 - accuracy: 0.9756 - val_loss: 0.2853 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 134: 297.41 sec +Time taken for epoch(SUBo) 134: 243.12 sec +<---------------------------------------|Epoch [134] END|---------------------------------------> + +Epoch: 135/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1629 - accuracy: 0.9482 - val_loss: 0.2191 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1362 - accuracy: 0.9575 - val_loss: 0.2275 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1400 - accuracy: 0.9570 - val_loss: 0.1914 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1302 - accuracy: 0.9639 - val_loss: 0.1995 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9653 - val_loss: 0.2003 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1085 - accuracy: 0.9697 - val_loss: 0.2064 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 135: 298.46 sec +Time taken for epoch(SUBo) 135: 243.19 sec +<---------------------------------------|Epoch [135] END|---------------------------------------> + +Epoch: 136/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1415 - accuracy: 0.9561 - val_loss: 0.1941 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1323 - accuracy: 0.9648 - val_loss: 0.2252 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.1982 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2166 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9678 - val_loss: 0.2508 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0991 - accuracy: 0.9707 - val_loss: 0.2181 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 136: 300.20 sec +Time taken for epoch(SUBo) 136: 243.49 sec +<---------------------------------------|Epoch [136] END|---------------------------------------> + +Epoch: 137/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1551 - accuracy: 0.9531 - val_loss: 0.2049 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1405 - accuracy: 0.9546 - val_loss: 0.2349 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1254 - accuracy: 0.9595 - val_loss: 0.1758 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1130 - accuracy: 0.9634 - val_loss: 0.2124 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0963 - accuracy: 0.9736 - val_loss: 0.1902 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.1870 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 137: 300.02 sec +Time taken for epoch(SUBo) 137: 243.90 sec +<---------------------------------------|Epoch [137] END|---------------------------------------> + +Epoch: 138/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1243 - accuracy: 0.9644 - val_loss: 0.1907 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1289 - accuracy: 0.9590 - val_loss: 0.1533 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1203 - accuracy: 0.9604 - val_loss: 0.1708 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.1635 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0951 - accuracy: 0.9736 - val_loss: 0.1628 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0872 - accuracy: 0.9756 - val_loss: 0.1781 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 138: 298.57 sec +Time taken for epoch(SUBo) 138: 243.89 sec +<---------------------------------------|Epoch [138] END|---------------------------------------> + +Epoch: 139/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1322 - accuracy: 0.9629 - val_loss: 0.1652 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1569 - accuracy: 0.9458 - val_loss: 0.2143 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2487 - val_accuracy: 0.9231 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1343 - accuracy: 0.9585 - val_loss: 0.1756 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1018 - accuracy: 0.9678 - val_loss: 0.1879 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0864 - accuracy: 0.9751 - val_loss: 0.2002 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 139: 296.96 sec +Time taken for epoch(SUBo) 139: 243.53 sec +<---------------------------------------|Epoch [139] END|---------------------------------------> + +Epoch: 140/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1588 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9595 - val_loss: 0.1786 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1241 - accuracy: 0.9619 - val_loss: 0.1725 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1104 - accuracy: 0.9683 - val_loss: 0.1877 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1057 - accuracy: 0.9702 - val_loss: 0.1923 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1891 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 140: 298.07 sec +Time taken for epoch(SUBo) 140: 243.40 sec +<---------------------------------------|Epoch [140] END|---------------------------------------> + +Epoch: 141/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1314 - accuracy: 0.9541 - val_loss: 0.1613 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.1692 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1292 - accuracy: 0.9580 - val_loss: 0.1645 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9673 - val_loss: 0.1783 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0957 - accuracy: 0.9727 - val_loss: 0.1860 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1725 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 141: 298.52 sec +Time taken for epoch(SUBo) 141: 243.77 sec +<---------------------------------------|Epoch [141] END|---------------------------------------> + +Epoch: 142/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.1811 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1378 - accuracy: 0.9536 - val_loss: 0.1458 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1216 - accuracy: 0.9614 - val_loss: 0.1723 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1112 - accuracy: 0.9683 - val_loss: 0.1895 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1075 - accuracy: 0.9707 - val_loss: 0.1709 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1590 - val_accuracy: 0.9599 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 142: 297.84 sec +Time taken for epoch(SUBo) 142: 243.24 sec +<---------------------------------------|Epoch [142] END|---------------------------------------> + +Epoch: 143/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1446 - accuracy: 0.9512 - val_loss: 0.1575 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9600 - val_loss: 0.1438 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1499 - accuracy: 0.9556 - val_loss: 0.1531 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1312 - accuracy: 0.9575 - val_loss: 0.1520 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9629 - val_loss: 0.1651 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1007 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 143: 296.59 sec +Time taken for epoch(SUBo) 143: 243.29 sec +<---------------------------------------|Epoch [143] END|---------------------------------------> + +Epoch: 144/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 158ms/step - loss: 0.1502 - accuracy: 0.9531 - val_loss: 0.1520 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1484 - accuracy: 0.9536 - val_loss: 0.1554 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1475 - accuracy: 0.9575 - val_loss: 0.1452 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1296 - accuracy: 0.9624 - val_loss: 0.1943 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1104 - accuracy: 0.9648 - val_loss: 0.1803 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0984 - accuracy: 0.9736 - val_loss: 0.1858 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 144: 296.73 sec +Time taken for epoch(SUBo) 144: 242.88 sec +<---------------------------------------|Epoch [144] END|---------------------------------------> + +Epoch: 145/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1262 - accuracy: 0.9600 - val_loss: 0.1634 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1191 - accuracy: 0.9639 - val_loss: 0.1680 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1056 - accuracy: 0.9658 - val_loss: 0.1970 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1031 - accuracy: 0.9707 - val_loss: 0.2054 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0822 - accuracy: 0.9800 - val_loss: 0.2039 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0879 - accuracy: 0.9746 - val_loss: 0.2102 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 145: 298.13 sec +Time taken for epoch(SUBo) 145: 242.21 sec +<---------------------------------------|Epoch [145] END|---------------------------------------> + +Epoch: 146/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1362 - accuracy: 0.9570 - val_loss: 0.1822 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1300 - accuracy: 0.9595 - val_loss: 0.2085 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1156 - accuracy: 0.9629 - val_loss: 0.2197 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0958 - accuracy: 0.9761 - val_loss: 0.2403 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.2088 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0887 - accuracy: 0.9702 - val_loss: 0.2360 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 146: 301.03 sec +Time taken for epoch(SUBo) 146: 242.33 sec +<---------------------------------------|Epoch [146] END|---------------------------------------> + +Epoch: 147/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1234 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9614 - val_loss: 0.1836 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1030 - accuracy: 0.9717 - val_loss: 0.1736 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0980 - accuracy: 0.9707 - val_loss: 0.1931 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0948 - accuracy: 0.9722 - val_loss: 0.1875 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1813 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 147: 303.25 sec +Time taken for epoch(SUBo) 147: 242.94 sec +<---------------------------------------|Epoch [147] END|---------------------------------------> + +Epoch: 148/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1321 - accuracy: 0.9565 - val_loss: 0.2085 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1171 - accuracy: 0.9629 - val_loss: 0.1716 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1633 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1642 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1000 - accuracy: 0.9702 - val_loss: 0.1597 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0804 - accuracy: 0.9756 - val_loss: 0.1575 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 148: 301.98 sec +Time taken for epoch(SUBo) 148: 243.14 sec +<---------------------------------------|Epoch [148] END|---------------------------------------> + +Epoch: 149/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1178 - accuracy: 0.9634 - val_loss: 0.1412 - val_accuracy: 0.9615 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1271 - accuracy: 0.9580 - val_loss: 0.1553 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1074 - accuracy: 0.9658 - val_loss: 0.1972 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0920 - accuracy: 0.9741 - val_loss: 0.1781 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1054 - accuracy: 0.9692 - val_loss: 0.1791 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0850 - accuracy: 0.9761 - val_loss: 0.1786 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 149: 298.73 sec +Time taken for epoch(SUBo) 149: 242.85 sec +<---------------------------------------|Epoch [149] END|---------------------------------------> + +Epoch: 150/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1315 - accuracy: 0.9580 - val_loss: 0.1966 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1324 - accuracy: 0.9551 - val_loss: 0.2153 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1131 - accuracy: 0.9634 - val_loss: 0.2608 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1028 - accuracy: 0.9697 - val_loss: 0.2539 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0900 - accuracy: 0.9707 - val_loss: 0.2782 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1002 - accuracy: 0.9697 - val_loss: 0.2693 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 150: 300.25 sec +Time taken for epoch(SUBo) 150: 243.69 sec +<---------------------------------------|Epoch [150] END|---------------------------------------> + +Epoch: 151/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2125 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1103 - accuracy: 0.9712 - val_loss: 0.2087 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1040 - accuracy: 0.9653 - val_loss: 0.2110 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0983 - accuracy: 0.9727 - val_loss: 0.1971 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1968 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0845 - accuracy: 0.9751 - val_loss: 0.2230 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 151: 298.97 sec +Time taken for epoch(SUBo) 151: 242.93 sec +<---------------------------------------|Epoch [151] END|---------------------------------------> + +Epoch: 152/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1268 - accuracy: 0.9663 - val_loss: 0.2006 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9678 - val_loss: 0.1805 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1365 - accuracy: 0.9565 - val_loss: 0.1432 - val_accuracy: 0.9631 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1488 - accuracy: 0.9517 - val_loss: 0.1688 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1657 - accuracy: 0.9458 - val_loss: 0.1674 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1403 - accuracy: 0.9561 - val_loss: 0.1698 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 152: 302.68 sec +Time taken for epoch(SUBo) 152: 243.68 sec +<---------------------------------------|Epoch [152] END|---------------------------------------> + +Epoch: 153/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1499 - accuracy: 0.9507 - val_loss: 0.1872 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1414 - accuracy: 0.9580 - val_loss: 0.1947 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1562 - accuracy: 0.9463 - val_loss: 0.2135 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9629 - val_loss: 0.1884 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9712 - val_loss: 0.2042 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9712 - val_loss: 0.1936 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 153: 299.14 sec +Time taken for epoch(SUBo) 153: 243.89 sec +<---------------------------------------|Epoch [153] END|---------------------------------------> + +Epoch: 154/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1391 - accuracy: 0.9531 - val_loss: 0.1623 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1460 - accuracy: 0.9497 - val_loss: 0.2164 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1347 - accuracy: 0.9619 - val_loss: 0.4024 - val_accuracy: 0.8686 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2569 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1417 - accuracy: 0.9546 - val_loss: 0.2886 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2901 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 154: 303.43 sec +Time taken for epoch(SUBo) 154: 244.09 sec +<---------------------------------------|Epoch [154] END|---------------------------------------> + +Epoch: 155/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1674 - accuracy: 0.9424 - val_loss: 0.2398 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1466 - accuracy: 0.9556 - val_loss: 0.2424 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1350 - accuracy: 0.9565 - val_loss: 0.2398 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1153 - accuracy: 0.9639 - val_loss: 0.2173 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9692 - val_loss: 0.2637 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2615 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 155: 299.62 sec +Time taken for epoch(SUBo) 155: 243.67 sec +<---------------------------------------|Epoch [155] END|---------------------------------------> + +Epoch: 156/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9434 - val_loss: 0.2209 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1493 - accuracy: 0.9517 - val_loss: 0.2582 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1431 - accuracy: 0.9502 - val_loss: 0.2281 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9551 - val_loss: 0.2542 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1168 - accuracy: 0.9600 - val_loss: 0.1981 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9531 - val_loss: 0.2167 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 156: 301.27 sec +Time taken for epoch(SUBo) 156: 244.46 sec +<---------------------------------------|Epoch [156] END|---------------------------------------> + +Epoch: 157/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1338 - accuracy: 0.9565 - val_loss: 0.2626 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1420 - accuracy: 0.9473 - val_loss: 0.3502 - val_accuracy: 0.9215 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1291 - accuracy: 0.9585 - val_loss: 0.2344 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1022 - accuracy: 0.9683 - val_loss: 0.2722 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1164 - accuracy: 0.9648 - val_loss: 0.2915 - val_accuracy: 0.9215 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2660 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 157: 298.53 sec +Time taken for epoch(SUBo) 157: 243.70 sec +<---------------------------------------|Epoch [157] END|---------------------------------------> + +Epoch: 158/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1569 - accuracy: 0.9517 - val_loss: 0.2548 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1227 - accuracy: 0.9609 - val_loss: 0.3033 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9575 - val_loss: 0.2928 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1117 - accuracy: 0.9663 - val_loss: 0.2713 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9717 - val_loss: 0.2921 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0927 - accuracy: 0.9741 - val_loss: 0.2760 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 158: 305.20 sec +Time taken for epoch(SUBo) 158: 244.85 sec +<---------------------------------------|Epoch [158] END|---------------------------------------> + +Epoch: 159/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1135 - accuracy: 0.9668 - val_loss: 0.2714 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1001 - accuracy: 0.9663 - val_loss: 0.3513 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0937 - accuracy: 0.9712 - val_loss: 0.2725 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0861 - accuracy: 0.9780 - val_loss: 0.2921 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0836 - accuracy: 0.9751 - val_loss: 0.2788 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0809 - accuracy: 0.9780 - val_loss: 0.2651 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 159: 306.51 sec +Time taken for epoch(SUBo) 159: 245.04 sec +<---------------------------------------|Epoch [159] END|---------------------------------------> + +Epoch: 160/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1241 - accuracy: 0.9609 - val_loss: 0.2724 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9570 - val_loss: 0.2510 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1102 - accuracy: 0.9653 - val_loss: 0.2081 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1101 - accuracy: 0.9702 - val_loss: 0.1942 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.2166 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0885 - accuracy: 0.9727 - val_loss: 0.2052 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 160: 305.10 sec +Time taken for epoch(SUBo) 160: 245.83 sec +<---------------------------------------|Epoch [160] END|---------------------------------------> + +Epoch: 161/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1290 - accuracy: 0.9614 - val_loss: 0.1891 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9575 - val_loss: 0.1965 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2083 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1031 - accuracy: 0.9678 - val_loss: 0.2418 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1070 - accuracy: 0.9678 - val_loss: 0.2420 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0859 - accuracy: 0.9761 - val_loss: 0.2691 - val_accuracy: 0.9247 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 161: 299.90 sec +Time taken for epoch(SUBo) 161: 244.85 sec +<---------------------------------------|Epoch [161] END|---------------------------------------> + +Epoch: 162/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1228 - accuracy: 0.9629 - val_loss: 0.2065 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1999 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1606 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1366 - accuracy: 0.9575 - val_loss: 0.2026 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9619 - val_loss: 0.2040 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1096 - accuracy: 0.9673 - val_loss: 0.2063 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 162: 299.56 sec +Time taken for epoch(SUBo) 162: 244.33 sec +<---------------------------------------|Epoch [162] END|---------------------------------------> + +Epoch: 163/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1399 - accuracy: 0.9565 - val_loss: 0.2292 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1215 - accuracy: 0.9585 - val_loss: 0.2450 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1078 - accuracy: 0.9648 - val_loss: 0.2188 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1154 - accuracy: 0.9648 - val_loss: 0.2537 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9619 - val_loss: 0.2278 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9634 - val_loss: 0.2206 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 163: 297.49 sec +Time taken for epoch(SUBo) 163: 243.39 sec +<---------------------------------------|Epoch [163] END|---------------------------------------> + +Epoch: 164/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1580 - accuracy: 0.9507 - val_loss: 0.2399 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9570 - val_loss: 0.2307 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1342 - accuracy: 0.9604 - val_loss: 0.1897 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.2260 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9668 - val_loss: 0.2024 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0980 - accuracy: 0.9673 - val_loss: 0.2013 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 164: 300.36 sec +Time taken for epoch(SUBo) 164: 244.25 sec +<---------------------------------------|Epoch [164] END|---------------------------------------> + +Epoch: 165/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1589 - accuracy: 0.9497 - val_loss: 0.1661 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9575 - val_loss: 0.2048 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1393 - accuracy: 0.9600 - val_loss: 0.1941 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.2079 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1092 - accuracy: 0.9688 - val_loss: 0.2288 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9761 - val_loss: 0.2080 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 165: 307.38 sec +Time taken for epoch(SUBo) 165: 245.49 sec +<---------------------------------------|Epoch [165] END|---------------------------------------> + +Epoch: 166/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1276 - accuracy: 0.9585 - val_loss: 0.2018 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1107 - accuracy: 0.9673 - val_loss: 0.1818 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1782 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9731 - val_loss: 0.1845 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0775 - accuracy: 0.9756 - val_loss: 0.1787 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 166: 306.37 sec +Time taken for epoch(SUBo) 166: 244.99 sec +<---------------------------------------|Epoch [166] END|---------------------------------------> + +Epoch: 167/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 162ms/step - loss: 0.1360 - accuracy: 0.9585 - val_loss: 0.1928 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.1949 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1286 - accuracy: 0.9600 - val_loss: 0.2223 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1548 - accuracy: 0.9487 - val_loss: 0.3237 - val_accuracy: 0.9199 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1733 - accuracy: 0.9395 - val_loss: 0.2911 - val_accuracy: 0.9135 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9565 - val_loss: 0.2720 - val_accuracy: 0.9231 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 167: 302.14 sec +Time taken for epoch(SUBo) 167: 244.91 sec +<---------------------------------------|Epoch [167] END|---------------------------------------> + +Epoch: 168/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1783 - accuracy: 0.9365 - val_loss: 0.3662 - val_accuracy: 0.9006 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1679 - accuracy: 0.9419 - val_loss: 0.2450 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1442 - accuracy: 0.9512 - val_loss: 0.2916 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1321 - accuracy: 0.9575 - val_loss: 0.3255 - val_accuracy: 0.9231 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1195 - accuracy: 0.9624 - val_loss: 0.3551 - val_accuracy: 0.9199 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1084 - accuracy: 0.9668 - val_loss: 0.3794 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 168: 299.21 sec +Time taken for epoch(SUBo) 168: 243.84 sec +<---------------------------------------|Epoch [168] END|---------------------------------------> + +Epoch: 169/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1427 - accuracy: 0.9624 - val_loss: 0.2396 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1854 - accuracy: 0.9336 - val_loss: 0.2213 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1539 - accuracy: 0.9458 - val_loss: 0.2068 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1354 - accuracy: 0.9585 - val_loss: 0.3011 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1135 - accuracy: 0.9629 - val_loss: 0.2591 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1127 - accuracy: 0.9629 - val_loss: 0.2691 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 169: 300.15 sec +Time taken for epoch(SUBo) 169: 243.82 sec +<---------------------------------------|Epoch [169] END|---------------------------------------> + +Epoch: 170/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1595 - accuracy: 0.9438 - val_loss: 0.2370 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1465 - accuracy: 0.9492 - val_loss: 0.1867 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1322 - accuracy: 0.9565 - val_loss: 0.2246 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1295 - accuracy: 0.9609 - val_loss: 0.2039 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1179 - accuracy: 0.9644 - val_loss: 0.1999 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2048 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 170: 299.63 sec +Time taken for epoch(SUBo) 170: 242.91 sec +<---------------------------------------|Epoch [170] END|---------------------------------------> + +Epoch: 171/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1650 - accuracy: 0.9541 - val_loss: 0.1615 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9565 - val_loss: 0.1734 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1397 - accuracy: 0.9570 - val_loss: 0.1680 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1035 - accuracy: 0.9741 - val_loss: 0.1722 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1021 - accuracy: 0.9668 - val_loss: 0.1847 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.1761 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 171: 304.06 sec +Time taken for epoch(SUBo) 171: 243.87 sec +<---------------------------------------|Epoch [171] END|---------------------------------------> + +Epoch: 172/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1201 - accuracy: 0.9629 - val_loss: 0.1739 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9614 - val_loss: 0.1925 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1138 - accuracy: 0.9673 - val_loss: 0.2372 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1000 - accuracy: 0.9697 - val_loss: 0.1883 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0899 - accuracy: 0.9731 - val_loss: 0.2044 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0740 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 172: 304.33 sec +Time taken for epoch(SUBo) 172: 243.65 sec +<---------------------------------------|Epoch [172] END|---------------------------------------> + +Epoch: 173/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1373 - accuracy: 0.9541 - val_loss: 0.1948 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1472 - accuracy: 0.9502 - val_loss: 0.2673 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1669 - accuracy: 0.9453 - val_loss: 0.1954 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.1729 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9629 - val_loss: 0.2251 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1095 - accuracy: 0.9658 - val_loss: 0.2223 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 173: 302.38 sec +Time taken for epoch(SUBo) 173: 244.10 sec +<---------------------------------------|Epoch [173] END|---------------------------------------> + +Epoch: 174/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.2098 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1407 - accuracy: 0.9561 - val_loss: 0.2066 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1279 - accuracy: 0.9609 - val_loss: 0.2408 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1170 - accuracy: 0.9629 - val_loss: 0.2116 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.2266 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0989 - accuracy: 0.9722 - val_loss: 0.2566 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 174: 298.38 sec +Time taken for epoch(SUBo) 174: 242.96 sec +<---------------------------------------|Epoch [174] END|---------------------------------------> + +Epoch: 175/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1366 - accuracy: 0.9546 - val_loss: 0.2196 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1153 - accuracy: 0.9619 - val_loss: 0.2363 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1186 - accuracy: 0.9624 - val_loss: 0.2094 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9683 - val_loss: 0.2792 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0901 - accuracy: 0.9736 - val_loss: 0.2793 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0818 - accuracy: 0.9751 - val_loss: 0.3102 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 175: 298.34 sec +Time taken for epoch(SUBo) 175: 243.27 sec +<---------------------------------------|Epoch [175] END|---------------------------------------> + +Epoch: 176/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1217 - accuracy: 0.9561 - val_loss: 0.3390 - val_accuracy: 0.8894 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1363 - accuracy: 0.9600 - val_loss: 0.3365 - val_accuracy: 0.9151 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9580 - val_loss: 0.2768 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1262 - accuracy: 0.9629 - val_loss: 0.2921 - val_accuracy: 0.9135 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0952 - accuracy: 0.9717 - val_loss: 0.3173 - val_accuracy: 0.9151 +Epoch 6/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0972 - accuracy: 0.9731 - val_loss: 0.3247 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 176: 300.75 sec +Time taken for epoch(SUBo) 176: 244.46 sec +<---------------------------------------|Epoch [176] END|---------------------------------------> + +Epoch: 177/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1301 - accuracy: 0.9600 - val_loss: 0.2746 - val_accuracy: 0.9215 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1191 - accuracy: 0.9658 - val_loss: 0.2657 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1160 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0987 - accuracy: 0.9722 - val_loss: 0.2429 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0863 - accuracy: 0.9756 - val_loss: 0.2320 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0852 - accuracy: 0.9771 - val_loss: 0.2548 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 177: 307.13 sec +Time taken for epoch(SUBo) 177: 245.28 sec +<---------------------------------------|Epoch [177] END|---------------------------------------> + +Epoch: 178/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1285 - accuracy: 0.9634 - val_loss: 0.1938 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1361 - accuracy: 0.9551 - val_loss: 0.2198 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1310 - accuracy: 0.9614 - val_loss: 0.2257 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1178 - accuracy: 0.9658 - val_loss: 0.1883 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1097 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9697 - val_loss: 0.2949 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 178: 307.31 sec +Time taken for epoch(SUBo) 178: 246.17 sec +<---------------------------------------|Epoch [178] END|---------------------------------------> + +Epoch: 179/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1366 - accuracy: 0.9551 - val_loss: 0.2232 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1883 - accuracy: 0.9370 - val_loss: 0.2155 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1590 - accuracy: 0.9492 - val_loss: 0.2392 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1456 - accuracy: 0.9517 - val_loss: 0.2673 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2418 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1098 - accuracy: 0.9658 - val_loss: 0.2398 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 179: 304.66 sec +Time taken for epoch(SUBo) 179: 246.00 sec +<---------------------------------------|Epoch [179] END|---------------------------------------> + +Epoch: 180/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1470 - accuracy: 0.9546 - val_loss: 0.2427 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1592 - accuracy: 0.9521 - val_loss: 0.3052 - val_accuracy: 0.9103 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1297 - accuracy: 0.9629 - val_loss: 0.2849 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9551 - val_loss: 0.2115 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1155 - accuracy: 0.9644 - val_loss: 0.2489 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1184 - accuracy: 0.9648 - val_loss: 0.2458 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 180: 303.24 sec +Time taken for epoch(SUBo) 180: 245.01 sec +<---------------------------------------|Epoch [180] END|---------------------------------------> + +Epoch: 181/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1431 - accuracy: 0.9556 - val_loss: 0.2670 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1354 - accuracy: 0.9580 - val_loss: 0.3152 - val_accuracy: 0.9071 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.2952 - val_accuracy: 0.9054 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.3917 - val_accuracy: 0.8958 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0896 - accuracy: 0.9756 - val_loss: 0.3502 - val_accuracy: 0.8990 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0898 - accuracy: 0.9707 - val_loss: 0.3361 - val_accuracy: 0.9071 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 181: 302.34 sec +Time taken for epoch(SUBo) 181: 244.62 sec +<---------------------------------------|Epoch [181] END|---------------------------------------> + +Epoch: 182/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2772 - val_accuracy: 0.9247 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1336 - accuracy: 0.9600 - val_loss: 0.2250 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9644 - val_loss: 0.3103 - val_accuracy: 0.9135 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9731 - val_loss: 0.3044 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0906 - accuracy: 0.9702 - val_loss: 0.3051 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0863 - accuracy: 0.9731 - val_loss: 0.3318 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 182: 304.09 sec +Time taken for epoch(SUBo) 182: 245.03 sec +<---------------------------------------|Epoch [182] END|---------------------------------------> + +Epoch: 183/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1180 - accuracy: 0.9609 - val_loss: 0.3431 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2777 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1235 - accuracy: 0.9634 - val_loss: 0.1881 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0921 - accuracy: 0.9717 - val_loss: 0.2754 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9712 - val_loss: 0.3383 - val_accuracy: 0.9103 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0866 - accuracy: 0.9751 - val_loss: 0.3123 - val_accuracy: 0.9215 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 183: 304.60 sec +Time taken for epoch(SUBo) 183: 244.97 sec +<---------------------------------------|Epoch [183] END|---------------------------------------> + +Epoch: 184/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1436 - accuracy: 0.9565 - val_loss: 0.2403 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9575 - val_loss: 0.2531 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1325 - accuracy: 0.9531 - val_loss: 0.3488 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.2155 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2753 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1108 - accuracy: 0.9644 - val_loss: 0.2761 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 184: 304.30 sec +Time taken for epoch(SUBo) 184: 244.89 sec +<---------------------------------------|Epoch [184] END|---------------------------------------> + +Epoch: 185/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1250 - accuracy: 0.9619 - val_loss: 0.2633 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.2972 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1252 - accuracy: 0.9639 - val_loss: 0.2754 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1152 - accuracy: 0.9683 - val_loss: 0.2419 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.2478 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0871 - accuracy: 0.9736 - val_loss: 0.2475 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 185: 306.52 sec +Time taken for epoch(SUBo) 185: 245.42 sec +<---------------------------------------|Epoch [185] END|---------------------------------------> + +Epoch: 186/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1323 - accuracy: 0.9585 - val_loss: 0.2456 - val_accuracy: 0.9295 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1374 - accuracy: 0.9639 - val_loss: 0.2509 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1351 - accuracy: 0.9639 - val_loss: 0.2669 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1114 - accuracy: 0.9639 - val_loss: 0.2947 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0944 - accuracy: 0.9766 - val_loss: 0.2886 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0906 - accuracy: 0.9736 - val_loss: 0.2739 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 186: 307.26 sec +Time taken for epoch(SUBo) 186: 246.41 sec +<---------------------------------------|Epoch [186] END|---------------------------------------> + +Epoch: 187/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1226 - accuracy: 0.9644 - val_loss: 0.2625 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1695 - accuracy: 0.9453 - val_loss: 1.2514 - val_accuracy: 0.7115 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1965 - accuracy: 0.9336 - val_loss: 0.5935 - val_accuracy: 0.8429 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1654 - accuracy: 0.9458 - val_loss: 0.4132 - val_accuracy: 0.9054 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9551 - val_loss: 0.4170 - val_accuracy: 0.9038 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1316 - accuracy: 0.9595 - val_loss: 0.4311 - val_accuracy: 0.9022 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 187: 297.48 sec +Time taken for epoch(SUBo) 187: 244.18 sec +<---------------------------------------|Epoch [187] END|---------------------------------------> + +Epoch: 188/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1581 - accuracy: 0.9458 - val_loss: 0.3557 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1425 - accuracy: 0.9561 - val_loss: 0.3358 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1316 - accuracy: 0.9551 - val_loss: 0.3622 - val_accuracy: 0.9231 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1155 - accuracy: 0.9634 - val_loss: 0.3811 - val_accuracy: 0.9119 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1323 - accuracy: 0.9546 - val_loss: 0.3472 - val_accuracy: 0.9167 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1224 - accuracy: 0.9644 - val_loss: 0.3330 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 188: 299.49 sec +Time taken for epoch(SUBo) 188: 244.91 sec +<---------------------------------------|Epoch [188] END|---------------------------------------> + +Epoch: 189/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1436 - accuracy: 0.9517 - val_loss: 0.2752 - val_accuracy: 0.9279 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1421 - accuracy: 0.9531 - val_loss: 0.2516 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1263 - accuracy: 0.9600 - val_loss: 0.2514 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1058 - accuracy: 0.9663 - val_loss: 0.2660 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1131 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 189: 301.75 sec +Time taken for epoch(SUBo) 189: 245.44 sec +<---------------------------------------|Epoch [189] END|---------------------------------------> + +Epoch: 190/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1480 - accuracy: 0.9570 - val_loss: 0.1996 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1882 - accuracy: 0.9380 - val_loss: 0.2167 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1597 - accuracy: 0.9512 - val_loss: 0.2156 - val_accuracy: 0.9247 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1344 - accuracy: 0.9590 - val_loss: 0.2198 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1345 - accuracy: 0.9609 - val_loss: 0.2668 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1128 - accuracy: 0.9644 - val_loss: 0.2396 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 190: 300.24 sec +Time taken for epoch(SUBo) 190: 245.01 sec +<---------------------------------------|Epoch [190] END|---------------------------------------> + +Epoch: 191/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1471 - accuracy: 0.9570 - val_loss: 0.2358 - val_accuracy: 0.9279 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1378 - accuracy: 0.9551 - val_loss: 0.2055 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.1978 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1355 - accuracy: 0.9595 - val_loss: 0.1849 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1076 - accuracy: 0.9727 - val_loss: 0.2088 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1103 - accuracy: 0.9663 - val_loss: 0.1988 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 191: 301.30 sec +Time taken for epoch(SUBo) 191: 245.49 sec +<---------------------------------------|Epoch [191] END|---------------------------------------> + +Epoch: 192/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1421 - accuracy: 0.9575 - val_loss: 0.2050 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.3539 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1398 - accuracy: 0.9551 - val_loss: 0.2728 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1200 - accuracy: 0.9653 - val_loss: 0.2649 - val_accuracy: 0.9103 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9604 - val_loss: 0.2364 - val_accuracy: 0.9247 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1229 - accuracy: 0.9580 - val_loss: 0.2355 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 192: 300.20 sec +Time taken for epoch(SUBo) 192: 245.83 sec +<---------------------------------------|Epoch [192] END|---------------------------------------> + +Epoch: 193/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1544 - accuracy: 0.9478 - val_loss: 0.2400 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1478 - accuracy: 0.9507 - val_loss: 0.2931 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.2789 - val_accuracy: 0.9327 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9585 - val_loss: 0.2220 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1067 - accuracy: 0.9712 - val_loss: 0.2248 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0902 - accuracy: 0.9751 - val_loss: 0.2198 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 193: 298.24 sec +Time taken for epoch(SUBo) 193: 245.22 sec +<---------------------------------------|Epoch [193] END|---------------------------------------> + +Epoch: 194/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.2151 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1429 - accuracy: 0.9595 - val_loss: 0.2100 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.2180 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1083 - accuracy: 0.9683 - val_loss: 0.2342 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1105 - accuracy: 0.9683 - val_loss: 0.2624 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0829 - accuracy: 0.9741 - val_loss: 0.2530 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 194: 299.52 sec +Time taken for epoch(SUBo) 194: 245.69 sec +<---------------------------------------|Epoch [194] END|---------------------------------------> + +Epoch: 195/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1158 - accuracy: 0.9673 - val_loss: 0.2753 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1058 - accuracy: 0.9648 - val_loss: 0.2734 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0861 - accuracy: 0.9756 - val_loss: 0.2831 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0798 - accuracy: 0.9775 - val_loss: 0.2666 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0687 - accuracy: 0.9824 - val_loss: 0.3035 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 195: 299.74 sec +Time taken for epoch(SUBo) 195: 244.18 sec +<---------------------------------------|Epoch [195] END|---------------------------------------> + +Epoch: 196/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1189 - accuracy: 0.9604 - val_loss: 0.2907 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1172 - accuracy: 0.9658 - val_loss: 0.2743 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1052 - accuracy: 0.9663 - val_loss: 0.2412 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1047 - accuracy: 0.9653 - val_loss: 0.4034 - val_accuracy: 0.9006 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1205 - accuracy: 0.9580 - val_loss: 0.3797 - val_accuracy: 0.9199 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1042 - accuracy: 0.9678 - val_loss: 0.3400 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 196: 300.20 sec +Time taken for epoch(SUBo) 196: 246.37 sec +<---------------------------------------|Epoch [196] END|---------------------------------------> + +Epoch: 197/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1318 - accuracy: 0.9595 - val_loss: 0.2433 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1237 - accuracy: 0.9624 - val_loss: 0.2380 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9600 - val_loss: 0.2326 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2398 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0943 - accuracy: 0.9751 - val_loss: 0.2242 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9736 - val_loss: 0.2357 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 197: 297.68 sec +Time taken for epoch(SUBo) 197: 246.24 sec +<---------------------------------------|Epoch [197] END|---------------------------------------> + +Epoch: 198/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1174 - accuracy: 0.9658 - val_loss: 0.2696 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9575 - val_loss: 0.2424 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0977 - accuracy: 0.9707 - val_loss: 0.2852 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0980 - accuracy: 0.9688 - val_loss: 0.2780 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0881 - accuracy: 0.9736 - val_loss: 0.2471 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0809 - accuracy: 0.9751 - val_loss: 0.2606 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 198: 297.77 sec +Time taken for epoch(SUBo) 198: 246.78 sec +<---------------------------------------|Epoch [198] END|---------------------------------------> + +Epoch: 199/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1334 - accuracy: 0.9609 - val_loss: 0.2220 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1240 - accuracy: 0.9604 - val_loss: 0.2392 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1112 - accuracy: 0.9658 - val_loss: 0.2233 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1175 - accuracy: 0.9673 - val_loss: 0.2212 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1032 - accuracy: 0.9678 - val_loss: 0.2742 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1011 - accuracy: 0.9663 - val_loss: 0.2787 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 199: 298.50 sec +Time taken for epoch(SUBo) 199: 246.76 sec +<---------------------------------------|Epoch [199] END|---------------------------------------> + +Epoch: 200/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1285 - accuracy: 0.9580 - val_loss: 0.3062 - val_accuracy: 0.9103 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1324 - accuracy: 0.9570 - val_loss: 0.2178 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1266 - accuracy: 0.9624 - val_loss: 0.2289 - val_accuracy: 0.9327 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1193 - accuracy: 0.9565 - val_loss: 0.2471 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1040 - accuracy: 0.9673 - val_loss: 0.2422 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0873 - accuracy: 0.9741 - val_loss: 0.2505 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 200: 298.67 sec +Time taken for epoch(SUBo) 200: 246.74 sec +<---------------------------------------|Epoch [200] END|---------------------------------------> + +Epoch: 201/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1427 - accuracy: 0.9551 - val_loss: 0.2224 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1369 - accuracy: 0.9541 - val_loss: 0.2401 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1309 - accuracy: 0.9595 - val_loss: 0.2131 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1004 - accuracy: 0.9683 - val_loss: 0.2495 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0969 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0972 - accuracy: 0.9697 - val_loss: 0.2479 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 201: 297.63 sec +Time taken for epoch(SUBo) 201: 245.98 sec +<---------------------------------------|Epoch [201] END|---------------------------------------> + +Epoch: 202/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1129 - accuracy: 0.9663 - val_loss: 0.2707 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1298 - accuracy: 0.9600 - val_loss: 0.2119 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1173 - accuracy: 0.9644 - val_loss: 0.2111 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1074 - accuracy: 0.9712 - val_loss: 0.1881 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0924 - accuracy: 0.9702 - val_loss: 0.2089 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9805 - val_loss: 0.2168 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 202: 298.33 sec +Time taken for epoch(SUBo) 202: 246.64 sec +<---------------------------------------|Epoch [202] END|---------------------------------------> + +Epoch: 203/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1369 - accuracy: 0.9561 - val_loss: 0.2180 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1303 - accuracy: 0.9541 - val_loss: 0.2391 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1245 - accuracy: 0.9634 - val_loss: 0.2390 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1135 - accuracy: 0.9648 - val_loss: 0.2664 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0981 - accuracy: 0.9727 - val_loss: 0.2374 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9722 - val_loss: 0.2165 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 203: 296.86 sec +Time taken for epoch(SUBo) 203: 245.14 sec +<---------------------------------------|Epoch [203] END|---------------------------------------> + +Epoch: 204/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.2079 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1075 - accuracy: 0.9648 - val_loss: 0.2058 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0978 - accuracy: 0.9673 - val_loss: 0.2125 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1015 - accuracy: 0.9722 - val_loss: 0.2370 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0780 - accuracy: 0.9775 - val_loss: 0.2245 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0684 - accuracy: 0.9814 - val_loss: 0.2192 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 204: 298.03 sec +Time taken for epoch(SUBo) 204: 246.30 sec +<---------------------------------------|Epoch [204] END|---------------------------------------> + +Epoch: 205/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1153 - accuracy: 0.9614 - val_loss: 0.2277 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1168 - accuracy: 0.9629 - val_loss: 0.2214 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1209 - accuracy: 0.9629 - val_loss: 0.1874 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1025 - accuracy: 0.9692 - val_loss: 0.2265 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0891 - accuracy: 0.9766 - val_loss: 0.1875 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0753 - accuracy: 0.9805 - val_loss: 0.2138 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 205: 297.87 sec +Time taken for epoch(SUBo) 205: 245.90 sec +<---------------------------------------|Epoch [205] END|---------------------------------------> + +Epoch: 206/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1070 - accuracy: 0.9697 - val_loss: 0.2057 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1039 - accuracy: 0.9673 - val_loss: 0.2215 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0855 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.3037 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9766 - val_loss: 0.2560 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0760 - accuracy: 0.9766 - val_loss: 0.2418 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 206: 297.94 sec +Time taken for epoch(SUBo) 206: 246.21 sec +<---------------------------------------|Epoch [206] END|---------------------------------------> + +Epoch: 207/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1259 - accuracy: 0.9658 - val_loss: 0.2366 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1204 - accuracy: 0.9644 - val_loss: 0.2283 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1144 - accuracy: 0.9624 - val_loss: 0.1889 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0992 - accuracy: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0875 - accuracy: 0.9775 - val_loss: 0.2601 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0808 - accuracy: 0.9800 - val_loss: 0.2478 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 207: 298.66 sec +Time taken for epoch(SUBo) 207: 246.51 sec +<---------------------------------------|Epoch [207] END|---------------------------------------> + +Epoch: 208/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1141 - accuracy: 0.9648 - val_loss: 0.2134 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1996 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0942 - accuracy: 0.9697 - val_loss: 0.1941 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0885 - accuracy: 0.9741 - val_loss: 0.2165 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0837 - accuracy: 0.9741 - val_loss: 0.2150 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0726 - accuracy: 0.9829 - val_loss: 0.2024 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 208: 298.91 sec +Time taken for epoch(SUBo) 208: 246.69 sec +<---------------------------------------|Epoch [208] END|---------------------------------------> + +Epoch: 209/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1141 - accuracy: 0.9639 - val_loss: 0.2234 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9629 - val_loss: 0.2288 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1251 - accuracy: 0.9624 - val_loss: 0.2119 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.2092 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9678 - val_loss: 0.2370 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0761 - accuracy: 0.9771 - val_loss: 0.2383 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 209: 296.92 sec +Time taken for epoch(SUBo) 209: 245.42 sec +<---------------------------------------|Epoch [209] END|---------------------------------------> + +Epoch: 210/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1219 - accuracy: 0.9619 - val_loss: 0.2331 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.2102 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1038 - accuracy: 0.9658 - val_loss: 0.1857 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9727 - val_loss: 0.2113 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1070 - accuracy: 0.9668 - val_loss: 0.2461 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0851 - accuracy: 0.9766 - val_loss: 0.2336 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 210: 295.74 sec +Time taken for epoch(SUBo) 210: 245.46 sec +<---------------------------------------|Epoch [210] END|---------------------------------------> + +Epoch: 211/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1203 - accuracy: 0.9658 - val_loss: 0.1951 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9673 - val_loss: 0.1898 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0830 - accuracy: 0.9751 - val_loss: 0.1828 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0600 - accuracy: 0.9829 - val_loss: 0.2026 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0587 - accuracy: 0.9854 - val_loss: 0.1957 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 211: 295.87 sec +Time taken for epoch(SUBo) 211: 245.59 sec +<---------------------------------------|Epoch [211] END|---------------------------------------> + +Epoch: 212/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9746 - val_loss: 0.1699 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1037 - accuracy: 0.9673 - val_loss: 0.2054 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0907 - accuracy: 0.9731 - val_loss: 0.2072 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0802 - accuracy: 0.9771 - val_loss: 0.1906 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0749 - accuracy: 0.9814 - val_loss: 0.1856 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0661 - accuracy: 0.9824 - val_loss: 0.1860 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 212: 295.86 sec +Time taken for epoch(SUBo) 212: 245.69 sec +<---------------------------------------|Epoch [212] END|---------------------------------------> + +Epoch: 213/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1047 - accuracy: 0.9688 - val_loss: 0.1803 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0977 - accuracy: 0.9746 - val_loss: 0.1586 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9722 - val_loss: 0.1882 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1002 - accuracy: 0.9756 - val_loss: 0.2034 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0865 - accuracy: 0.9766 - val_loss: 0.2175 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0730 - accuracy: 0.9790 - val_loss: 0.2228 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 213: 295.87 sec +Time taken for epoch(SUBo) 213: 245.31 sec +<---------------------------------------|Epoch [213] END|---------------------------------------> + +Epoch: 214/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1225 - accuracy: 0.9619 - val_loss: 0.1941 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1188 - accuracy: 0.9658 - val_loss: 0.1750 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1020 - accuracy: 0.9644 - val_loss: 0.2022 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0990 - accuracy: 0.9668 - val_loss: 0.1984 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0992 - accuracy: 0.9722 - val_loss: 0.2096 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0697 - accuracy: 0.9814 - val_loss: 0.2177 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 214: 295.73 sec +Time taken for epoch(SUBo) 214: 245.40 sec +<---------------------------------------|Epoch [214] END|---------------------------------------> + +Epoch: 215/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2052 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1099 - accuracy: 0.9688 - val_loss: 0.2122 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0904 - accuracy: 0.9712 - val_loss: 0.2057 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0789 - accuracy: 0.9756 - val_loss: 0.2348 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0699 - accuracy: 0.9834 - val_loss: 0.2055 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.2412 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 215: 296.11 sec +Time taken for epoch(SUBo) 215: 245.32 sec +<---------------------------------------|Epoch [215] END|---------------------------------------> + +Epoch: 216/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1354 - accuracy: 0.9619 - val_loss: 1.9127 - val_accuracy: 0.6250 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.6524 - accuracy: 0.6860 - val_loss: 0.5187 - val_accuracy: 0.8253 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.4618 - accuracy: 0.8057 - val_loss: 0.4150 - val_accuracy: 0.9103 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3662 - accuracy: 0.8638 - val_loss: 0.2908 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3131 - accuracy: 0.8921 - val_loss: 0.3339 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2602 - accuracy: 0.9121 - val_loss: 0.3118 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 216: 294.77 sec +Time taken for epoch(SUBo) 216: 244.51 sec +<---------------------------------------|Epoch [216] END|---------------------------------------> + +Epoch: 217/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.3051 - accuracy: 0.8945 - val_loss: 0.2281 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2581 - accuracy: 0.9053 - val_loss: 0.2585 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2153 - accuracy: 0.9385 - val_loss: 0.1958 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1935 - accuracy: 0.9463 - val_loss: 0.1896 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1759 - accuracy: 0.9492 - val_loss: 0.2038 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.2104 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 217: 295.94 sec +Time taken for epoch(SUBo) 217: 245.61 sec +<---------------------------------------|Epoch [217] END|---------------------------------------> + +Epoch: 218/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2018 - accuracy: 0.9331 - val_loss: 0.2546 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1969 - accuracy: 0.9355 - val_loss: 0.2012 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1747 - accuracy: 0.9453 - val_loss: 0.1932 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1722 - accuracy: 0.9507 - val_loss: 0.2019 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1437 - accuracy: 0.9536 - val_loss: 0.2124 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1302 - accuracy: 0.9609 - val_loss: 0.2347 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 218: 295.43 sec +Time taken for epoch(SUBo) 218: 245.14 sec +<---------------------------------------|Epoch [218] END|---------------------------------------> + +Epoch: 219/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1756 - accuracy: 0.9478 - val_loss: 0.1971 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1803 - accuracy: 0.9414 - val_loss: 0.1779 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1618 - accuracy: 0.9424 - val_loss: 0.2014 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1546 - accuracy: 0.9600 - val_loss: 0.2209 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1289 - accuracy: 0.9639 - val_loss: 0.2224 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 219: 293.27 sec +Time taken for epoch(SUBo) 219: 243.09 sec +<---------------------------------------|Epoch [219] END|---------------------------------------> + +Epoch: 220/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1384 - accuracy: 0.9580 - val_loss: 0.1899 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1636 - accuracy: 0.9497 - val_loss: 0.1965 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9561 - val_loss: 0.1807 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1192 - accuracy: 0.9629 - val_loss: 0.2034 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1268 - accuracy: 0.9585 - val_loss: 0.1812 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.1890 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 220: 289.19 sec +Time taken for epoch(SUBo) 220: 239.59 sec +<---------------------------------------|Epoch [220] END|---------------------------------------> + +Epoch: 221/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1293 - accuracy: 0.9604 - val_loss: 0.2001 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1258 - accuracy: 0.9629 - val_loss: 0.2138 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9629 - val_loss: 0.2242 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1100 - accuracy: 0.9712 - val_loss: 0.2425 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1082 - accuracy: 0.9712 - val_loss: 0.2177 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0903 - accuracy: 0.9751 - val_loss: 0.2145 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 221: 303.15 sec +Time taken for epoch(SUBo) 221: 246.93 sec +<---------------------------------------|Epoch [221] END|---------------------------------------> + +Epoch: 222/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2076 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1585 - accuracy: 0.9556 - val_loss: 0.2135 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1446 - accuracy: 0.9575 - val_loss: 0.2137 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1215 - accuracy: 0.9663 - val_loss: 0.2196 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1310 - accuracy: 0.9609 - val_loss: 0.2567 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1038 - accuracy: 0.9727 - val_loss: 0.2416 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 222: 299.03 sec +Time taken for epoch(SUBo) 222: 248.17 sec +<---------------------------------------|Epoch [222] END|---------------------------------------> + +Epoch: 223/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 165ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2650 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 42s 165ms/step - loss: 0.1193 - accuracy: 0.9570 - val_loss: 0.1668 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.1817 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.2031 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 43s 166ms/step - loss: 0.0876 - accuracy: 0.9751 - val_loss: 0.1877 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.0826 - accuracy: 0.9766 - val_loss: 0.1862 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 223: 312.69 sec +Time taken for epoch(SUBo) 223: 256.34 sec +<---------------------------------------|Epoch [223] END|---------------------------------------> + +Epoch: 224/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1238 - accuracy: 0.9668 - val_loss: 0.1797 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1198 - accuracy: 0.9624 - val_loss: 0.1924 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1028 - accuracy: 0.9712 - val_loss: 0.2374 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1065 - accuracy: 0.9722 - val_loss: 0.2279 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0899 - accuracy: 0.9771 - val_loss: 0.1902 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9795 - val_loss: 0.1907 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 224: 301.18 sec +Time taken for epoch(SUBo) 224: 248.32 sec +<---------------------------------------|Epoch [224] END|---------------------------------------> + +Epoch: 225/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1296 - accuracy: 0.9609 - val_loss: 0.1972 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.1699 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1233 - accuracy: 0.9624 - val_loss: 0.2114 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1953 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0820 - accuracy: 0.9780 - val_loss: 0.2077 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0815 - accuracy: 0.9814 - val_loss: 0.2196 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 225: 302.66 sec +Time taken for epoch(SUBo) 225: 248.69 sec +<---------------------------------------|Epoch [225] END|---------------------------------------> + +Epoch: 226/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1353 - accuracy: 0.9604 - val_loss: 0.2359 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.2577 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2211 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1084 - accuracy: 0.9707 - val_loss: 0.1719 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1007 - accuracy: 0.9712 - val_loss: 0.1720 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0895 - accuracy: 0.9751 - val_loss: 0.1756 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 226: 313.16 sec +Time taken for epoch(SUBo) 226: 251.41 sec +<---------------------------------------|Epoch [226] END|---------------------------------------> + +Epoch: 227/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1123 - accuracy: 0.9639 - val_loss: 0.1721 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1115 - accuracy: 0.9653 - val_loss: 0.2263 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1077 - accuracy: 0.9639 - val_loss: 0.1975 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0943 - accuracy: 0.9717 - val_loss: 0.2010 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0949 - accuracy: 0.9736 - val_loss: 0.1780 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0812 - accuracy: 0.9771 - val_loss: 0.1900 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 227: 306.72 sec +Time taken for epoch(SUBo) 227: 248.78 sec +<---------------------------------------|Epoch [227] END|---------------------------------------> + +Epoch: 228/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1398 - accuracy: 0.9546 - val_loss: 0.1847 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1483 - accuracy: 0.9551 - val_loss: 0.1827 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1162 - accuracy: 0.9678 - val_loss: 0.2110 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1037 - accuracy: 0.9639 - val_loss: 0.1890 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0836 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.0876 - accuracy: 0.9746 - val_loss: 0.1758 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 228: 307.11 sec +Time taken for epoch(SUBo) 228: 249.60 sec +<---------------------------------------|Epoch [228] END|---------------------------------------> + +Epoch: 229/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 162ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.1633 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1031 - accuracy: 0.9702 - val_loss: 0.1893 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1045 - accuracy: 0.9717 - val_loss: 0.1849 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0950 - accuracy: 0.9780 - val_loss: 0.1626 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0764 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0747 - accuracy: 0.9795 - val_loss: 0.1604 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 229: 302.46 sec +Time taken for epoch(SUBo) 229: 249.29 sec +<---------------------------------------|Epoch [229] END|---------------------------------------> + +Epoch: 230/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1203 - accuracy: 0.9648 - val_loss: 0.1849 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1080 - accuracy: 0.9639 - val_loss: 0.1861 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0951 - accuracy: 0.9697 - val_loss: 0.2135 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1004 - accuracy: 0.9712 - val_loss: 0.2054 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0709 - accuracy: 0.9771 - val_loss: 0.2113 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0771 - accuracy: 0.9766 - val_loss: 0.2083 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 230: 299.92 sec +Time taken for epoch(SUBo) 230: 248.71 sec +<---------------------------------------|Epoch [230] END|---------------------------------------> + +Epoch: 231/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 167ms/step - loss: 0.0909 - accuracy: 0.9707 - val_loss: 0.1829 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0896 - accuracy: 0.9746 - val_loss: 0.1859 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0809 - accuracy: 0.9766 - val_loss: 0.1953 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0791 - accuracy: 0.9780 - val_loss: 0.1783 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0703 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0497 - accuracy: 0.9863 - val_loss: 0.1703 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 231: 304.17 sec +Time taken for epoch(SUBo) 231: 253.03 sec +<---------------------------------------|Epoch [231] END|---------------------------------------> + +Epoch: 232/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 165ms/step - loss: 0.1107 - accuracy: 0.9683 - val_loss: 0.1718 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.1952 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1595 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0998 - accuracy: 0.9722 - val_loss: 0.1685 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.0773 - accuracy: 0.9795 - val_loss: 0.2042 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0897 - accuracy: 0.9780 - val_loss: 0.1887 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 232: 312.43 sec +Time taken for epoch(SUBo) 232: 254.46 sec +<---------------------------------------|Epoch [232] END|---------------------------------------> + +Epoch: 233/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 48s 167ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.1891 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.1052 - accuracy: 0.9688 - val_loss: 0.1659 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.1140 - accuracy: 0.9688 - val_loss: 0.1445 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0954 - accuracy: 0.9717 - val_loss: 0.1710 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.0933 - accuracy: 0.9761 - val_loss: 0.1612 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0744 - accuracy: 0.9814 - val_loss: 0.1741 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 233: 313.69 sec +Time taken for epoch(SUBo) 233: 256.06 sec +<---------------------------------------|Epoch [233] END|---------------------------------------> + +Epoch: 234/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 167ms/step - loss: 0.1023 - accuracy: 0.9683 - val_loss: 0.1438 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 42s 162ms/step - loss: 0.0962 - accuracy: 0.9707 - val_loss: 0.2408 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 42s 162ms/step - loss: 0.0875 - accuracy: 0.9736 - val_loss: 0.1795 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.0846 - accuracy: 0.9722 - val_loss: 0.1669 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0591 - accuracy: 0.9844 - val_loss: 0.1704 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0565 - accuracy: 0.9873 - val_loss: 0.1818 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 234: 311.13 sec +Time taken for epoch(SUBo) 234: 255.01 sec +<---------------------------------------|Epoch [234] END|---------------------------------------> + +Epoch: 235/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1210 - accuracy: 0.9629 - val_loss: 0.1778 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1125 - accuracy: 0.9663 - val_loss: 0.1453 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.1608 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0843 - accuracy: 0.9775 - val_loss: 0.1615 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.1832 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0672 - accuracy: 0.9800 - val_loss: 0.1808 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 235: 300.21 sec +Time taken for epoch(SUBo) 235: 251.11 sec +<---------------------------------------|Epoch [235] END|---------------------------------------> + +Epoch: 236/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1133 - accuracy: 0.9639 - val_loss: 0.1626 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0993 - accuracy: 0.9648 - val_loss: 0.1585 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0924 - accuracy: 0.9717 - val_loss: 0.1581 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1336 - val_accuracy: 0.9583 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0724 - accuracy: 0.9790 - val_loss: 0.1694 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0585 - accuracy: 0.9839 - val_loss: 0.1735 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 236: 302.45 sec +Time taken for epoch(SUBo) 236: 251.67 sec +<---------------------------------------|Epoch [236] END|---------------------------------------> + +Epoch: 237/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1015 - accuracy: 0.9663 - val_loss: 0.1594 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0919 - accuracy: 0.9736 - val_loss: 0.1593 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0886 - accuracy: 0.9746 - val_loss: 0.1714 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0809 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0690 - accuracy: 0.9829 - val_loss: 0.2800 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0600 - accuracy: 0.9873 - val_loss: 0.2560 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 237: 301.88 sec +Time taken for epoch(SUBo) 237: 251.53 sec +<---------------------------------------|Epoch [237] END|---------------------------------------> + +Epoch: 238/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 166ms/step - loss: 0.1051 - accuracy: 0.9663 - val_loss: 0.2133 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0934 - accuracy: 0.9717 - val_loss: 0.2560 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0785 - accuracy: 0.9790 - val_loss: 0.2045 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0702 - accuracy: 0.9790 - val_loss: 0.2433 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0706 - accuracy: 0.9800 - val_loss: 0.1769 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0689 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 238: 307.10 sec +Time taken for epoch(SUBo) 238: 252.62 sec +<---------------------------------------|Epoch [238] END|---------------------------------------> + +Epoch: 239/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1147 - accuracy: 0.9673 - val_loss: 0.1823 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0958 - accuracy: 0.9751 - val_loss: 0.2081 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0865 - accuracy: 0.9775 - val_loss: 0.2058 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0716 - accuracy: 0.9795 - val_loss: 0.2068 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2146 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.0562 - accuracy: 0.9834 - val_loss: 0.2186 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 239: 303.13 sec +Time taken for epoch(SUBo) 239: 249.48 sec +<---------------------------------------|Epoch [239] END|---------------------------------------> + +Epoch: 240/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1219 - accuracy: 0.9595 - val_loss: 0.1957 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1010 - accuracy: 0.9717 - val_loss: 0.2189 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0829 - accuracy: 0.9756 - val_loss: 0.2015 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0715 - accuracy: 0.9780 - val_loss: 0.2191 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0614 - accuracy: 0.9839 - val_loss: 0.2335 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0522 - accuracy: 0.9858 - val_loss: 0.2491 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 240: 296.96 sec +Time taken for epoch(SUBo) 240: 247.49 sec +<---------------------------------------|Epoch [240] END|---------------------------------------> + +Epoch: 241/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0874 - accuracy: 0.9731 - val_loss: 0.2011 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0942 - accuracy: 0.9731 - val_loss: 0.1900 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0867 - accuracy: 0.9731 - val_loss: 0.2119 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0814 - accuracy: 0.9727 - val_loss: 0.2344 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0619 - accuracy: 0.9834 - val_loss: 0.2379 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0528 - accuracy: 0.9868 - val_loss: 0.2390 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 241: 301.50 sec +Time taken for epoch(SUBo) 241: 244.85 sec +<---------------------------------------|Epoch [241] END|---------------------------------------> + +Epoch: 242/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 157ms/step - loss: 0.1068 - accuracy: 0.9692 - val_loss: 0.2088 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.0962 - accuracy: 0.9692 - val_loss: 0.2827 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.2028 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0831 - accuracy: 0.9761 - val_loss: 0.2217 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0908 - accuracy: 0.9775 - val_loss: 0.2048 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0678 - accuracy: 0.9814 - val_loss: 0.1931 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 242: 289.32 sec +Time taken for epoch(SUBo) 242: 241.25 sec +<---------------------------------------|Epoch [242] END|---------------------------------------> + +Epoch: 243/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 158ms/step - loss: 0.1125 - accuracy: 0.9692 - val_loss: 0.1588 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0962 - accuracy: 0.9668 - val_loss: 0.1660 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0947 - accuracy: 0.9717 - val_loss: 0.2053 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0780 - accuracy: 0.9756 - val_loss: 0.1659 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0762 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0544 - accuracy: 0.9844 - val_loss: 0.1827 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 243: 289.77 sec +Time taken for epoch(SUBo) 243: 242.82 sec +<---------------------------------------|Epoch [243] END|---------------------------------------> + +Epoch: 244/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1976 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0864 - accuracy: 0.9775 - val_loss: 0.2101 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1914 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2286 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0735 - accuracy: 0.9819 - val_loss: 0.2039 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0471 - accuracy: 0.9897 - val_loss: 0.2055 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 244: 292.32 sec +Time taken for epoch(SUBo) 244: 245.77 sec +<---------------------------------------|Epoch [244] END|---------------------------------------> + +Epoch: 245/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1215 - accuracy: 0.9648 - val_loss: 0.1895 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1283 - accuracy: 0.9629 - val_loss: 0.1734 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0933 - accuracy: 0.9731 - val_loss: 0.1550 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1631 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0857 - accuracy: 0.9731 - val_loss: 0.1576 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0706 - accuracy: 0.9824 - val_loss: 0.1603 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 245: 293.35 sec +Time taken for epoch(SUBo) 245: 246.05 sec +<---------------------------------------|Epoch [245] END|---------------------------------------> + +Epoch: 246/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0914 - accuracy: 0.9771 - val_loss: 0.1657 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9697 - val_loss: 0.1844 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9756 - val_loss: 0.1675 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0649 - accuracy: 0.9800 - val_loss: 0.1947 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0551 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0458 - accuracy: 0.9897 - val_loss: 0.1977 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 246: 292.03 sec +Time taken for epoch(SUBo) 246: 245.80 sec +<---------------------------------------|Epoch [246] END|---------------------------------------> + +Epoch: 247/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.0973 - accuracy: 0.9727 - val_loss: 0.1630 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0989 - accuracy: 0.9702 - val_loss: 0.1590 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0671 - accuracy: 0.9800 - val_loss: 0.1650 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0731 - accuracy: 0.9805 - val_loss: 0.1396 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9854 - val_loss: 0.1649 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0560 - accuracy: 0.9883 - val_loss: 0.1677 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 247: 294.16 sec +Time taken for epoch(SUBo) 247: 247.01 sec +<---------------------------------------|Epoch [247] END|---------------------------------------> + +Epoch: 248/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0900 - accuracy: 0.9756 - val_loss: 0.1515 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9761 - val_loss: 0.1617 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0630 - accuracy: 0.9858 - val_loss: 0.1660 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0626 - accuracy: 0.9834 - val_loss: 0.1958 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0587 - accuracy: 0.9849 - val_loss: 0.1824 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 248: 293.13 sec +Time taken for epoch(SUBo) 248: 246.15 sec +<---------------------------------------|Epoch [248] END|---------------------------------------> + +Epoch: 249/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.1567 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1036 - accuracy: 0.9717 - val_loss: 0.1435 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0755 - accuracy: 0.9780 - val_loss: 0.1969 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0742 - accuracy: 0.9775 - val_loss: 0.1623 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0557 - accuracy: 0.9844 - val_loss: 0.1914 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 249: 293.33 sec +Time taken for epoch(SUBo) 249: 245.80 sec +<---------------------------------------|Epoch [249] END|---------------------------------------> + +Epoch: 250/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1097 - accuracy: 0.9658 - val_loss: 0.1761 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0999 - accuracy: 0.9697 - val_loss: 0.1736 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0943 - accuracy: 0.9673 - val_loss: 0.1766 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.1743 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0821 - accuracy: 0.9727 - val_loss: 0.1941 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0683 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 250: 292.13 sec +Time taken for epoch(SUBo) 250: 244.59 sec +<---------------------------------------|Epoch [250] END|---------------------------------------> + +Epoch: 251/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9707 - val_loss: 0.1764 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0835 - accuracy: 0.9736 - val_loss: 0.1675 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9785 - val_loss: 0.1513 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0704 - accuracy: 0.9800 - val_loss: 0.1564 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0581 - accuracy: 0.9839 - val_loss: 0.1602 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0579 - accuracy: 0.9849 - val_loss: 0.1547 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 251: 292.96 sec +Time taken for epoch(SUBo) 251: 246.01 sec +<---------------------------------------|Epoch [251] END|---------------------------------------> + +Epoch: 252/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0999 - accuracy: 0.9707 - val_loss: 0.1387 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0826 - accuracy: 0.9756 - val_loss: 0.1897 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0722 - accuracy: 0.9775 - val_loss: 0.1514 - val_accuracy: 0.9615 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.1432 - val_accuracy: 0.9599 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0720 - accuracy: 0.9814 - val_loss: 0.1414 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0650 - accuracy: 0.9795 - val_loss: 0.1418 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.146798238158226 to 0.14178654551506042. Saving model. +Time taken for epoch(FULL) 252: 295.30 sec +Time taken for epoch(SUBo) 252: 246.41 sec +<---------------------------------------|Epoch [252] END|---------------------------------------> + +Epoch: 253/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1538 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0866 - accuracy: 0.9761 - val_loss: 0.1447 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0777 - accuracy: 0.9800 - val_loss: 0.1519 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0644 - accuracy: 0.9829 - val_loss: 0.1863 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0585 - accuracy: 0.9868 - val_loss: 0.1939 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0511 - accuracy: 0.9878 - val_loss: 0.1766 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 253: 293.56 sec +Time taken for epoch(SUBo) 253: 246.59 sec +<---------------------------------------|Epoch [253] END|---------------------------------------> + +Epoch: 254/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1089 - accuracy: 0.9673 - val_loss: 0.1512 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0968 - accuracy: 0.9653 - val_loss: 0.1482 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0950 - accuracy: 0.9658 - val_loss: 0.1955 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0852 - accuracy: 0.9756 - val_loss: 0.1505 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0796 - accuracy: 0.9795 - val_loss: 0.1484 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0683 - accuracy: 0.9810 - val_loss: 0.1534 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 254: 293.79 sec +Time taken for epoch(SUBo) 254: 246.40 sec +<---------------------------------------|Epoch [254] END|---------------------------------------> + +Epoch: 255/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0860 - accuracy: 0.9746 - val_loss: 0.1747 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9727 - val_loss: 0.1806 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0816 - accuracy: 0.9756 - val_loss: 0.1677 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9834 - val_loss: 0.1808 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0564 - accuracy: 0.9844 - val_loss: 0.2127 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0513 - accuracy: 0.9883 - val_loss: 0.1953 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 255: 293.85 sec +Time taken for epoch(SUBo) 255: 246.35 sec +<---------------------------------------|Epoch [255] END|---------------------------------------> +Training done. + diff --git a/backup/V4/TRAIN_LOG_ANSI.ans b/backup/V4/TRAIN_LOG_ANSI.ans index 6052d84..0e1ce86 100644 --- a/backup/V4/TRAIN_LOG_ANSI.ans +++ b/backup/V4/TRAIN_LOG_ANSI.ans @@ -1,6693 +1,6693 @@ -Training the model... - -Epoch: 1/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 55s 166ms/step - loss: 20.4375 - accuracy: 0.6216 - val_loss: 15.7907 - val_accuracy: 0.8157 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 10.5470 - accuracy: 0.7383 - val_loss: 6.2110 - val_accuracy: 0.8205 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 4.1127 - accuracy: 0.7842 - val_loss: 2.5556 - val_accuracy: 0.8702 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 1.8795 - accuracy: 0.8096 - val_loss: 1.2612 - val_accuracy: 0.8718 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 1.0468 - accuracy: 0.8398 - val_loss: 0.8444 - val_accuracy: 0.8686 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.7275 - accuracy: 0.8555 - val_loss: 0.6163 - val_accuracy: 0.8766 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.5332 - accuracy: 0.8926 - val_loss: 0.5743 - val_accuracy: 0.8878 -Epoch 8/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.4697 - accuracy: 0.8979 - val_loss: 0.5413 - val_accuracy: 0.8702 -Subset training done. -Improved model accuracy from 0 to 0.870192289352417. Saving model. -Improved model loss from inf to 0.5412302017211914. Saving model. -Time taken for epoch(FULL) 1: 386.46 sec -Time taken for epoch(SUBo) 1: 333.70 sec -<---------------------------------------|Epoch [1] END|---------------------------------------> - -Epoch: 2/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.5620 - accuracy: 0.8359 - val_loss: 0.5067 - val_accuracy: 0.8446 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.5338 - accuracy: 0.8403 - val_loss: 0.6622 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.5047 - accuracy: 0.8418 - val_loss: 0.3689 - val_accuracy: 0.8926 -Epoch 4/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.4192 - accuracy: 0.8638 - val_loss: 0.4566 - val_accuracy: 0.8686 -Epoch 5/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.4020 - accuracy: 0.8677 - val_loss: 0.3214 - val_accuracy: 0.8670 -Epoch 6/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3681 - accuracy: 0.8813 - val_loss: 0.3148 - val_accuracy: 0.9199 -Epoch 7/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3198 - accuracy: 0.8931 - val_loss: 0.2567 - val_accuracy: 0.9279 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2715 - accuracy: 0.9180 - val_loss: 0.2393 - val_accuracy: 0.9311 -Subset training done. -Improved model accuracy from 0.870192289352417 to 0.9310897588729858. Saving model. -Improved model loss from 0.5412302017211914 to 0.23925769329071045. Saving model. -Time taken for epoch(FULL) 2: 381.03 sec -Time taken for epoch(SUBo) 2: 325.77 sec -<---------------------------------------|Epoch [2] END|---------------------------------------> - -Epoch: 3/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 161ms/step - loss: 0.3640 - accuracy: 0.8696 - val_loss: 0.3126 - val_accuracy: 0.9247 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3588 - accuracy: 0.8735 - val_loss: 0.3768 - val_accuracy: 0.9295 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3830 - accuracy: 0.8730 - val_loss: 0.4670 - val_accuracy: 0.9391 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3658 - accuracy: 0.8887 - val_loss: 0.2308 - val_accuracy: 0.9359 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3717 - accuracy: 0.8779 - val_loss: 0.2747 - val_accuracy: 0.9199 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3136 - accuracy: 0.9028 - val_loss: 0.3153 - val_accuracy: 0.9022 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2696 - accuracy: 0.9136 - val_loss: 0.2452 - val_accuracy: 0.9247 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2407 - accuracy: 0.9243 - val_loss: 0.2541 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9310897588729858. Not saving model. -Model loss did not improve from 0.23925769329071045. Not saving model. -Time taken for epoch(FULL) 3: 376.52 sec -Time taken for epoch(SUBo) 3: 324.58 sec -<---------------------------------------|Epoch [3] END|---------------------------------------> - -Epoch: 4/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.3534 - accuracy: 0.8784 - val_loss: 0.2325 - val_accuracy: 0.9215 -Epoch 2/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.3861 - accuracy: 0.8584 - val_loss: 0.4468 - val_accuracy: 0.9103 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3696 - accuracy: 0.8765 - val_loss: 0.4794 - val_accuracy: 0.9038 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3680 - accuracy: 0.8828 - val_loss: 0.2781 - val_accuracy: 0.9231 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2897 - accuracy: 0.9165 - val_loss: 0.2823 - val_accuracy: 0.9327 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2801 - accuracy: 0.9165 - val_loss: 0.2447 - val_accuracy: 0.9071 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2460 - accuracy: 0.9326 - val_loss: 0.2840 - val_accuracy: 0.9359 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1982 - accuracy: 0.9414 - val_loss: 0.2283 - val_accuracy: 0.9343 -Subset training done. -Improved model accuracy from 0.9310897588729858 to 0.9342948794364929. Saving model. -Improved model loss from 0.23925769329071045 to 0.22827185690402985. Saving model. -Time taken for epoch(FULL) 4: 379.86 sec -Time taken for epoch(SUBo) 4: 325.33 sec -<---------------------------------------|Epoch [4] END|---------------------------------------> - -Epoch: 5/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.3226 - accuracy: 0.8950 - val_loss: 0.3022 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3538 - accuracy: 0.8862 - val_loss: 0.3310 - val_accuracy: 0.9279 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3201 - accuracy: 0.8892 - val_loss: 0.2884 - val_accuracy: 0.9071 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3229 - accuracy: 0.9009 - val_loss: 0.5201 - val_accuracy: 0.7340 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3079 - accuracy: 0.8926 - val_loss: 0.2863 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2670 - accuracy: 0.9141 - val_loss: 0.2587 - val_accuracy: 0.9151 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2639 - accuracy: 0.9209 - val_loss: 0.2800 - val_accuracy: 0.9054 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1925 - accuracy: 0.9541 - val_loss: 0.2547 - val_accuracy: 0.9087 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 5: 375.38 sec -Time taken for epoch(SUBo) 5: 323.81 sec -<---------------------------------------|Epoch [5] END|---------------------------------------> - -Epoch: 6/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2908 - accuracy: 0.8994 - val_loss: 0.3886 - val_accuracy: 0.9151 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2973 - accuracy: 0.8984 - val_loss: 0.4025 - val_accuracy: 0.7917 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3093 - accuracy: 0.8945 - val_loss: 0.9113 - val_accuracy: 0.6907 -Epoch 4/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2903 - accuracy: 0.9048 - val_loss: 0.3253 - val_accuracy: 0.8766 -Epoch 5/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2965 - accuracy: 0.9131 - val_loss: 0.3971 - val_accuracy: 0.8798 -Epoch 6/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2341 - accuracy: 0.9238 - val_loss: 0.3240 - val_accuracy: 0.9071 -Epoch 7/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.2202 - accuracy: 0.9316 - val_loss: 0.3072 - val_accuracy: 0.9151 -Epoch 8/8 -256/256 [==============================] - 40s 157ms/step - loss: 0.1613 - accuracy: 0.9614 - val_loss: 0.3554 - val_accuracy: 0.9167 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 6: 377.12 sec -Time taken for epoch(SUBo) 6: 325.55 sec -<---------------------------------------|Epoch [6] END|---------------------------------------> - -Epoch: 7/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 161ms/step - loss: 0.2840 - accuracy: 0.9102 - val_loss: 0.3625 - val_accuracy: 0.8878 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3095 - accuracy: 0.9033 - val_loss: 0.3963 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3532 - accuracy: 0.8887 - val_loss: 0.2555 - val_accuracy: 0.9263 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3018 - accuracy: 0.8979 - val_loss: 0.2644 - val_accuracy: 0.9375 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3154 - accuracy: 0.9048 - val_loss: 0.4598 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2509 - accuracy: 0.9312 - val_loss: 0.2478 - val_accuracy: 0.9295 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1902 - accuracy: 0.9478 - val_loss: 0.2697 - val_accuracy: 0.9311 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1628 - accuracy: 0.9531 - val_loss: 0.2426 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9342948794364929. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 7: 376.09 sec -Time taken for epoch(SUBo) 7: 324.32 sec -<---------------------------------------|Epoch [7] END|---------------------------------------> - -Epoch: 8/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2691 - accuracy: 0.9106 - val_loss: 0.4805 - val_accuracy: 0.9071 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3014 - accuracy: 0.8994 - val_loss: 0.2715 - val_accuracy: 0.8926 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3387 - accuracy: 0.8818 - val_loss: 0.3345 - val_accuracy: 0.8542 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3069 - accuracy: 0.9072 - val_loss: 0.3671 - val_accuracy: 0.9359 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2874 - accuracy: 0.9058 - val_loss: 0.2579 - val_accuracy: 0.9343 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2255 - accuracy: 0.9399 - val_loss: 0.3501 - val_accuracy: 0.9375 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1835 - accuracy: 0.9492 - val_loss: 0.2757 - val_accuracy: 0.9407 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1739 - accuracy: 0.9492 - val_loss: 0.2712 - val_accuracy: 0.9391 -Subset training done. -Improved model accuracy from 0.9342948794364929 to 0.9391025900840759. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 8: 377.68 sec -Time taken for epoch(SUBo) 8: 324.30 sec -<---------------------------------------|Epoch [8] END|---------------------------------------> - -Epoch: 9/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2921 - accuracy: 0.9077 - val_loss: 0.3537 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2866 - accuracy: 0.9082 - val_loss: 0.3213 - val_accuracy: 0.9359 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2978 - accuracy: 0.8999 - val_loss: 0.3623 - val_accuracy: 0.9199 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2635 - accuracy: 0.9209 - val_loss: 0.4593 - val_accuracy: 0.8942 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2444 - accuracy: 0.9287 - val_loss: 0.3207 - val_accuracy: 0.9215 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2366 - accuracy: 0.9277 - val_loss: 0.3259 - val_accuracy: 0.9167 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1802 - accuracy: 0.9478 - val_loss: 0.3234 - val_accuracy: 0.9231 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9648 - val_loss: 0.2856 - val_accuracy: 0.9247 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 9: 373.86 sec -Time taken for epoch(SUBo) 9: 323.15 sec -<---------------------------------------|Epoch [9] END|---------------------------------------> - -Epoch: 10/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2630 - accuracy: 0.9165 - val_loss: 0.2739 - val_accuracy: 0.9311 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3113 - accuracy: 0.9082 - val_loss: 0.3775 - val_accuracy: 0.9054 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2991 - accuracy: 0.9102 - val_loss: 0.4075 - val_accuracy: 0.9247 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2560 - accuracy: 0.9365 - val_loss: 0.3893 - val_accuracy: 0.9103 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2622 - accuracy: 0.9360 - val_loss: 0.3810 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2561 - accuracy: 0.9360 - val_loss: 0.3800 - val_accuracy: 0.9215 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1804 - accuracy: 0.9561 - val_loss: 0.2602 - val_accuracy: 0.9295 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1792 - accuracy: 0.9565 - val_loss: 0.3396 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 10: 375.11 sec -Time taken for epoch(SUBo) 10: 323.65 sec -<---------------------------------------|Epoch [10] END|---------------------------------------> - -Epoch: 11/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2826 - accuracy: 0.9058 - val_loss: 0.2663 - val_accuracy: 0.9183 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.3059 - accuracy: 0.9033 - val_loss: 0.3297 - val_accuracy: 0.9199 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.3283 - accuracy: 0.9102 - val_loss: 0.3599 - val_accuracy: 0.9375 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2491 - accuracy: 0.9375 - val_loss: 0.3099 - val_accuracy: 0.9327 -Epoch 5/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.2357 - accuracy: 0.9336 - val_loss: 0.4078 - val_accuracy: 0.9167 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2435 - accuracy: 0.9365 - val_loss: 0.2847 - val_accuracy: 0.9359 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1802 - accuracy: 0.9575 - val_loss: 0.3534 - val_accuracy: 0.9295 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1446 - accuracy: 0.9644 - val_loss: 0.3434 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 11: 374.31 sec -Time taken for epoch(SUBo) 11: 322.89 sec -<---------------------------------------|Epoch [11] END|---------------------------------------> - -Epoch: 12/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 160ms/step - loss: 0.2543 - accuracy: 0.9263 - val_loss: 0.4121 - val_accuracy: 0.9327 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2741 - accuracy: 0.9258 - val_loss: 0.3493 - val_accuracy: 0.9359 -Epoch 3/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2763 - accuracy: 0.9307 - val_loss: 0.3661 - val_accuracy: 0.9359 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2352 - accuracy: 0.9414 - val_loss: 0.3281 - val_accuracy: 0.9215 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2251 - accuracy: 0.9453 - val_loss: 0.2411 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1783 - accuracy: 0.9595 - val_loss: 0.3297 - val_accuracy: 0.9247 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1812 - accuracy: 0.9619 - val_loss: 0.2638 - val_accuracy: 0.9087 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9702 - val_loss: 0.2747 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9391025900840759. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 12: 375.91 sec -Time taken for epoch(SUBo) 12: 324.60 sec -<---------------------------------------|Epoch [12] END|---------------------------------------> - -Epoch: 13/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 159ms/step - loss: 0.2384 - accuracy: 0.9326 - val_loss: 0.2895 - val_accuracy: 0.9231 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2609 - accuracy: 0.9287 - val_loss: 0.2950 - val_accuracy: 0.9119 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2872 - accuracy: 0.9277 - val_loss: 0.3571 - val_accuracy: 0.9087 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2855 - accuracy: 0.9229 - val_loss: 0.5538 - val_accuracy: 0.9087 -Epoch 5/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2314 - accuracy: 0.9478 - val_loss: 0.2693 - val_accuracy: 0.9311 -Epoch 6/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1893 - accuracy: 0.9546 - val_loss: 0.2341 - val_accuracy: 0.9343 -Epoch 7/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1685 - accuracy: 0.9600 - val_loss: 0.2727 - val_accuracy: 0.9439 -Epoch 8/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.1422 - accuracy: 0.9736 - val_loss: 0.2968 - val_accuracy: 0.9407 -Subset training done. -Improved model accuracy from 0.9391025900840759 to 0.9407051205635071. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 13: 376.01 sec -Time taken for epoch(SUBo) 13: 323.00 sec -<---------------------------------------|Epoch [13] END|---------------------------------------> - -Epoch: 14/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 45s 159ms/step - loss: 0.2536 - accuracy: 0.9341 - val_loss: 0.3728 - val_accuracy: 0.9295 -Epoch 2/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2549 - accuracy: 0.9272 - val_loss: 0.2704 - val_accuracy: 0.9279 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2345 - accuracy: 0.9419 - val_loss: 0.3342 - val_accuracy: 0.9327 -Epoch 4/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2225 - accuracy: 0.9541 - val_loss: 0.3081 - val_accuracy: 0.9151 -Epoch 5/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.2233 - accuracy: 0.9443 - val_loss: 0.2983 - val_accuracy: 0.9263 -Epoch 6/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1875 - accuracy: 0.9521 - val_loss: 0.2882 - val_accuracy: 0.9327 -Epoch 7/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1461 - accuracy: 0.9673 - val_loss: 0.2289 - val_accuracy: 0.9359 -Epoch 8/8 -256/256 [==============================] - 40s 156ms/step - loss: 0.1285 - accuracy: 0.9717 - val_loss: 0.2355 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9407051205635071. Not saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 14: 376.09 sec -Time taken for epoch(SUBo) 14: 324.46 sec -<---------------------------------------|Epoch [14] END|---------------------------------------> - -Epoch: 15/256 | [Learning the patterns] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [8]... -Training on subset... -Epoch 1/8 -256/256 [==============================] - 44s 158ms/step - loss: 0.2348 - accuracy: 0.9341 - val_loss: 0.3880 - val_accuracy: 0.9263 -Epoch 2/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2625 - accuracy: 0.9224 - val_loss: 0.3617 - val_accuracy: 0.9327 -Epoch 3/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2578 - accuracy: 0.9292 - val_loss: 0.3288 - val_accuracy: 0.9263 -Epoch 4/8 -256/256 [==============================] - 40s 155ms/step - loss: 0.2543 - accuracy: 0.9302 - val_loss: 0.3120 - val_accuracy: 0.9038 -Epoch 5/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.3444 - accuracy: 0.9067 - val_loss: 0.2470 - val_accuracy: 0.9391 -Epoch 6/8 -256/256 [==============================] - 40s 154ms/step - loss: 0.2173 - accuracy: 0.9424 - val_loss: 0.3219 - val_accuracy: 0.9343 -Epoch 7/8 -256/256 [==============================] - 39s 154ms/step - loss: 0.1908 - accuracy: 0.9526 - val_loss: 0.2278 - val_accuracy: 0.9407 -Epoch 8/8 -256/256 [==============================] - 39s 154ms/step - loss: 0.1584 - accuracy: 0.9600 - val_loss: 0.2384 - val_accuracy: 0.9439 -Subset training done. -Improved model accuracy from 0.9407051205635071 to 0.9439102411270142. Saving model. -Model loss did not improve from 0.22827185690402985. Not saving model. -Time taken for epoch(FULL) 15: 374.28 sec -Time taken for epoch(SUBo) 15: 321.58 sec -<---------------------------------------|Epoch [15] END|---------------------------------------> - -Epoch: 16/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.010000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.2960 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.3020 - accuracy: 0.9111 - val_loss: 0.3527 - val_accuracy: 0.8622 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2673 - accuracy: 0.9238 - val_loss: 0.5715 - val_accuracy: 0.7340 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2500 - accuracy: 0.9277 - val_loss: 0.5034 - val_accuracy: 0.7484 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2083 - accuracy: 0.9478 - val_loss: 0.2478 - val_accuracy: 0.9071 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1548 - accuracy: 0.9624 - val_loss: 0.2110 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Improved model loss from 0.22827185690402985 to 0.21101020276546478. Saving model. -Time taken for epoch(FULL) 16: 297.03 sec -Time taken for epoch(SUBo) 16: 244.35 sec -<---------------------------------------|Epoch [16] END|---------------------------------------> - -Epoch: 17/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.009500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.2628 - accuracy: 0.9282 - val_loss: 0.2316 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2760 - accuracy: 0.9209 - val_loss: 0.3350 - val_accuracy: 0.9279 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2708 - accuracy: 0.9189 - val_loss: 0.3418 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.2240 - accuracy: 0.9419 - val_loss: 0.2829 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1962 - accuracy: 0.9526 - val_loss: 0.2832 - val_accuracy: 0.8926 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1605 - accuracy: 0.9609 - val_loss: 0.2716 - val_accuracy: 0.8974 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 17: 294.63 sec -Time taken for epoch(SUBo) 17: 243.78 sec -<---------------------------------------|Epoch [17] END|---------------------------------------> - -Epoch: 18/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.009000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2515 - accuracy: 0.9263 - val_loss: 0.2493 - val_accuracy: 0.9199 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3009 - accuracy: 0.9219 - val_loss: 0.3727 - val_accuracy: 0.8894 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2677 - accuracy: 0.9224 - val_loss: 0.3309 - val_accuracy: 0.9151 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.2292 - accuracy: 0.9395 - val_loss: 0.2943 - val_accuracy: 0.8910 -Epoch 5/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.1811 - accuracy: 0.9556 - val_loss: 0.2777 - val_accuracy: 0.9087 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1560 - accuracy: 0.9663 - val_loss: 0.2857 - val_accuracy: 0.9215 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 18: 293.64 sec -Time taken for epoch(SUBo) 18: 242.80 sec -<---------------------------------------|Epoch [18] END|---------------------------------------> - -Epoch: 19/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.008500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.2747 - accuracy: 0.9248 - val_loss: 0.2540 - val_accuracy: 0.9038 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.3029 - accuracy: 0.9082 - val_loss: 0.2379 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2496 - accuracy: 0.9297 - val_loss: 0.2431 - val_accuracy: 0.9103 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2807 - accuracy: 0.9087 - val_loss: 0.2517 - val_accuracy: 0.8958 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1925 - accuracy: 0.9463 - val_loss: 0.2512 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9639 - val_loss: 0.2388 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.21101020276546478. Not saving model. -Time taken for epoch(FULL) 19: 287.17 sec -Time taken for epoch(SUBo) 19: 237.22 sec -<---------------------------------------|Epoch [19] END|---------------------------------------> - -Epoch: 20/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.008000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2361 - accuracy: 0.9326 - val_loss: 0.3213 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2437 - accuracy: 0.9282 - val_loss: 0.3130 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2265 - accuracy: 0.9390 - val_loss: 0.7231 - val_accuracy: 0.5817 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2058 - accuracy: 0.9463 - val_loss: 0.2048 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1747 - accuracy: 0.9585 - val_loss: 0.2309 - val_accuracy: 0.9135 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1581 - accuracy: 0.9624 - val_loss: 0.2022 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Improved model loss from 0.21101020276546478 to 0.20221146941184998. Saving model. -Time taken for epoch(FULL) 20: 287.49 sec -Time taken for epoch(SUBo) 20: 236.52 sec -<---------------------------------------|Epoch [20] END|---------------------------------------> - -Epoch: 21/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.007500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2639 - accuracy: 0.9204 - val_loss: 0.3842 - val_accuracy: 0.8542 -Epoch 2/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.2602 - accuracy: 0.9224 - val_loss: 0.2024 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2491 - accuracy: 0.9204 - val_loss: 0.3014 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2034 - accuracy: 0.9521 - val_loss: 0.2709 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2075 - accuracy: 0.9429 - val_loss: 0.3214 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1429 - accuracy: 0.9648 - val_loss: 0.2890 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 21: 285.90 sec -Time taken for epoch(SUBo) 21: 236.69 sec -<---------------------------------------|Epoch [21] END|---------------------------------------> - -Epoch: 22/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.007000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2293 - accuracy: 0.9360 - val_loss: 0.1936 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2278 - accuracy: 0.9341 - val_loss: 0.2616 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2114 - accuracy: 0.9438 - val_loss: 0.2647 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2235 - accuracy: 0.9453 - val_loss: 0.2567 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1777 - accuracy: 0.9541 - val_loss: 0.2569 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1626 - accuracy: 0.9575 - val_loss: 0.2484 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 22: 285.94 sec -Time taken for epoch(SUBo) 22: 236.66 sec -<---------------------------------------|Epoch [22] END|---------------------------------------> - -Epoch: 23/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.006500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2132 - accuracy: 0.9385 - val_loss: 0.2144 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2413 - accuracy: 0.9360 - val_loss: 0.5426 - val_accuracy: 0.8750 -Epoch 3/6 -256/256 [==============================] - 38s 149ms/step - loss: 0.2458 - accuracy: 0.9375 - val_loss: 0.2533 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1869 - accuracy: 0.9453 - val_loss: 0.2258 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9663 - val_loss: 0.2642 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 38s 149ms/step - loss: 0.1227 - accuracy: 0.9746 - val_loss: 0.2471 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9439102411270142. Not saving model. -Model loss did not improve from 0.20221146941184998. Not saving model. -Time taken for epoch(FULL) 23: 284.47 sec -Time taken for epoch(SUBo) 23: 235.20 sec -<---------------------------------------|Epoch [23] END|---------------------------------------> - -Epoch: 24/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.006000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2147 - accuracy: 0.9365 - val_loss: 0.2431 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2151 - accuracy: 0.9385 - val_loss: 0.2308 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2120 - accuracy: 0.9380 - val_loss: 0.2704 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1936 - accuracy: 0.9453 - val_loss: 0.2529 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1086 - accuracy: 0.9756 - val_loss: 0.1858 - val_accuracy: 0.9471 -Subset training done. -Improved model accuracy from 0.9439102411270142 to 0.9471153616905212. Saving model. -Improved model loss from 0.20221146941184998 to 0.1857679933309555. Saving model. -Time taken for epoch(FULL) 24: 288.85 sec -Time taken for epoch(SUBo) 24: 236.73 sec -<---------------------------------------|Epoch [24] END|---------------------------------------> - -Epoch: 25/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.005500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.2106 - accuracy: 0.9414 - val_loss: 0.2085 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2304 - accuracy: 0.9326 - val_loss: 0.2498 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2059 - accuracy: 0.9482 - val_loss: 0.3972 - val_accuracy: 0.9247 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1980 - accuracy: 0.9458 - val_loss: 0.2653 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9731 - val_loss: 0.2222 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1402 - accuracy: 0.9604 - val_loss: 0.2944 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 25: 285.39 sec -Time taken for epoch(SUBo) 25: 236.55 sec -<---------------------------------------|Epoch [25] END|---------------------------------------> - -Epoch: 26/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.005000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2292 - accuracy: 0.9341 - val_loss: 0.2645 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2017 - accuracy: 0.9414 - val_loss: 0.2456 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.2125 - accuracy: 0.9341 - val_loss: 0.3309 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1715 - accuracy: 0.9536 - val_loss: 0.2653 - val_accuracy: 0.9183 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9658 - val_loss: 0.2156 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9741 - val_loss: 0.2134 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 26: 285.12 sec -Time taken for epoch(SUBo) 26: 236.07 sec -<---------------------------------------|Epoch [26] END|---------------------------------------> - -Epoch: 27/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.004500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1868 - accuracy: 0.9463 - val_loss: 0.1853 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2043 - accuracy: 0.9351 - val_loss: 0.3479 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1911 - accuracy: 0.9453 - val_loss: 0.2130 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1510 - accuracy: 0.9600 - val_loss: 0.2097 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1655 - accuracy: 0.9561 - val_loss: 0.1885 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1346 - accuracy: 0.9692 - val_loss: 0.1939 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9471153616905212. Not saving model. -Model loss did not improve from 0.1857679933309555. Not saving model. -Time taken for epoch(FULL) 27: 285.71 sec -Time taken for epoch(SUBo) 27: 236.48 sec -<---------------------------------------|Epoch [27] END|---------------------------------------> - -Epoch: 28/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.004000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2180 - accuracy: 0.9360 - val_loss: 0.1893 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2060 - accuracy: 0.9385 - val_loss: 0.1826 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1867 - accuracy: 0.9448 - val_loss: 0.1701 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1611 - accuracy: 0.9614 - val_loss: 0.1821 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1444 - accuracy: 0.9609 - val_loss: 0.1652 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1409 - accuracy: 0.9644 - val_loss: 0.1546 - val_accuracy: 0.9567 -Subset training done. -Improved model accuracy from 0.9471153616905212 to 0.9567307829856873. Saving model. -Improved model loss from 0.1857679933309555 to 0.15460731089115143. Saving model. -Time taken for epoch(FULL) 28: 288.65 sec -Time taken for epoch(SUBo) 28: 236.43 sec -<---------------------------------------|Epoch [28] END|---------------------------------------> - -Epoch: 29/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.003500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1936 - accuracy: 0.9404 - val_loss: 0.1560 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1892 - accuracy: 0.9390 - val_loss: 0.1654 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1752 - accuracy: 0.9541 - val_loss: 0.2738 - val_accuracy: 0.8926 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1570 - accuracy: 0.9561 - val_loss: 0.1721 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1441 - accuracy: 0.9639 - val_loss: 0.1639 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1111 - accuracy: 0.9692 - val_loss: 0.1661 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 29: 285.51 sec -Time taken for epoch(SUBo) 29: 236.35 sec -<---------------------------------------|Epoch [29] END|---------------------------------------> - -Epoch: 30/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.003000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1650 - accuracy: 0.9531 - val_loss: 0.1881 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1823 - accuracy: 0.9468 - val_loss: 0.2431 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1812 - accuracy: 0.9473 - val_loss: 0.1803 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1608 - accuracy: 0.9546 - val_loss: 0.1606 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9609 - val_loss: 0.1624 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1155 - accuracy: 0.9702 - val_loss: 0.1665 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 30: 285.48 sec -Time taken for epoch(SUBo) 30: 236.40 sec -<---------------------------------------|Epoch [30] END|---------------------------------------> - -Epoch: 31/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.002500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1981 - accuracy: 0.9370 - val_loss: 0.1560 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1661 - accuracy: 0.9482 - val_loss: 0.1612 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1624 - accuracy: 0.9517 - val_loss: 0.1743 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1685 - accuracy: 0.9517 - val_loss: 0.1903 - val_accuracy: 0.9247 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1254 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9231 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9707 - val_loss: 0.1807 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 31: 285.66 sec -Time taken for epoch(SUBo) 31: 236.69 sec -<---------------------------------------|Epoch [31] END|---------------------------------------> - -Epoch: 32/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.002000]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1669 - accuracy: 0.9502 - val_loss: 0.1911 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1540 - accuracy: 0.9531 - val_loss: 0.1633 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9624 - val_loss: 0.1597 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1643 - accuracy: 0.9551 - val_loss: 0.1712 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9585 - val_loss: 0.1951 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1076 - accuracy: 0.9658 - val_loss: 0.1953 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 32: 285.69 sec -Time taken for epoch(SUBo) 32: 236.38 sec -<---------------------------------------|Epoch [32] END|---------------------------------------> - -Epoch: 33/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1745 - accuracy: 0.9463 - val_loss: 0.1852 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1641 - accuracy: 0.9512 - val_loss: 0.1889 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1578 - accuracy: 0.9512 - val_loss: 0.1950 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1493 - accuracy: 0.9507 - val_loss: 0.1669 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.1736 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1185 - accuracy: 0.9658 - val_loss: 0.1680 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15460731089115143. Not saving model. -Time taken for epoch(FULL) 33: 286.31 sec -Time taken for epoch(SUBo) 33: 236.60 sec -<---------------------------------------|Epoch [33] END|---------------------------------------> - -Epoch: 34/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1615 - accuracy: 0.9521 - val_loss: 0.1627 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1649 - accuracy: 0.9521 - val_loss: 0.2083 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1949 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1419 - accuracy: 0.9517 - val_loss: 0.1563 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1317 - accuracy: 0.9565 - val_loss: 0.1606 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1158 - accuracy: 0.9688 - val_loss: 0.1512 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Improved model loss from 0.15460731089115143 to 0.15118563175201416. Saving model. -Time taken for epoch(FULL) 34: 287.71 sec -Time taken for epoch(SUBo) 34: 236.71 sec -<---------------------------------------|Epoch [34] END|---------------------------------------> - -Epoch: 35/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1739 - accuracy: 0.9443 - val_loss: 0.1441 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2022 - accuracy: 0.9336 - val_loss: 0.1491 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1754 - accuracy: 0.9458 - val_loss: 0.1782 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1629 - accuracy: 0.9458 - val_loss: 0.1656 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1582 - accuracy: 0.9546 - val_loss: 0.1640 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9531 - val_loss: 0.1650 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 35: 287.73 sec -Time taken for epoch(SUBo) 35: 237.55 sec -<---------------------------------------|Epoch [35] END|---------------------------------------> - -Epoch: 36/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9526 - val_loss: 0.1498 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1602 - accuracy: 0.9468 - val_loss: 0.1686 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9521 - val_loss: 0.1585 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1683 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9604 - val_loss: 0.1843 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1206 - accuracy: 0.9644 - val_loss: 0.1951 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 36: 287.50 sec -Time taken for epoch(SUBo) 36: 237.39 sec -<---------------------------------------|Epoch [36] END|---------------------------------------> - -Epoch: 37/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1843 - accuracy: 0.9414 - val_loss: 0.1578 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1683 - accuracy: 0.9497 - val_loss: 0.1731 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1612 - accuracy: 0.9463 - val_loss: 0.2032 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1507 - accuracy: 0.9521 - val_loss: 0.1985 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9590 - val_loss: 0.1618 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9595 - val_loss: 0.1653 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 37: 287.80 sec -Time taken for epoch(SUBo) 37: 237.45 sec -<---------------------------------------|Epoch [37] END|---------------------------------------> - -Epoch: 38/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1649 - accuracy: 0.9487 - val_loss: 0.1677 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1775 - accuracy: 0.9438 - val_loss: 0.1582 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1564 - accuracy: 0.9526 - val_loss: 0.1516 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1513 - accuracy: 0.9541 - val_loss: 0.1526 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1408 - accuracy: 0.9595 - val_loss: 0.1522 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1186 - accuracy: 0.9634 - val_loss: 0.1668 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 38: 287.81 sec -Time taken for epoch(SUBo) 38: 237.65 sec -<---------------------------------------|Epoch [38] END|---------------------------------------> - -Epoch: 39/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1748 - accuracy: 0.9414 - val_loss: 0.1468 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9521 - val_loss: 0.1940 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1527 - accuracy: 0.9536 - val_loss: 0.1679 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1440 - accuracy: 0.9521 - val_loss: 0.2192 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1304 - accuracy: 0.9570 - val_loss: 0.1655 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1088 - accuracy: 0.9697 - val_loss: 0.1865 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 39: 288.44 sec -Time taken for epoch(SUBo) 39: 237.93 sec -<---------------------------------------|Epoch [39] END|---------------------------------------> - -Epoch: 40/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1613 - accuracy: 0.9502 - val_loss: 0.1476 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1465 - accuracy: 0.9590 - val_loss: 0.1613 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1391 - accuracy: 0.9609 - val_loss: 0.1533 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1231 - accuracy: 0.9648 - val_loss: 0.1602 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1393 - accuracy: 0.9609 - val_loss: 0.1537 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1065 - accuracy: 0.9727 - val_loss: 0.1562 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 40: 287.78 sec -Time taken for epoch(SUBo) 40: 237.78 sec -<---------------------------------------|Epoch [40] END|---------------------------------------> - -Epoch: 41/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1631 - accuracy: 0.9478 - val_loss: 0.1572 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1542 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1441 - accuracy: 0.9531 - val_loss: 0.1653 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9614 - val_loss: 0.1968 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1599 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1870 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 41: 287.23 sec -Time taken for epoch(SUBo) 41: 237.14 sec -<---------------------------------------|Epoch [41] END|---------------------------------------> - -Epoch: 42/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1298 - accuracy: 0.9531 - val_loss: 0.2101 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9565 - val_loss: 0.1721 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1334 - accuracy: 0.9614 - val_loss: 0.1705 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9678 - val_loss: 0.1819 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1071 - accuracy: 0.9678 - val_loss: 0.1882 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0998 - accuracy: 0.9712 - val_loss: 0.2143 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 42: 287.96 sec -Time taken for epoch(SUBo) 42: 237.29 sec -<---------------------------------------|Epoch [42] END|---------------------------------------> - -Epoch: 43/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1517 - accuracy: 0.9556 - val_loss: 0.1814 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.2212 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2157 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2624 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1266 - accuracy: 0.9648 - val_loss: 0.2113 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2185 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 43: 288.58 sec -Time taken for epoch(SUBo) 43: 237.71 sec -<---------------------------------------|Epoch [43] END|---------------------------------------> - -Epoch: 44/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1549 - accuracy: 0.9507 - val_loss: 0.1907 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1406 - accuracy: 0.9561 - val_loss: 0.1945 - val_accuracy: 0.9279 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9556 - val_loss: 0.2094 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1300 - accuracy: 0.9619 - val_loss: 0.2000 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1146 - accuracy: 0.9678 - val_loss: 0.2591 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1207 - accuracy: 0.9648 - val_loss: 0.2343 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 44: 288.18 sec -Time taken for epoch(SUBo) 44: 237.53 sec -<---------------------------------------|Epoch [44] END|---------------------------------------> - -Epoch: 45/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1691 - accuracy: 0.9507 - val_loss: 0.1829 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9570 - val_loss: 0.1635 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1363 - accuracy: 0.9609 - val_loss: 0.2010 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1235 - accuracy: 0.9624 - val_loss: 0.1995 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9600 - val_loss: 0.2820 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1434 - accuracy: 0.9512 - val_loss: 0.2766 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 45: 288.43 sec -Time taken for epoch(SUBo) 45: 237.92 sec -<---------------------------------------|Epoch [45] END|---------------------------------------> - -Epoch: 46/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1684 - accuracy: 0.9468 - val_loss: 0.3024 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1606 - accuracy: 0.9478 - val_loss: 0.3133 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1545 - accuracy: 0.9585 - val_loss: 0.2165 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1639 - accuracy: 0.9468 - val_loss: 0.2465 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.2787 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9551 - val_loss: 0.2559 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 46: 288.00 sec -Time taken for epoch(SUBo) 46: 237.42 sec -<---------------------------------------|Epoch [46] END|---------------------------------------> - -Epoch: 47/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1874 - accuracy: 0.9414 - val_loss: 0.2024 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1816 - accuracy: 0.9487 - val_loss: 0.2076 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1674 - accuracy: 0.9434 - val_loss: 0.3245 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1442 - accuracy: 0.9604 - val_loss: 0.2564 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1221 - accuracy: 0.9609 - val_loss: 0.3057 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1317 - accuracy: 0.9556 - val_loss: 0.2604 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 47: 287.69 sec -Time taken for epoch(SUBo) 47: 236.59 sec -<---------------------------------------|Epoch [47] END|---------------------------------------> - -Epoch: 48/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1541 - accuracy: 0.9453 - val_loss: 0.2779 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1480 - accuracy: 0.9526 - val_loss: 0.2490 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1341 - accuracy: 0.9614 - val_loss: 0.2237 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1366 - accuracy: 0.9570 - val_loss: 0.2314 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2416 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1106 - accuracy: 0.9644 - val_loss: 0.2330 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 48: 286.84 sec -Time taken for epoch(SUBo) 48: 236.62 sec -<---------------------------------------|Epoch [48] END|---------------------------------------> - -Epoch: 49/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1551 - accuracy: 0.9561 - val_loss: 0.2252 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9570 - val_loss: 0.2131 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1401 - accuracy: 0.9580 - val_loss: 0.1908 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1271 - accuracy: 0.9639 - val_loss: 0.2179 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1260 - accuracy: 0.9634 - val_loss: 0.2022 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1087 - accuracy: 0.9717 - val_loss: 0.1932 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 49: 286.33 sec -Time taken for epoch(SUBo) 49: 236.60 sec -<---------------------------------------|Epoch [49] END|---------------------------------------> - -Epoch: 50/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1449 - accuracy: 0.9521 - val_loss: 0.1748 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1448 - accuracy: 0.9507 - val_loss: 0.2003 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9521 - val_loss: 0.2190 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9390 - val_loss: 0.2207 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9521 - val_loss: 0.2131 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1572 - accuracy: 0.9478 - val_loss: 0.2142 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 50: 286.59 sec -Time taken for epoch(SUBo) 50: 236.86 sec -<---------------------------------------|Epoch [50] END|---------------------------------------> - -Epoch: 51/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1601 - accuracy: 0.9497 - val_loss: 0.1783 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1519 - accuracy: 0.9517 - val_loss: 0.2485 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1687 - accuracy: 0.9521 - val_loss: 0.2295 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1445 - accuracy: 0.9600 - val_loss: 0.2580 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1283 - accuracy: 0.9619 - val_loss: 0.2596 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1248 - accuracy: 0.9624 - val_loss: 0.2709 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 51: 286.23 sec -Time taken for epoch(SUBo) 51: 236.38 sec -<---------------------------------------|Epoch [51] END|---------------------------------------> - -Epoch: 52/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1478 - accuracy: 0.9512 - val_loss: 0.2317 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1364 - accuracy: 0.9614 - val_loss: 0.2805 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9634 - val_loss: 0.2886 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2800 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1081 - accuracy: 0.9712 - val_loss: 0.2406 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9702 - val_loss: 0.2587 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 52: 286.99 sec -Time taken for epoch(SUBo) 52: 236.83 sec -<---------------------------------------|Epoch [52] END|---------------------------------------> - -Epoch: 53/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2206 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9468 - val_loss: 0.2399 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1522 - accuracy: 0.9546 - val_loss: 0.2213 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9546 - val_loss: 0.1943 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1258 - accuracy: 0.9580 - val_loss: 0.1851 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9541 - val_loss: 0.1898 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 53: 287.29 sec -Time taken for epoch(SUBo) 53: 237.19 sec -<---------------------------------------|Epoch [53] END|---------------------------------------> - -Epoch: 54/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1649 - accuracy: 0.9429 - val_loss: 0.2123 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1791 - accuracy: 0.9424 - val_loss: 0.2041 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1739 - accuracy: 0.9429 - val_loss: 0.2438 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1467 - accuracy: 0.9521 - val_loss: 0.2370 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1384 - accuracy: 0.9541 - val_loss: 0.3072 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1439 - accuracy: 0.9580 - val_loss: 0.2901 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 54: 287.00 sec -Time taken for epoch(SUBo) 54: 236.97 sec -<---------------------------------------|Epoch [54] END|---------------------------------------> - -Epoch: 55/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1734 - accuracy: 0.9438 - val_loss: 0.2456 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1551 - accuracy: 0.9512 - val_loss: 0.2227 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9468 - val_loss: 0.2150 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1365 - accuracy: 0.9600 - val_loss: 0.1964 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9595 - val_loss: 0.2038 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.2228 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 55: 286.51 sec -Time taken for epoch(SUBo) 55: 236.75 sec -<---------------------------------------|Epoch [55] END|---------------------------------------> - -Epoch: 56/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1372 - accuracy: 0.9575 - val_loss: 0.2215 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1534 - accuracy: 0.9541 - val_loss: 0.2516 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9629 - val_loss: 0.2329 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9673 - val_loss: 0.2124 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1028 - accuracy: 0.9727 - val_loss: 0.2299 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0982 - accuracy: 0.9736 - val_loss: 0.2280 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 56: 286.73 sec -Time taken for epoch(SUBo) 56: 237.12 sec -<---------------------------------------|Epoch [56] END|---------------------------------------> - -Epoch: 57/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 154ms/step - loss: 0.1279 - accuracy: 0.9604 - val_loss: 0.1954 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9590 - val_loss: 0.2062 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1403 - accuracy: 0.9580 - val_loss: 0.1679 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1308 - accuracy: 0.9570 - val_loss: 0.1776 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1117 - accuracy: 0.9648 - val_loss: 0.1890 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1019 - accuracy: 0.9717 - val_loss: 0.1922 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 57: 286.14 sec -Time taken for epoch(SUBo) 57: 235.92 sec -<---------------------------------------|Epoch [57] END|---------------------------------------> - -Epoch: 58/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1579 - accuracy: 0.9468 - val_loss: 0.1934 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1771 - accuracy: 0.9409 - val_loss: 0.1981 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1471 - accuracy: 0.9561 - val_loss: 0.2460 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9595 - val_loss: 0.1832 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9536 - val_loss: 0.1711 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1317 - accuracy: 0.9609 - val_loss: 0.1742 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 58: 287.08 sec -Time taken for epoch(SUBo) 58: 236.57 sec -<---------------------------------------|Epoch [58] END|---------------------------------------> - -Epoch: 59/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1481 - accuracy: 0.9551 - val_loss: 0.1874 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1438 - accuracy: 0.9546 - val_loss: 0.1799 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1512 - accuracy: 0.9575 - val_loss: 0.1774 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1369 - accuracy: 0.9595 - val_loss: 0.1793 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1269 - accuracy: 0.9663 - val_loss: 0.1713 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9688 - val_loss: 0.1879 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 59: 286.52 sec -Time taken for epoch(SUBo) 59: 237.12 sec -<---------------------------------------|Epoch [59] END|---------------------------------------> - -Epoch: 60/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9531 - val_loss: 0.1852 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1386 - accuracy: 0.9575 - val_loss: 0.1995 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9663 - val_loss: 0.2111 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1169 - accuracy: 0.9663 - val_loss: 0.2195 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1004 - accuracy: 0.9717 - val_loss: 0.2351 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9668 - val_loss: 0.2677 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 60: 287.80 sec -Time taken for epoch(SUBo) 60: 237.20 sec -<---------------------------------------|Epoch [60] END|---------------------------------------> - -Epoch: 61/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1434 - accuracy: 0.9551 - val_loss: 0.2024 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1346 - accuracy: 0.9604 - val_loss: 0.2110 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1218 - accuracy: 0.9644 - val_loss: 0.1917 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9629 - val_loss: 0.2180 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1204 - accuracy: 0.9639 - val_loss: 0.1932 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1012 - accuracy: 0.9683 - val_loss: 0.1964 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 61: 288.27 sec -Time taken for epoch(SUBo) 61: 237.72 sec -<---------------------------------------|Epoch [61] END|---------------------------------------> - -Epoch: 62/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1389 - accuracy: 0.9575 - val_loss: 0.2335 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1295 - accuracy: 0.9561 - val_loss: 0.2828 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1223 - accuracy: 0.9619 - val_loss: 0.2642 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9673 - val_loss: 0.2734 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.2583 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1019 - accuracy: 0.9707 - val_loss: 0.2563 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 62: 288.25 sec -Time taken for epoch(SUBo) 62: 237.52 sec -<---------------------------------------|Epoch [62] END|---------------------------------------> - -Epoch: 63/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1588 - accuracy: 0.9517 - val_loss: 0.2404 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1392 - accuracy: 0.9624 - val_loss: 0.1892 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.1851 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9634 - val_loss: 0.1914 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1298 - accuracy: 0.9619 - val_loss: 0.2004 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9673 - val_loss: 0.1989 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 63: 288.35 sec -Time taken for epoch(SUBo) 63: 237.48 sec -<---------------------------------------|Epoch [63] END|---------------------------------------> - -Epoch: 64/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1376 - accuracy: 0.9556 - val_loss: 0.1802 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1370 - accuracy: 0.9575 - val_loss: 0.2342 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1335 - accuracy: 0.9604 - val_loss: 0.1916 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1462 - accuracy: 0.9580 - val_loss: 0.1591 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1061 - accuracy: 0.9663 - val_loss: 0.2386 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9688 - val_loss: 0.2423 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 64: 288.62 sec -Time taken for epoch(SUBo) 64: 237.68 sec -<---------------------------------------|Epoch [64] END|---------------------------------------> - -Epoch: 65/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1365 - accuracy: 0.9556 - val_loss: 0.2579 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1324 - accuracy: 0.9595 - val_loss: 0.2196 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9619 - val_loss: 0.2640 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1136 - accuracy: 0.9663 - val_loss: 0.2262 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1052 - accuracy: 0.9692 - val_loss: 0.2272 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0993 - accuracy: 0.9697 - val_loss: 0.2402 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 65: 288.68 sec -Time taken for epoch(SUBo) 65: 238.01 sec -<---------------------------------------|Epoch [65] END|---------------------------------------> - -Epoch: 66/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9590 - val_loss: 0.2096 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1272 - accuracy: 0.9604 - val_loss: 0.2505 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1286 - accuracy: 0.9561 - val_loss: 0.2210 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9683 - val_loss: 0.1834 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1793 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1017 - accuracy: 0.9697 - val_loss: 0.2070 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 66: 288.12 sec -Time taken for epoch(SUBo) 66: 237.92 sec -<---------------------------------------|Epoch [66] END|---------------------------------------> - -Epoch: 67/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1517 - accuracy: 0.9565 - val_loss: 0.1927 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1471 - accuracy: 0.9502 - val_loss: 0.2064 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2286 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1262 - accuracy: 0.9619 - val_loss: 0.1877 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1157 - accuracy: 0.9639 - val_loss: 0.1992 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9658 - val_loss: 0.1889 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 67: 288.09 sec -Time taken for epoch(SUBo) 67: 237.40 sec -<---------------------------------------|Epoch [67] END|---------------------------------------> - -Epoch: 68/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1311 - accuracy: 0.9556 - val_loss: 0.1958 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1119 - accuracy: 0.9644 - val_loss: 0.2010 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1263 - accuracy: 0.9595 - val_loss: 0.1595 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9595 - val_loss: 0.1492 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1132 - accuracy: 0.9639 - val_loss: 0.1464 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1003 - accuracy: 0.9712 - val_loss: 0.1529 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 68: 288.54 sec -Time taken for epoch(SUBo) 68: 237.57 sec -<---------------------------------------|Epoch [68] END|---------------------------------------> - -Epoch: 69/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1554 - accuracy: 0.9546 - val_loss: 0.1697 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1428 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1287 - accuracy: 0.9629 - val_loss: 0.2158 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1152 - accuracy: 0.9634 - val_loss: 0.1788 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1029 - accuracy: 0.9697 - val_loss: 0.1732 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0991 - accuracy: 0.9722 - val_loss: 0.1837 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 69: 287.94 sec -Time taken for epoch(SUBo) 69: 237.46 sec -<---------------------------------------|Epoch [69] END|---------------------------------------> - -Epoch: 70/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1386 - accuracy: 0.9648 - val_loss: 0.1742 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.2681 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1782 - accuracy: 0.9482 - val_loss: 0.3058 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1468 - accuracy: 0.9526 - val_loss: 0.2156 - val_accuracy: 0.9327 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1217 - accuracy: 0.9634 - val_loss: 0.1891 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9668 - val_loss: 0.1983 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9567307829856873. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 70: 288.24 sec -Time taken for epoch(SUBo) 70: 237.80 sec -<---------------------------------------|Epoch [70] END|---------------------------------------> - -Epoch: 71/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.1688 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1528 - accuracy: 0.9546 - val_loss: 0.1514 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9609 - val_loss: 0.1770 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9585 - val_loss: 0.1579 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1195 - accuracy: 0.9653 - val_loss: 0.1543 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1538 - val_accuracy: 0.9599 -Subset training done. -Improved model accuracy from 0.9567307829856873 to 0.9599359035491943. Saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 71: 289.74 sec -Time taken for epoch(SUBo) 71: 237.66 sec -<---------------------------------------|Epoch [71] END|---------------------------------------> - -Epoch: 72/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1505 - accuracy: 0.9521 - val_loss: 0.1529 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1589 - accuracy: 0.9512 - val_loss: 0.1426 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1484 - accuracy: 0.9546 - val_loss: 0.1592 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9658 - val_loss: 0.1791 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1154 - accuracy: 0.9673 - val_loss: 0.1634 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 72: 288.91 sec -Time taken for epoch(SUBo) 72: 237.07 sec -<---------------------------------------|Epoch [72] END|---------------------------------------> - -Epoch: 73/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1304 - accuracy: 0.9609 - val_loss: 0.1894 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1949 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9526 - val_loss: 0.2177 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.2006 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1074 - accuracy: 0.9746 - val_loss: 0.2530 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9692 - val_loss: 0.2516 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 73: 286.60 sec -Time taken for epoch(SUBo) 73: 236.85 sec -<---------------------------------------|Epoch [73] END|---------------------------------------> - -Epoch: 74/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1103 - accuracy: 0.9653 - val_loss: 0.2006 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1242 - accuracy: 0.9600 - val_loss: 0.2702 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1351 - accuracy: 0.9580 - val_loss: 0.2475 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9731 - val_loss: 0.2133 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2043 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 150ms/step - loss: 0.0768 - accuracy: 0.9780 - val_loss: 0.2014 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 74: 287.09 sec -Time taken for epoch(SUBo) 74: 237.29 sec -<---------------------------------------|Epoch [74] END|---------------------------------------> - -Epoch: 75/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1430 - accuracy: 0.9546 - val_loss: 0.2063 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1306 - accuracy: 0.9619 - val_loss: 0.1984 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1205 - accuracy: 0.9663 - val_loss: 0.1844 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1186 - accuracy: 0.9653 - val_loss: 0.1739 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9727 - val_loss: 0.1955 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0930 - accuracy: 0.9731 - val_loss: 0.1780 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 75: 287.36 sec -Time taken for epoch(SUBo) 75: 237.36 sec -<---------------------------------------|Epoch [75] END|---------------------------------------> - -Epoch: 76/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1332 - accuracy: 0.9561 - val_loss: 0.1757 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1358 - accuracy: 0.9590 - val_loss: 0.1649 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1475 - accuracy: 0.9546 - val_loss: 0.1689 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9570 - val_loss: 0.1557 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1127 - accuracy: 0.9619 - val_loss: 0.1633 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9717 - val_loss: 0.1716 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 76: 286.87 sec -Time taken for epoch(SUBo) 76: 237.21 sec -<---------------------------------------|Epoch [76] END|---------------------------------------> - -Epoch: 77/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1613 - accuracy: 0.9429 - val_loss: 0.1702 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1557 - accuracy: 0.9463 - val_loss: 0.1623 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9546 - val_loss: 0.2084 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1245 - accuracy: 0.9619 - val_loss: 0.2221 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1156 - accuracy: 0.9624 - val_loss: 0.2435 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9683 - val_loss: 0.2508 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 77: 286.93 sec -Time taken for epoch(SUBo) 77: 236.81 sec -<---------------------------------------|Epoch [77] END|---------------------------------------> - -Epoch: 78/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1258 - accuracy: 0.9609 - val_loss: 0.1880 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1473 - accuracy: 0.9507 - val_loss: 0.1763 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1170 - accuracy: 0.9658 - val_loss: 0.2302 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9551 - val_loss: 0.1573 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1236 - accuracy: 0.9585 - val_loss: 0.1819 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1150 - accuracy: 0.9639 - val_loss: 0.1925 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 78: 287.03 sec -Time taken for epoch(SUBo) 78: 237.13 sec -<---------------------------------------|Epoch [78] END|---------------------------------------> - -Epoch: 79/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1394 - accuracy: 0.9570 - val_loss: 0.1949 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1345 - accuracy: 0.9604 - val_loss: 0.2434 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.2313 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1145 - accuracy: 0.9648 - val_loss: 0.2336 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1077 - accuracy: 0.9707 - val_loss: 0.2261 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1020 - accuracy: 0.9688 - val_loss: 0.2249 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 79: 286.93 sec -Time taken for epoch(SUBo) 79: 237.31 sec -<---------------------------------------|Epoch [79] END|---------------------------------------> - -Epoch: 80/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9512 - val_loss: 0.2335 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2401 - val_accuracy: 0.9183 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2689 - accuracy: 0.9048 - val_loss: 0.4998 - val_accuracy: 0.7821 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.2924 - accuracy: 0.8955 - val_loss: 0.4549 - val_accuracy: 0.8782 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2427 - accuracy: 0.9136 - val_loss: 0.3899 - val_accuracy: 0.8830 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.2071 - accuracy: 0.9292 - val_loss: 0.3938 - val_accuracy: 0.8830 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 80: 287.37 sec -Time taken for epoch(SUBo) 80: 237.50 sec -<---------------------------------------|Epoch [80] END|---------------------------------------> - -Epoch: 81/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.2117 - accuracy: 0.9272 - val_loss: 0.3888 - val_accuracy: 0.8942 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.2039 - accuracy: 0.9326 - val_loss: 0.4718 - val_accuracy: 0.9038 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1797 - accuracy: 0.9424 - val_loss: 0.4449 - val_accuracy: 0.9087 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1627 - accuracy: 0.9512 - val_loss: 0.2830 - val_accuracy: 0.9151 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1495 - accuracy: 0.9565 - val_loss: 0.3565 - val_accuracy: 0.9167 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9541 - val_loss: 0.3372 - val_accuracy: 0.9199 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 81: 287.21 sec -Time taken for epoch(SUBo) 81: 237.47 sec -<---------------------------------------|Epoch [81] END|---------------------------------------> - -Epoch: 82/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1753 - accuracy: 0.9424 - val_loss: 0.3639 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 38s 150ms/step - loss: 0.1803 - accuracy: 0.9429 - val_loss: 0.3132 - val_accuracy: 0.9215 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1485 - accuracy: 0.9565 - val_loss: 0.2975 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.3335 - val_accuracy: 0.9247 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9561 - val_loss: 0.2650 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9653 - val_loss: 0.2362 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 82: 286.96 sec -Time taken for epoch(SUBo) 82: 236.90 sec -<---------------------------------------|Epoch [82] END|---------------------------------------> - -Epoch: 83/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1623 - accuracy: 0.9492 - val_loss: 0.2152 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1599 - accuracy: 0.9502 - val_loss: 0.2598 - val_accuracy: 0.9231 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1508 - accuracy: 0.9609 - val_loss: 0.2304 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9517 - val_loss: 0.2164 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9624 - val_loss: 0.2169 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1250 - accuracy: 0.9595 - val_loss: 0.2147 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 83: 287.52 sec -Time taken for epoch(SUBo) 83: 237.43 sec -<---------------------------------------|Epoch [83] END|---------------------------------------> - -Epoch: 84/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9595 - val_loss: 0.2386 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.1926 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1437 - accuracy: 0.9526 - val_loss: 0.2082 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1389 - accuracy: 0.9556 - val_loss: 0.2051 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1211 - accuracy: 0.9634 - val_loss: 0.1852 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9702 - val_loss: 0.1848 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 84: 286.91 sec -Time taken for epoch(SUBo) 84: 237.24 sec -<---------------------------------------|Epoch [84] END|---------------------------------------> - -Epoch: 85/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1612 - accuracy: 0.9478 - val_loss: 0.2066 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1647 - accuracy: 0.9448 - val_loss: 0.1899 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1606 - accuracy: 0.9448 - val_loss: 0.1948 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1336 - accuracy: 0.9561 - val_loss: 0.1954 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.1911 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.1925 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 85: 288.09 sec -Time taken for epoch(SUBo) 85: 237.57 sec -<---------------------------------------|Epoch [85] END|---------------------------------------> - -Epoch: 86/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1545 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1532 - accuracy: 0.9517 - val_loss: 0.2042 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1454 - accuracy: 0.9492 - val_loss: 0.1683 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1330 - accuracy: 0.9604 - val_loss: 0.1693 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1233 - accuracy: 0.9604 - val_loss: 0.1930 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1207 - accuracy: 0.9619 - val_loss: 0.1804 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 86: 288.17 sec -Time taken for epoch(SUBo) 86: 237.64 sec -<---------------------------------------|Epoch [86] END|---------------------------------------> - -Epoch: 87/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9536 - val_loss: 0.1667 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1656 - accuracy: 0.9478 - val_loss: 0.1621 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1384 - accuracy: 0.9595 - val_loss: 0.1620 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9585 - val_loss: 0.1718 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1227 - accuracy: 0.9595 - val_loss: 0.1562 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1226 - accuracy: 0.9653 - val_loss: 0.1679 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 87: 288.63 sec -Time taken for epoch(SUBo) 87: 237.58 sec -<---------------------------------------|Epoch [87] END|---------------------------------------> - -Epoch: 88/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1496 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1700 - accuracy: 0.9399 - val_loss: 0.1543 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1560 - accuracy: 0.9546 - val_loss: 0.1877 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.1802 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1187 - accuracy: 0.9609 - val_loss: 0.1640 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1221 - accuracy: 0.9629 - val_loss: 0.1898 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 88: 289.56 sec -Time taken for epoch(SUBo) 88: 238.18 sec -<---------------------------------------|Epoch [88] END|---------------------------------------> - -Epoch: 89/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1682 - accuracy: 0.9497 - val_loss: 0.1799 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1313 - accuracy: 0.9580 - val_loss: 0.2257 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9585 - val_loss: 0.2209 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1873 - accuracy: 0.9399 - val_loss: 0.1585 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1725 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1436 - accuracy: 0.9580 - val_loss: 0.1682 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 89: 288.75 sec -Time taken for epoch(SUBo) 89: 238.29 sec -<---------------------------------------|Epoch [89] END|---------------------------------------> - -Epoch: 90/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1505 - accuracy: 0.9502 - val_loss: 0.1977 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1613 - accuracy: 0.9478 - val_loss: 0.1510 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1232 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1183 - accuracy: 0.9658 - val_loss: 0.1810 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9717 - val_loss: 0.1728 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9658 - val_loss: 0.1794 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 90: 288.69 sec -Time taken for epoch(SUBo) 90: 237.88 sec -<---------------------------------------|Epoch [90] END|---------------------------------------> - -Epoch: 91/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1654 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1286 - accuracy: 0.9604 - val_loss: 0.2092 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1339 - accuracy: 0.9604 - val_loss: 0.1610 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1881 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9688 - val_loss: 0.2103 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0968 - accuracy: 0.9741 - val_loss: 0.2091 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 91: 290.04 sec -Time taken for epoch(SUBo) 91: 238.32 sec -<---------------------------------------|Epoch [91] END|---------------------------------------> - -Epoch: 92/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1806 - accuracy: 0.9453 - val_loss: 0.1973 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1625 - accuracy: 0.9502 - val_loss: 0.1934 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1476 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9551 - val_loss: 0.1942 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1282 - accuracy: 0.9580 - val_loss: 0.1883 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1260 - accuracy: 0.9619 - val_loss: 0.1955 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 92: 288.96 sec -Time taken for epoch(SUBo) 92: 237.66 sec -<---------------------------------------|Epoch [92] END|---------------------------------------> - -Epoch: 93/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1499 - accuracy: 0.9473 - val_loss: 0.1841 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1426 - accuracy: 0.9507 - val_loss: 0.2240 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1467 - accuracy: 0.9600 - val_loss: 0.1832 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1411 - accuracy: 0.9531 - val_loss: 0.4701 - val_accuracy: 0.8910 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1303 - accuracy: 0.9600 - val_loss: 0.3182 - val_accuracy: 0.9103 -Epoch 6/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.1197 - accuracy: 0.9692 - val_loss: 0.2972 - val_accuracy: 0.9151 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 93: 290.33 sec -Time taken for epoch(SUBo) 93: 239.01 sec -<---------------------------------------|Epoch [93] END|---------------------------------------> - -Epoch: 94/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1449 - accuracy: 0.9536 - val_loss: 0.2477 - val_accuracy: 0.9295 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1876 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9526 - val_loss: 0.2062 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1405 - accuracy: 0.9531 - val_loss: 0.1995 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1120 - accuracy: 0.9692 - val_loss: 0.2110 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9712 - val_loss: 0.2041 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 94: 289.36 sec -Time taken for epoch(SUBo) 94: 238.47 sec -<---------------------------------------|Epoch [94] END|---------------------------------------> - -Epoch: 95/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1489 - accuracy: 0.9580 - val_loss: 0.1769 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1445 - accuracy: 0.9512 - val_loss: 0.1728 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1269 - accuracy: 0.9565 - val_loss: 0.2260 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1205 - accuracy: 0.9624 - val_loss: 0.1696 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1278 - accuracy: 0.9624 - val_loss: 0.1737 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1040 - accuracy: 0.9707 - val_loss: 0.1714 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 95: 289.33 sec -Time taken for epoch(SUBo) 95: 238.40 sec -<---------------------------------------|Epoch [95] END|---------------------------------------> - -Epoch: 96/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1672 - accuracy: 0.9492 - val_loss: 0.1677 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1451 - accuracy: 0.9565 - val_loss: 0.1917 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9614 - val_loss: 0.2296 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.2639 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0987 - accuracy: 0.9717 - val_loss: 0.3081 - val_accuracy: 0.9215 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9653 - val_loss: 0.2600 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 96: 288.89 sec -Time taken for epoch(SUBo) 96: 237.88 sec -<---------------------------------------|Epoch [96] END|---------------------------------------> - -Epoch: 97/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1431 - accuracy: 0.9463 - val_loss: 0.2139 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1526 - accuracy: 0.9492 - val_loss: 0.2200 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1348 - accuracy: 0.9575 - val_loss: 0.2507 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9575 - val_loss: 0.2652 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9683 - val_loss: 0.2767 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1255 - accuracy: 0.9604 - val_loss: 0.2645 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 97: 288.48 sec -Time taken for epoch(SUBo) 97: 237.23 sec -<---------------------------------------|Epoch [97] END|---------------------------------------> - -Epoch: 98/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2275 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1329 - accuracy: 0.9614 - val_loss: 0.2393 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1515 - accuracy: 0.9556 - val_loss: 0.3716 - val_accuracy: 0.9135 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1402 - accuracy: 0.9595 - val_loss: 0.3404 - val_accuracy: 0.9087 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9712 - val_loss: 0.2649 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9648 - val_loss: 0.2462 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 98: 287.65 sec -Time taken for epoch(SUBo) 98: 237.26 sec -<---------------------------------------|Epoch [98] END|---------------------------------------> - -Epoch: 99/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.2086 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1320 - accuracy: 0.9580 - val_loss: 0.2175 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1388 - accuracy: 0.9556 - val_loss: 0.1846 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1222 - accuracy: 0.9658 - val_loss: 0.2280 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1001 - accuracy: 0.9692 - val_loss: 0.2335 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0935 - accuracy: 0.9741 - val_loss: 0.2289 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 99: 287.39 sec -Time taken for epoch(SUBo) 99: 237.52 sec -<---------------------------------------|Epoch [99] END|---------------------------------------> - -Epoch: 100/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1431 - accuracy: 0.9580 - val_loss: 0.2261 - val_accuracy: 0.9247 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1552 - accuracy: 0.9536 - val_loss: 0.1987 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1221 - accuracy: 0.9619 - val_loss: 0.2009 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9604 - val_loss: 0.2111 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1100 - accuracy: 0.9692 - val_loss: 0.2023 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0975 - accuracy: 0.9736 - val_loss: 0.1899 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 100: 287.11 sec -Time taken for epoch(SUBo) 100: 237.35 sec -<---------------------------------------|Epoch [100] END|---------------------------------------> - -Epoch: 101/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9541 - val_loss: 0.2182 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1364 - accuracy: 0.9629 - val_loss: 0.1850 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1349 - accuracy: 0.9600 - val_loss: 0.2381 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.1880 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1042 - accuracy: 0.9692 - val_loss: 0.2007 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0986 - accuracy: 0.9731 - val_loss: 0.2144 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 101: 287.74 sec -Time taken for epoch(SUBo) 101: 237.93 sec -<---------------------------------------|Epoch [101] END|---------------------------------------> - -Epoch: 102/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9570 - val_loss: 0.2415 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1164 - accuracy: 0.9653 - val_loss: 0.2319 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1270 - accuracy: 0.9658 - val_loss: 0.2692 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1342 - accuracy: 0.9629 - val_loss: 0.2067 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.1845 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1135 - accuracy: 0.9688 - val_loss: 0.2075 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 102: 288.00 sec -Time taken for epoch(SUBo) 102: 237.50 sec -<---------------------------------------|Epoch [102] END|---------------------------------------> - -Epoch: 103/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1454 - accuracy: 0.9531 - val_loss: 0.2672 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1464 - accuracy: 0.9556 - val_loss: 0.1568 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9614 - val_loss: 0.2431 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1267 - accuracy: 0.9595 - val_loss: 0.1676 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1114 - accuracy: 0.9648 - val_loss: 0.1947 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1131 - accuracy: 0.9688 - val_loss: 0.1926 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 103: 287.73 sec -Time taken for epoch(SUBo) 103: 237.64 sec -<---------------------------------------|Epoch [103] END|---------------------------------------> - -Epoch: 104/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1319 - accuracy: 0.9551 - val_loss: 0.2187 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1435 - accuracy: 0.9565 - val_loss: 0.2262 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1363 - accuracy: 0.9556 - val_loss: 0.1924 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1133 - accuracy: 0.9678 - val_loss: 0.2607 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9717 - val_loss: 0.2344 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1026 - accuracy: 0.9673 - val_loss: 0.2418 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 104: 286.90 sec -Time taken for epoch(SUBo) 104: 237.53 sec -<---------------------------------------|Epoch [104] END|---------------------------------------> - -Epoch: 105/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9580 - val_loss: 0.2079 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1239 - accuracy: 0.9600 - val_loss: 0.2032 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1005 - accuracy: 0.9722 - val_loss: 0.2134 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9688 - val_loss: 0.1937 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0898 - accuracy: 0.9741 - val_loss: 0.1968 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 105: 287.02 sec -Time taken for epoch(SUBo) 105: 237.52 sec -<---------------------------------------|Epoch [105] END|---------------------------------------> - -Epoch: 106/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1352 - accuracy: 0.9575 - val_loss: 0.1525 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1355 - accuracy: 0.9570 - val_loss: 0.1892 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1163 - accuracy: 0.9692 - val_loss: 0.1639 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1066 - accuracy: 0.9678 - val_loss: 0.1816 - val_accuracy: 0.9583 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0869 - accuracy: 0.9736 - val_loss: 0.1968 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0897 - accuracy: 0.9741 - val_loss: 0.2022 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9599359035491943. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 106: 287.48 sec -Time taken for epoch(SUBo) 106: 237.69 sec -<---------------------------------------|Epoch [106] END|---------------------------------------> - -Epoch: 107/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 155ms/step - loss: 0.1194 - accuracy: 0.9644 - val_loss: 0.1767 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9668 - val_loss: 0.1995 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1046 - accuracy: 0.9663 - val_loss: 0.1818 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0864 - accuracy: 0.9746 - val_loss: 0.1969 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0910 - accuracy: 0.9722 - val_loss: 0.1441 - val_accuracy: 0.9663 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9653 - val_loss: 0.1590 - val_accuracy: 0.9696 -Subset training done. -Improved model accuracy from 0.9599359035491943 to 0.9695512652397156. Saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 107: 289.43 sec -Time taken for epoch(SUBo) 107: 237.56 sec -<---------------------------------------|Epoch [107] END|---------------------------------------> - -Epoch: 108/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1730 - accuracy: 0.9492 - val_loss: 0.1516 - val_accuracy: 0.9679 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1736 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.1854 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1192 - accuracy: 0.9658 - val_loss: 0.2242 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1115 - accuracy: 0.9663 - val_loss: 0.1922 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0976 - accuracy: 0.9722 - val_loss: 0.1996 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 108: 288.48 sec -Time taken for epoch(SUBo) 108: 238.16 sec -<---------------------------------------|Epoch [108] END|---------------------------------------> - -Epoch: 109/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1546 - accuracy: 0.9526 - val_loss: 0.1503 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1529 - accuracy: 0.9551 - val_loss: 0.1752 - val_accuracy: 0.9631 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.1519 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1593 - accuracy: 0.9492 - val_loss: 0.1787 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1744 - accuracy: 0.9434 - val_loss: 0.1705 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1520 - accuracy: 0.9502 - val_loss: 0.1609 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 109: 287.98 sec -Time taken for epoch(SUBo) 109: 238.06 sec -<---------------------------------------|Epoch [109] END|---------------------------------------> - -Epoch: 110/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1470 - accuracy: 0.9482 - val_loss: 0.1651 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1690 - accuracy: 0.9443 - val_loss: 0.2425 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1394 - accuracy: 0.9561 - val_loss: 0.1863 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9619 - val_loss: 0.1728 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1037 - accuracy: 0.9653 - val_loss: 0.1770 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0962 - accuracy: 0.9712 - val_loss: 0.1774 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 110: 288.95 sec -Time taken for epoch(SUBo) 110: 238.41 sec -<---------------------------------------|Epoch [110] END|---------------------------------------> - -Epoch: 111/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1625 - accuracy: 0.9487 - val_loss: 0.1659 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1540 - accuracy: 0.9556 - val_loss: 0.1548 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1736 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1230 - accuracy: 0.9639 - val_loss: 0.2110 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1110 - accuracy: 0.9717 - val_loss: 0.1803 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1079 - accuracy: 0.9688 - val_loss: 0.1742 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 111: 288.76 sec -Time taken for epoch(SUBo) 111: 238.28 sec -<---------------------------------------|Epoch [111] END|---------------------------------------> - -Epoch: 112/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1898 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9473 - val_loss: 0.2439 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1295 - accuracy: 0.9614 - val_loss: 0.2080 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1483 - accuracy: 0.9604 - val_loss: 0.2009 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.2107 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2227 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 112: 288.69 sec -Time taken for epoch(SUBo) 112: 237.84 sec -<---------------------------------------|Epoch [112] END|---------------------------------------> - -Epoch: 113/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1289 - accuracy: 0.9604 - val_loss: 0.1870 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1315 - accuracy: 0.9619 - val_loss: 0.1862 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1271 - accuracy: 0.9604 - val_loss: 0.1778 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9707 - val_loss: 0.1887 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2135 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0856 - accuracy: 0.9741 - val_loss: 0.2159 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 113: 289.27 sec -Time taken for epoch(SUBo) 113: 237.88 sec -<---------------------------------------|Epoch [113] END|---------------------------------------> - -Epoch: 114/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 43s 155ms/step - loss: 0.1358 - accuracy: 0.9595 - val_loss: 0.1854 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9644 - val_loss: 0.2141 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2008 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9639 - val_loss: 0.1953 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1022 - accuracy: 0.9663 - val_loss: 0.1951 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0806 - accuracy: 0.9775 - val_loss: 0.1923 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 114: 288.83 sec -Time taken for epoch(SUBo) 114: 237.68 sec -<---------------------------------------|Epoch [114] END|---------------------------------------> - -Epoch: 115/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1186 - accuracy: 0.9600 - val_loss: 0.2549 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1196 - accuracy: 0.9604 - val_loss: 0.2198 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1253 - accuracy: 0.9590 - val_loss: 0.2396 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1043 - accuracy: 0.9736 - val_loss: 0.2314 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.0960 - accuracy: 0.9712 - val_loss: 0.2056 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.0915 - accuracy: 0.9722 - val_loss: 0.2126 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 115: 289.11 sec -Time taken for epoch(SUBo) 115: 238.53 sec -<---------------------------------------|Epoch [115] END|---------------------------------------> - -Epoch: 116/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2195 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1368 - accuracy: 0.9595 - val_loss: 0.1903 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 39s 151ms/step - loss: 0.1198 - accuracy: 0.9614 - val_loss: 0.2051 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1856 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1008 - accuracy: 0.9702 - val_loss: 0.1742 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1027 - accuracy: 0.9717 - val_loss: 0.1697 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 116: 289.60 sec -Time taken for epoch(SUBo) 116: 239.21 sec -<---------------------------------------|Epoch [116] END|---------------------------------------> - -Epoch: 117/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.1718 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9580 - val_loss: 0.2046 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0925 - accuracy: 0.9722 - val_loss: 0.2292 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0834 - accuracy: 0.9751 - val_loss: 0.2023 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9727 - val_loss: 0.2151 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1000 - accuracy: 0.9722 - val_loss: 0.2206 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 117: 294.65 sec -Time taken for epoch(SUBo) 117: 244.16 sec -<---------------------------------------|Epoch [117] END|---------------------------------------> - -Epoch: 118/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1199 - accuracy: 0.9644 - val_loss: 0.2294 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1139 - accuracy: 0.9663 - val_loss: 0.1655 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1037 - accuracy: 0.9707 - val_loss: 0.1589 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0889 - accuracy: 0.9741 - val_loss: 0.2250 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0840 - accuracy: 0.9785 - val_loss: 0.1895 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0828 - accuracy: 0.9727 - val_loss: 0.1852 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15118563175201416. Not saving model. -Time taken for epoch(FULL) 118: 295.73 sec -Time taken for epoch(SUBo) 118: 244.43 sec -<---------------------------------------|Epoch [118] END|---------------------------------------> - -Epoch: 119/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1416 - accuracy: 0.9585 - val_loss: 0.1226 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1682 - accuracy: 0.9434 - val_loss: 0.1301 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1486 - accuracy: 0.9497 - val_loss: 0.1562 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1247 - accuracy: 0.9604 - val_loss: 0.1408 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1476 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1120 - accuracy: 0.9629 - val_loss: 0.1468 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.15118563175201416 to 0.146798238158226. Saving model. -Time taken for epoch(FULL) 119: 296.83 sec -Time taken for epoch(SUBo) 119: 244.81 sec -<---------------------------------------|Epoch [119] END|---------------------------------------> - -Epoch: 120/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1305 - accuracy: 0.9570 - val_loss: 0.1442 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1428 - accuracy: 0.9551 - val_loss: 0.1382 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9653 - val_loss: 0.1388 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1095 - accuracy: 0.9692 - val_loss: 0.1446 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0795 - accuracy: 0.9790 - val_loss: 0.1430 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1469 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 120: 295.05 sec -Time taken for epoch(SUBo) 120: 244.35 sec -<---------------------------------------|Epoch [120] END|---------------------------------------> - -Epoch: 121/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.1539 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1415 - accuracy: 0.9541 - val_loss: 0.1573 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1153 - accuracy: 0.9717 - val_loss: 0.1778 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1108 - accuracy: 0.9683 - val_loss: 0.1774 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9697 - val_loss: 0.1738 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9727 - val_loss: 0.1716 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 121: 294.56 sec -Time taken for epoch(SUBo) 121: 244.29 sec -<---------------------------------------|Epoch [121] END|---------------------------------------> - -Epoch: 122/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1261 - accuracy: 0.9619 - val_loss: 0.1905 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9634 - val_loss: 0.1801 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1278 - accuracy: 0.9580 - val_loss: 0.2058 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9663 - val_loss: 0.2683 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1103 - accuracy: 0.9648 - val_loss: 0.1943 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 122: 295.23 sec -Time taken for epoch(SUBo) 122: 244.50 sec -<---------------------------------------|Epoch [122] END|---------------------------------------> - -Epoch: 123/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1423 - accuracy: 0.9570 - val_loss: 0.1759 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9624 - val_loss: 0.2300 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1347 - accuracy: 0.9600 - val_loss: 0.2434 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1360 - accuracy: 0.9565 - val_loss: 0.2215 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1029 - accuracy: 0.9678 - val_loss: 0.2258 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1030 - accuracy: 0.9658 - val_loss: 0.1975 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 123: 294.95 sec -Time taken for epoch(SUBo) 123: 244.32 sec -<---------------------------------------|Epoch [123] END|---------------------------------------> - -Epoch: 124/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1253 - accuracy: 0.9614 - val_loss: 0.2786 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9600 - val_loss: 0.2731 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1414 - accuracy: 0.9575 - val_loss: 0.2149 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1280 - accuracy: 0.9609 - val_loss: 0.2693 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.2356 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.2349 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 124: 294.95 sec -Time taken for epoch(SUBo) 124: 244.30 sec -<---------------------------------------|Epoch [124] END|---------------------------------------> - -Epoch: 125/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1388 - accuracy: 0.9570 - val_loss: 0.2241 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1322 - accuracy: 0.9595 - val_loss: 0.2067 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1604 - accuracy: 0.9448 - val_loss: 0.2070 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1206 - accuracy: 0.9629 - val_loss: 0.1951 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1370 - accuracy: 0.9556 - val_loss: 0.1795 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9614 - val_loss: 0.1803 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 125: 296.66 sec -Time taken for epoch(SUBo) 125: 245.25 sec -<---------------------------------------|Epoch [125] END|---------------------------------------> - -Epoch: 126/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9443 - val_loss: 0.1636 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1469 - accuracy: 0.9531 - val_loss: 0.1743 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9600 - val_loss: 0.2001 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1122 - accuracy: 0.9634 - val_loss: 0.2148 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9692 - val_loss: 0.1990 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0975 - accuracy: 0.9727 - val_loss: 0.1967 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 126: 296.05 sec -Time taken for epoch(SUBo) 126: 244.69 sec -<---------------------------------------|Epoch [126] END|---------------------------------------> - -Epoch: 127/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1350 - accuracy: 0.9590 - val_loss: 0.2002 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9604 - val_loss: 0.1730 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1136 - accuracy: 0.9658 - val_loss: 0.2452 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0970 - accuracy: 0.9756 - val_loss: 0.2381 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0872 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0813 - accuracy: 0.9761 - val_loss: 0.2530 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 127: 295.58 sec -Time taken for epoch(SUBo) 127: 244.41 sec -<---------------------------------------|Epoch [127] END|---------------------------------------> - -Epoch: 128/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1365 - accuracy: 0.9521 - val_loss: 0.1995 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1957 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1184 - accuracy: 0.9609 - val_loss: 0.1864 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1086 - accuracy: 0.9712 - val_loss: 0.2123 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1137 - accuracy: 0.9653 - val_loss: 0.1765 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1008 - accuracy: 0.9697 - val_loss: 0.1619 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 128: 303.71 sec -Time taken for epoch(SUBo) 128: 244.02 sec -<---------------------------------------|Epoch [128] END|---------------------------------------> - -Epoch: 129/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1492 - accuracy: 0.9492 - val_loss: 0.1890 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1478 - accuracy: 0.9565 - val_loss: 0.1770 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1285 - accuracy: 0.9609 - val_loss: 0.1963 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1629 - val_accuracy: 0.9599 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1027 - accuracy: 0.9722 - val_loss: 0.1720 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0962 - accuracy: 0.9722 - val_loss: 0.1728 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 129: 304.31 sec -Time taken for epoch(SUBo) 129: 243.77 sec -<---------------------------------------|Epoch [129] END|---------------------------------------> - -Epoch: 130/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1344 - accuracy: 0.9595 - val_loss: 0.1606 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1276 - accuracy: 0.9624 - val_loss: 0.1791 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.1730 - val_accuracy: 0.9615 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1088 - accuracy: 0.9683 - val_loss: 0.1984 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1004 - accuracy: 0.9668 - val_loss: 0.2138 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1041 - accuracy: 0.9683 - val_loss: 0.1963 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 130: 301.26 sec -Time taken for epoch(SUBo) 130: 244.07 sec -<---------------------------------------|Epoch [130] END|---------------------------------------> - -Epoch: 131/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1314 - accuracy: 0.9614 - val_loss: 0.1733 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9556 - val_loss: 0.1815 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9639 - val_loss: 0.1522 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1197 - accuracy: 0.9644 - val_loss: 0.1593 - val_accuracy: 0.9615 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1065 - accuracy: 0.9707 - val_loss: 0.1619 - val_accuracy: 0.9615 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0984 - accuracy: 0.9697 - val_loss: 0.1596 - val_accuracy: 0.9631 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 131: 300.73 sec -Time taken for epoch(SUBo) 131: 244.44 sec -<---------------------------------------|Epoch [131] END|---------------------------------------> - -Epoch: 132/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1359 - accuracy: 0.9590 - val_loss: 0.1611 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1136 - accuracy: 0.9644 - val_loss: 0.1692 - val_accuracy: 0.9615 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1270 - accuracy: 0.9629 - val_loss: 0.2881 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1380 - accuracy: 0.9609 - val_loss: 0.1959 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1193 - accuracy: 0.9658 - val_loss: 0.2176 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1125 - accuracy: 0.9648 - val_loss: 0.2147 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 132: 298.91 sec -Time taken for epoch(SUBo) 132: 243.60 sec -<---------------------------------------|Epoch [132] END|---------------------------------------> - -Epoch: 133/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1469 - accuracy: 0.9521 - val_loss: 0.2294 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1442 - accuracy: 0.9580 - val_loss: 0.2275 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1246 - accuracy: 0.9619 - val_loss: 0.2881 - val_accuracy: 0.9295 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1150 - accuracy: 0.9673 - val_loss: 0.2647 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1132 - accuracy: 0.9648 - val_loss: 0.2474 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0897 - accuracy: 0.9751 - val_loss: 0.2609 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 133: 296.74 sec -Time taken for epoch(SUBo) 133: 242.77 sec -<---------------------------------------|Epoch [133] END|---------------------------------------> - -Epoch: 134/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1280 - accuracy: 0.9604 - val_loss: 0.2374 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1308 - accuracy: 0.9590 - val_loss: 0.2543 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1377 - accuracy: 0.9565 - val_loss: 0.2752 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1032 - accuracy: 0.9736 - val_loss: 0.2675 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9663 - val_loss: 0.2584 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0954 - accuracy: 0.9756 - val_loss: 0.2853 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 134: 297.41 sec -Time taken for epoch(SUBo) 134: 243.12 sec -<---------------------------------------|Epoch [134] END|---------------------------------------> - -Epoch: 135/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1629 - accuracy: 0.9482 - val_loss: 0.2191 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1362 - accuracy: 0.9575 - val_loss: 0.2275 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1400 - accuracy: 0.9570 - val_loss: 0.1914 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1302 - accuracy: 0.9639 - val_loss: 0.1995 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9653 - val_loss: 0.2003 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1085 - accuracy: 0.9697 - val_loss: 0.2064 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 135: 298.46 sec -Time taken for epoch(SUBo) 135: 243.19 sec -<---------------------------------------|Epoch [135] END|---------------------------------------> - -Epoch: 136/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1415 - accuracy: 0.9561 - val_loss: 0.1941 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1323 - accuracy: 0.9648 - val_loss: 0.2252 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.1982 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2166 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9678 - val_loss: 0.2508 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0991 - accuracy: 0.9707 - val_loss: 0.2181 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 136: 300.20 sec -Time taken for epoch(SUBo) 136: 243.49 sec -<---------------------------------------|Epoch [136] END|---------------------------------------> - -Epoch: 137/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1551 - accuracy: 0.9531 - val_loss: 0.2049 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1405 - accuracy: 0.9546 - val_loss: 0.2349 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1254 - accuracy: 0.9595 - val_loss: 0.1758 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1130 - accuracy: 0.9634 - val_loss: 0.2124 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0963 - accuracy: 0.9736 - val_loss: 0.1902 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.1870 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 137: 300.02 sec -Time taken for epoch(SUBo) 137: 243.90 sec -<---------------------------------------|Epoch [137] END|---------------------------------------> - -Epoch: 138/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1243 - accuracy: 0.9644 - val_loss: 0.1907 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1289 - accuracy: 0.9590 - val_loss: 0.1533 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1203 - accuracy: 0.9604 - val_loss: 0.1708 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.1635 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0951 - accuracy: 0.9736 - val_loss: 0.1628 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0872 - accuracy: 0.9756 - val_loss: 0.1781 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 138: 298.57 sec -Time taken for epoch(SUBo) 138: 243.89 sec -<---------------------------------------|Epoch [138] END|---------------------------------------> - -Epoch: 139/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1322 - accuracy: 0.9629 - val_loss: 0.1652 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1569 - accuracy: 0.9458 - val_loss: 0.2143 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2487 - val_accuracy: 0.9231 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1343 - accuracy: 0.9585 - val_loss: 0.1756 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1018 - accuracy: 0.9678 - val_loss: 0.1879 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0864 - accuracy: 0.9751 - val_loss: 0.2002 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 139: 296.96 sec -Time taken for epoch(SUBo) 139: 243.53 sec -<---------------------------------------|Epoch [139] END|---------------------------------------> - -Epoch: 140/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1588 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9595 - val_loss: 0.1786 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1241 - accuracy: 0.9619 - val_loss: 0.1725 - val_accuracy: 0.9599 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1104 - accuracy: 0.9683 - val_loss: 0.1877 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1057 - accuracy: 0.9702 - val_loss: 0.1923 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1891 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 140: 298.07 sec -Time taken for epoch(SUBo) 140: 243.40 sec -<---------------------------------------|Epoch [140] END|---------------------------------------> - -Epoch: 141/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1314 - accuracy: 0.9541 - val_loss: 0.1613 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.1692 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1292 - accuracy: 0.9580 - val_loss: 0.1645 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9673 - val_loss: 0.1783 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0957 - accuracy: 0.9727 - val_loss: 0.1860 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1725 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 141: 298.52 sec -Time taken for epoch(SUBo) 141: 243.77 sec -<---------------------------------------|Epoch [141] END|---------------------------------------> - -Epoch: 142/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.1811 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1378 - accuracy: 0.9536 - val_loss: 0.1458 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1216 - accuracy: 0.9614 - val_loss: 0.1723 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1112 - accuracy: 0.9683 - val_loss: 0.1895 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1075 - accuracy: 0.9707 - val_loss: 0.1709 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1590 - val_accuracy: 0.9599 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 142: 297.84 sec -Time taken for epoch(SUBo) 142: 243.24 sec -<---------------------------------------|Epoch [142] END|---------------------------------------> - -Epoch: 143/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 159ms/step - loss: 0.1446 - accuracy: 0.9512 - val_loss: 0.1575 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9600 - val_loss: 0.1438 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1499 - accuracy: 0.9556 - val_loss: 0.1531 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1312 - accuracy: 0.9575 - val_loss: 0.1520 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9629 - val_loss: 0.1651 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1007 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 143: 296.59 sec -Time taken for epoch(SUBo) 143: 243.29 sec -<---------------------------------------|Epoch [143] END|---------------------------------------> - -Epoch: 144/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 158ms/step - loss: 0.1502 - accuracy: 0.9531 - val_loss: 0.1520 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1484 - accuracy: 0.9536 - val_loss: 0.1554 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1475 - accuracy: 0.9575 - val_loss: 0.1452 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1296 - accuracy: 0.9624 - val_loss: 0.1943 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1104 - accuracy: 0.9648 - val_loss: 0.1803 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0984 - accuracy: 0.9736 - val_loss: 0.1858 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 144: 296.73 sec -Time taken for epoch(SUBo) 144: 242.88 sec -<---------------------------------------|Epoch [144] END|---------------------------------------> - -Epoch: 145/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1262 - accuracy: 0.9600 - val_loss: 0.1634 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1191 - accuracy: 0.9639 - val_loss: 0.1680 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1056 - accuracy: 0.9658 - val_loss: 0.1970 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1031 - accuracy: 0.9707 - val_loss: 0.2054 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0822 - accuracy: 0.9800 - val_loss: 0.2039 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0879 - accuracy: 0.9746 - val_loss: 0.2102 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 145: 298.13 sec -Time taken for epoch(SUBo) 145: 242.21 sec -<---------------------------------------|Epoch [145] END|---------------------------------------> - -Epoch: 146/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1362 - accuracy: 0.9570 - val_loss: 0.1822 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1300 - accuracy: 0.9595 - val_loss: 0.2085 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1156 - accuracy: 0.9629 - val_loss: 0.2197 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0958 - accuracy: 0.9761 - val_loss: 0.2403 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.2088 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0887 - accuracy: 0.9702 - val_loss: 0.2360 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 146: 301.03 sec -Time taken for epoch(SUBo) 146: 242.33 sec -<---------------------------------------|Epoch [146] END|---------------------------------------> - -Epoch: 147/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1234 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9614 - val_loss: 0.1836 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1030 - accuracy: 0.9717 - val_loss: 0.1736 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0980 - accuracy: 0.9707 - val_loss: 0.1931 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0948 - accuracy: 0.9722 - val_loss: 0.1875 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1813 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 147: 303.25 sec -Time taken for epoch(SUBo) 147: 242.94 sec -<---------------------------------------|Epoch [147] END|---------------------------------------> - -Epoch: 148/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1321 - accuracy: 0.9565 - val_loss: 0.2085 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1171 - accuracy: 0.9629 - val_loss: 0.1716 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1633 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1642 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1000 - accuracy: 0.9702 - val_loss: 0.1597 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0804 - accuracy: 0.9756 - val_loss: 0.1575 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 148: 301.98 sec -Time taken for epoch(SUBo) 148: 243.14 sec -<---------------------------------------|Epoch [148] END|---------------------------------------> - -Epoch: 149/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1178 - accuracy: 0.9634 - val_loss: 0.1412 - val_accuracy: 0.9615 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1271 - accuracy: 0.9580 - val_loss: 0.1553 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1074 - accuracy: 0.9658 - val_loss: 0.1972 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0920 - accuracy: 0.9741 - val_loss: 0.1781 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1054 - accuracy: 0.9692 - val_loss: 0.1791 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0850 - accuracy: 0.9761 - val_loss: 0.1786 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 149: 298.73 sec -Time taken for epoch(SUBo) 149: 242.85 sec -<---------------------------------------|Epoch [149] END|---------------------------------------> - -Epoch: 150/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1315 - accuracy: 0.9580 - val_loss: 0.1966 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1324 - accuracy: 0.9551 - val_loss: 0.2153 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1131 - accuracy: 0.9634 - val_loss: 0.2608 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1028 - accuracy: 0.9697 - val_loss: 0.2539 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0900 - accuracy: 0.9707 - val_loss: 0.2782 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1002 - accuracy: 0.9697 - val_loss: 0.2693 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 150: 300.25 sec -Time taken for epoch(SUBo) 150: 243.69 sec -<---------------------------------------|Epoch [150] END|---------------------------------------> - -Epoch: 151/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2125 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1103 - accuracy: 0.9712 - val_loss: 0.2087 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1040 - accuracy: 0.9653 - val_loss: 0.2110 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0983 - accuracy: 0.9727 - val_loss: 0.1971 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1968 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0845 - accuracy: 0.9751 - val_loss: 0.2230 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 151: 298.97 sec -Time taken for epoch(SUBo) 151: 242.93 sec -<---------------------------------------|Epoch [151] END|---------------------------------------> - -Epoch: 152/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1268 - accuracy: 0.9663 - val_loss: 0.2006 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9678 - val_loss: 0.1805 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1365 - accuracy: 0.9565 - val_loss: 0.1432 - val_accuracy: 0.9631 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1488 - accuracy: 0.9517 - val_loss: 0.1688 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1657 - accuracy: 0.9458 - val_loss: 0.1674 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1403 - accuracy: 0.9561 - val_loss: 0.1698 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 152: 302.68 sec -Time taken for epoch(SUBo) 152: 243.68 sec -<---------------------------------------|Epoch [152] END|---------------------------------------> - -Epoch: 153/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1499 - accuracy: 0.9507 - val_loss: 0.1872 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1414 - accuracy: 0.9580 - val_loss: 0.1947 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1562 - accuracy: 0.9463 - val_loss: 0.2135 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9629 - val_loss: 0.1884 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9712 - val_loss: 0.2042 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9712 - val_loss: 0.1936 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 153: 299.14 sec -Time taken for epoch(SUBo) 153: 243.89 sec -<---------------------------------------|Epoch [153] END|---------------------------------------> - -Epoch: 154/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1391 - accuracy: 0.9531 - val_loss: 0.1623 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1460 - accuracy: 0.9497 - val_loss: 0.2164 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1347 - accuracy: 0.9619 - val_loss: 0.4024 - val_accuracy: 0.8686 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2569 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1417 - accuracy: 0.9546 - val_loss: 0.2886 - val_accuracy: 0.9279 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2901 - val_accuracy: 0.9263 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 154: 303.43 sec -Time taken for epoch(SUBo) 154: 244.09 sec -<---------------------------------------|Epoch [154] END|---------------------------------------> - -Epoch: 155/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1674 - accuracy: 0.9424 - val_loss: 0.2398 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1466 - accuracy: 0.9556 - val_loss: 0.2424 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1350 - accuracy: 0.9565 - val_loss: 0.2398 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1153 - accuracy: 0.9639 - val_loss: 0.2173 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9692 - val_loss: 0.2637 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2615 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 155: 299.62 sec -Time taken for epoch(SUBo) 155: 243.67 sec -<---------------------------------------|Epoch [155] END|---------------------------------------> - -Epoch: 156/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9434 - val_loss: 0.2209 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1493 - accuracy: 0.9517 - val_loss: 0.2582 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1431 - accuracy: 0.9502 - val_loss: 0.2281 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9551 - val_loss: 0.2542 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1168 - accuracy: 0.9600 - val_loss: 0.1981 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9531 - val_loss: 0.2167 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 156: 301.27 sec -Time taken for epoch(SUBo) 156: 244.46 sec -<---------------------------------------|Epoch [156] END|---------------------------------------> - -Epoch: 157/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1338 - accuracy: 0.9565 - val_loss: 0.2626 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1420 - accuracy: 0.9473 - val_loss: 0.3502 - val_accuracy: 0.9215 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1291 - accuracy: 0.9585 - val_loss: 0.2344 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1022 - accuracy: 0.9683 - val_loss: 0.2722 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1164 - accuracy: 0.9648 - val_loss: 0.2915 - val_accuracy: 0.9215 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2660 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 157: 298.53 sec -Time taken for epoch(SUBo) 157: 243.70 sec -<---------------------------------------|Epoch [157] END|---------------------------------------> - -Epoch: 158/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1569 - accuracy: 0.9517 - val_loss: 0.2548 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1227 - accuracy: 0.9609 - val_loss: 0.3033 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9575 - val_loss: 0.2928 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1117 - accuracy: 0.9663 - val_loss: 0.2713 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9717 - val_loss: 0.2921 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0927 - accuracy: 0.9741 - val_loss: 0.2760 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 158: 305.20 sec -Time taken for epoch(SUBo) 158: 244.85 sec -<---------------------------------------|Epoch [158] END|---------------------------------------> - -Epoch: 159/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1135 - accuracy: 0.9668 - val_loss: 0.2714 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1001 - accuracy: 0.9663 - val_loss: 0.3513 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0937 - accuracy: 0.9712 - val_loss: 0.2725 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0861 - accuracy: 0.9780 - val_loss: 0.2921 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0836 - accuracy: 0.9751 - val_loss: 0.2788 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0809 - accuracy: 0.9780 - val_loss: 0.2651 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 159: 306.51 sec -Time taken for epoch(SUBo) 159: 245.04 sec -<---------------------------------------|Epoch [159] END|---------------------------------------> - -Epoch: 160/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1241 - accuracy: 0.9609 - val_loss: 0.2724 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9570 - val_loss: 0.2510 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1102 - accuracy: 0.9653 - val_loss: 0.2081 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1101 - accuracy: 0.9702 - val_loss: 0.1942 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.2166 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0885 - accuracy: 0.9727 - val_loss: 0.2052 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 160: 305.10 sec -Time taken for epoch(SUBo) 160: 245.83 sec -<---------------------------------------|Epoch [160] END|---------------------------------------> - -Epoch: 161/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1290 - accuracy: 0.9614 - val_loss: 0.1891 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9575 - val_loss: 0.1965 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2083 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1031 - accuracy: 0.9678 - val_loss: 0.2418 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1070 - accuracy: 0.9678 - val_loss: 0.2420 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0859 - accuracy: 0.9761 - val_loss: 0.2691 - val_accuracy: 0.9247 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 161: 299.90 sec -Time taken for epoch(SUBo) 161: 244.85 sec -<---------------------------------------|Epoch [161] END|---------------------------------------> - -Epoch: 162/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1228 - accuracy: 0.9629 - val_loss: 0.2065 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1999 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1606 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1366 - accuracy: 0.9575 - val_loss: 0.2026 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9619 - val_loss: 0.2040 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1096 - accuracy: 0.9673 - val_loss: 0.2063 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 162: 299.56 sec -Time taken for epoch(SUBo) 162: 244.33 sec -<---------------------------------------|Epoch [162] END|---------------------------------------> - -Epoch: 163/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1399 - accuracy: 0.9565 - val_loss: 0.2292 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1215 - accuracy: 0.9585 - val_loss: 0.2450 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1078 - accuracy: 0.9648 - val_loss: 0.2188 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1154 - accuracy: 0.9648 - val_loss: 0.2537 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9619 - val_loss: 0.2278 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9634 - val_loss: 0.2206 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 163: 297.49 sec -Time taken for epoch(SUBo) 163: 243.39 sec -<---------------------------------------|Epoch [163] END|---------------------------------------> - -Epoch: 164/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1580 - accuracy: 0.9507 - val_loss: 0.2399 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9570 - val_loss: 0.2307 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1342 - accuracy: 0.9604 - val_loss: 0.1897 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.2260 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9668 - val_loss: 0.2024 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0980 - accuracy: 0.9673 - val_loss: 0.2013 - val_accuracy: 0.9551 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 164: 300.36 sec -Time taken for epoch(SUBo) 164: 244.25 sec -<---------------------------------------|Epoch [164] END|---------------------------------------> - -Epoch: 165/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1589 - accuracy: 0.9497 - val_loss: 0.1661 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9575 - val_loss: 0.2048 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1393 - accuracy: 0.9600 - val_loss: 0.1941 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.2079 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1092 - accuracy: 0.9688 - val_loss: 0.2288 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9761 - val_loss: 0.2080 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 165: 307.38 sec -Time taken for epoch(SUBo) 165: 245.49 sec -<---------------------------------------|Epoch [165] END|---------------------------------------> - -Epoch: 166/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1276 - accuracy: 0.9585 - val_loss: 0.2018 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1107 - accuracy: 0.9673 - val_loss: 0.1818 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1782 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9731 - val_loss: 0.1845 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0775 - accuracy: 0.9756 - val_loss: 0.1787 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 166: 306.37 sec -Time taken for epoch(SUBo) 166: 244.99 sec -<---------------------------------------|Epoch [166] END|---------------------------------------> - -Epoch: 167/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 162ms/step - loss: 0.1360 - accuracy: 0.9585 - val_loss: 0.1928 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.1949 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1286 - accuracy: 0.9600 - val_loss: 0.2223 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1548 - accuracy: 0.9487 - val_loss: 0.3237 - val_accuracy: 0.9199 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1733 - accuracy: 0.9395 - val_loss: 0.2911 - val_accuracy: 0.9135 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9565 - val_loss: 0.2720 - val_accuracy: 0.9231 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 167: 302.14 sec -Time taken for epoch(SUBo) 167: 244.91 sec -<---------------------------------------|Epoch [167] END|---------------------------------------> - -Epoch: 168/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1783 - accuracy: 0.9365 - val_loss: 0.3662 - val_accuracy: 0.9006 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1679 - accuracy: 0.9419 - val_loss: 0.2450 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1442 - accuracy: 0.9512 - val_loss: 0.2916 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1321 - accuracy: 0.9575 - val_loss: 0.3255 - val_accuracy: 0.9231 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1195 - accuracy: 0.9624 - val_loss: 0.3551 - val_accuracy: 0.9199 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1084 - accuracy: 0.9668 - val_loss: 0.3794 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 168: 299.21 sec -Time taken for epoch(SUBo) 168: 243.84 sec -<---------------------------------------|Epoch [168] END|---------------------------------------> - -Epoch: 169/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1427 - accuracy: 0.9624 - val_loss: 0.2396 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1854 - accuracy: 0.9336 - val_loss: 0.2213 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1539 - accuracy: 0.9458 - val_loss: 0.2068 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1354 - accuracy: 0.9585 - val_loss: 0.3011 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1135 - accuracy: 0.9629 - val_loss: 0.2591 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1127 - accuracy: 0.9629 - val_loss: 0.2691 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 169: 300.15 sec -Time taken for epoch(SUBo) 169: 243.82 sec -<---------------------------------------|Epoch [169] END|---------------------------------------> - -Epoch: 170/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1595 - accuracy: 0.9438 - val_loss: 0.2370 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1465 - accuracy: 0.9492 - val_loss: 0.1867 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1322 - accuracy: 0.9565 - val_loss: 0.2246 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1295 - accuracy: 0.9609 - val_loss: 0.2039 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1179 - accuracy: 0.9644 - val_loss: 0.1999 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2048 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 170: 299.63 sec -Time taken for epoch(SUBo) 170: 242.91 sec -<---------------------------------------|Epoch [170] END|---------------------------------------> - -Epoch: 171/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1650 - accuracy: 0.9541 - val_loss: 0.1615 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9565 - val_loss: 0.1734 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1397 - accuracy: 0.9570 - val_loss: 0.1680 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1035 - accuracy: 0.9741 - val_loss: 0.1722 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1021 - accuracy: 0.9668 - val_loss: 0.1847 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.1761 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 171: 304.06 sec -Time taken for epoch(SUBo) 171: 243.87 sec -<---------------------------------------|Epoch [171] END|---------------------------------------> - -Epoch: 172/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1201 - accuracy: 0.9629 - val_loss: 0.1739 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9614 - val_loss: 0.1925 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1138 - accuracy: 0.9673 - val_loss: 0.2372 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1000 - accuracy: 0.9697 - val_loss: 0.1883 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0899 - accuracy: 0.9731 - val_loss: 0.2044 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0740 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 172: 304.33 sec -Time taken for epoch(SUBo) 172: 243.65 sec -<---------------------------------------|Epoch [172] END|---------------------------------------> - -Epoch: 173/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1373 - accuracy: 0.9541 - val_loss: 0.1948 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1472 - accuracy: 0.9502 - val_loss: 0.2673 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1669 - accuracy: 0.9453 - val_loss: 0.1954 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.1729 - val_accuracy: 0.9519 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9629 - val_loss: 0.2251 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1095 - accuracy: 0.9658 - val_loss: 0.2223 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 173: 302.38 sec -Time taken for epoch(SUBo) 173: 244.10 sec -<---------------------------------------|Epoch [173] END|---------------------------------------> - -Epoch: 174/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.2098 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1407 - accuracy: 0.9561 - val_loss: 0.2066 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1279 - accuracy: 0.9609 - val_loss: 0.2408 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1170 - accuracy: 0.9629 - val_loss: 0.2116 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.2266 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0989 - accuracy: 0.9722 - val_loss: 0.2566 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 174: 298.38 sec -Time taken for epoch(SUBo) 174: 242.96 sec -<---------------------------------------|Epoch [174] END|---------------------------------------> - -Epoch: 175/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1366 - accuracy: 0.9546 - val_loss: 0.2196 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.1153 - accuracy: 0.9619 - val_loss: 0.2363 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1186 - accuracy: 0.9624 - val_loss: 0.2094 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9683 - val_loss: 0.2792 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0901 - accuracy: 0.9736 - val_loss: 0.2793 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0818 - accuracy: 0.9751 - val_loss: 0.3102 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 175: 298.34 sec -Time taken for epoch(SUBo) 175: 243.27 sec -<---------------------------------------|Epoch [175] END|---------------------------------------> - -Epoch: 176/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1217 - accuracy: 0.9561 - val_loss: 0.3390 - val_accuracy: 0.8894 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1363 - accuracy: 0.9600 - val_loss: 0.3365 - val_accuracy: 0.9151 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9580 - val_loss: 0.2768 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.1262 - accuracy: 0.9629 - val_loss: 0.2921 - val_accuracy: 0.9135 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0952 - accuracy: 0.9717 - val_loss: 0.3173 - val_accuracy: 0.9151 -Epoch 6/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0972 - accuracy: 0.9731 - val_loss: 0.3247 - val_accuracy: 0.9135 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 176: 300.75 sec -Time taken for epoch(SUBo) 176: 244.46 sec -<---------------------------------------|Epoch [176] END|---------------------------------------> - -Epoch: 177/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1301 - accuracy: 0.9600 - val_loss: 0.2746 - val_accuracy: 0.9215 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1191 - accuracy: 0.9658 - val_loss: 0.2657 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1160 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0987 - accuracy: 0.9722 - val_loss: 0.2429 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0863 - accuracy: 0.9756 - val_loss: 0.2320 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0852 - accuracy: 0.9771 - val_loss: 0.2548 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 177: 307.13 sec -Time taken for epoch(SUBo) 177: 245.28 sec -<---------------------------------------|Epoch [177] END|---------------------------------------> - -Epoch: 178/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1285 - accuracy: 0.9634 - val_loss: 0.1938 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1361 - accuracy: 0.9551 - val_loss: 0.2198 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1310 - accuracy: 0.9614 - val_loss: 0.2257 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1178 - accuracy: 0.9658 - val_loss: 0.1883 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1097 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9697 - val_loss: 0.2949 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 178: 307.31 sec -Time taken for epoch(SUBo) 178: 246.17 sec -<---------------------------------------|Epoch [178] END|---------------------------------------> - -Epoch: 179/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1366 - accuracy: 0.9551 - val_loss: 0.2232 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1883 - accuracy: 0.9370 - val_loss: 0.2155 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1590 - accuracy: 0.9492 - val_loss: 0.2392 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1456 - accuracy: 0.9517 - val_loss: 0.2673 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2418 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1098 - accuracy: 0.9658 - val_loss: 0.2398 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 179: 304.66 sec -Time taken for epoch(SUBo) 179: 246.00 sec -<---------------------------------------|Epoch [179] END|---------------------------------------> - -Epoch: 180/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1470 - accuracy: 0.9546 - val_loss: 0.2427 - val_accuracy: 0.9231 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1592 - accuracy: 0.9521 - val_loss: 0.3052 - val_accuracy: 0.9103 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1297 - accuracy: 0.9629 - val_loss: 0.2849 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9551 - val_loss: 0.2115 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1155 - accuracy: 0.9644 - val_loss: 0.2489 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1184 - accuracy: 0.9648 - val_loss: 0.2458 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 180: 303.24 sec -Time taken for epoch(SUBo) 180: 245.01 sec -<---------------------------------------|Epoch [180] END|---------------------------------------> - -Epoch: 181/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1431 - accuracy: 0.9556 - val_loss: 0.2670 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1354 - accuracy: 0.9580 - val_loss: 0.3152 - val_accuracy: 0.9071 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.2952 - val_accuracy: 0.9054 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.3917 - val_accuracy: 0.8958 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0896 - accuracy: 0.9756 - val_loss: 0.3502 - val_accuracy: 0.8990 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0898 - accuracy: 0.9707 - val_loss: 0.3361 - val_accuracy: 0.9071 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 181: 302.34 sec -Time taken for epoch(SUBo) 181: 244.62 sec -<---------------------------------------|Epoch [181] END|---------------------------------------> - -Epoch: 182/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2772 - val_accuracy: 0.9247 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1336 - accuracy: 0.9600 - val_loss: 0.2250 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9644 - val_loss: 0.3103 - val_accuracy: 0.9135 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9731 - val_loss: 0.3044 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0906 - accuracy: 0.9702 - val_loss: 0.3051 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0863 - accuracy: 0.9731 - val_loss: 0.3318 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 182: 304.09 sec -Time taken for epoch(SUBo) 182: 245.03 sec -<---------------------------------------|Epoch [182] END|---------------------------------------> - -Epoch: 183/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1180 - accuracy: 0.9609 - val_loss: 0.3431 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2777 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1235 - accuracy: 0.9634 - val_loss: 0.1881 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0921 - accuracy: 0.9717 - val_loss: 0.2754 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9712 - val_loss: 0.3383 - val_accuracy: 0.9103 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0866 - accuracy: 0.9751 - val_loss: 0.3123 - val_accuracy: 0.9215 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 183: 304.60 sec -Time taken for epoch(SUBo) 183: 244.97 sec -<---------------------------------------|Epoch [183] END|---------------------------------------> - -Epoch: 184/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1436 - accuracy: 0.9565 - val_loss: 0.2403 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9575 - val_loss: 0.2531 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1325 - accuracy: 0.9531 - val_loss: 0.3488 - val_accuracy: 0.9215 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.2155 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2753 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1108 - accuracy: 0.9644 - val_loss: 0.2761 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 184: 304.30 sec -Time taken for epoch(SUBo) 184: 244.89 sec -<---------------------------------------|Epoch [184] END|---------------------------------------> - -Epoch: 185/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 160ms/step - loss: 0.1250 - accuracy: 0.9619 - val_loss: 0.2633 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.2972 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1252 - accuracy: 0.9639 - val_loss: 0.2754 - val_accuracy: 0.9263 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1152 - accuracy: 0.9683 - val_loss: 0.2419 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.2478 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0871 - accuracy: 0.9736 - val_loss: 0.2475 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 185: 306.52 sec -Time taken for epoch(SUBo) 185: 245.42 sec -<---------------------------------------|Epoch [185] END|---------------------------------------> - -Epoch: 186/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1323 - accuracy: 0.9585 - val_loss: 0.2456 - val_accuracy: 0.9295 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1374 - accuracy: 0.9639 - val_loss: 0.2509 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1351 - accuracy: 0.9639 - val_loss: 0.2669 - val_accuracy: 0.9311 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1114 - accuracy: 0.9639 - val_loss: 0.2947 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0944 - accuracy: 0.9766 - val_loss: 0.2886 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0906 - accuracy: 0.9736 - val_loss: 0.2739 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 186: 307.26 sec -Time taken for epoch(SUBo) 186: 246.41 sec -<---------------------------------------|Epoch [186] END|---------------------------------------> - -Epoch: 187/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1226 - accuracy: 0.9644 - val_loss: 0.2625 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1695 - accuracy: 0.9453 - val_loss: 1.2514 - val_accuracy: 0.7115 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1965 - accuracy: 0.9336 - val_loss: 0.5935 - val_accuracy: 0.8429 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1654 - accuracy: 0.9458 - val_loss: 0.4132 - val_accuracy: 0.9054 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9551 - val_loss: 0.4170 - val_accuracy: 0.9038 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1316 - accuracy: 0.9595 - val_loss: 0.4311 - val_accuracy: 0.9022 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 187: 297.48 sec -Time taken for epoch(SUBo) 187: 244.18 sec -<---------------------------------------|Epoch [187] END|---------------------------------------> - -Epoch: 188/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1581 - accuracy: 0.9458 - val_loss: 0.3557 - val_accuracy: 0.9087 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1425 - accuracy: 0.9561 - val_loss: 0.3358 - val_accuracy: 0.9199 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1316 - accuracy: 0.9551 - val_loss: 0.3622 - val_accuracy: 0.9231 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1155 - accuracy: 0.9634 - val_loss: 0.3811 - val_accuracy: 0.9119 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1323 - accuracy: 0.9546 - val_loss: 0.3472 - val_accuracy: 0.9167 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1224 - accuracy: 0.9644 - val_loss: 0.3330 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 188: 299.49 sec -Time taken for epoch(SUBo) 188: 244.91 sec -<---------------------------------------|Epoch [188] END|---------------------------------------> - -Epoch: 189/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1436 - accuracy: 0.9517 - val_loss: 0.2752 - val_accuracy: 0.9279 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1421 - accuracy: 0.9531 - val_loss: 0.2516 - val_accuracy: 0.9263 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1263 - accuracy: 0.9600 - val_loss: 0.2514 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1058 - accuracy: 0.9663 - val_loss: 0.2660 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1131 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9311 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 189: 301.75 sec -Time taken for epoch(SUBo) 189: 245.44 sec -<---------------------------------------|Epoch [189] END|---------------------------------------> - -Epoch: 190/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1480 - accuracy: 0.9570 - val_loss: 0.1996 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1882 - accuracy: 0.9380 - val_loss: 0.2167 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1597 - accuracy: 0.9512 - val_loss: 0.2156 - val_accuracy: 0.9247 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1344 - accuracy: 0.9590 - val_loss: 0.2198 - val_accuracy: 0.9295 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1345 - accuracy: 0.9609 - val_loss: 0.2668 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1128 - accuracy: 0.9644 - val_loss: 0.2396 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 190: 300.24 sec -Time taken for epoch(SUBo) 190: 245.01 sec -<---------------------------------------|Epoch [190] END|---------------------------------------> - -Epoch: 191/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1471 - accuracy: 0.9570 - val_loss: 0.2358 - val_accuracy: 0.9279 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1378 - accuracy: 0.9551 - val_loss: 0.2055 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.1978 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1355 - accuracy: 0.9595 - val_loss: 0.1849 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1076 - accuracy: 0.9727 - val_loss: 0.2088 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1103 - accuracy: 0.9663 - val_loss: 0.1988 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 191: 301.30 sec -Time taken for epoch(SUBo) 191: 245.49 sec -<---------------------------------------|Epoch [191] END|---------------------------------------> - -Epoch: 192/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 161ms/step - loss: 0.1421 - accuracy: 0.9575 - val_loss: 0.2050 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.3539 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1398 - accuracy: 0.9551 - val_loss: 0.2728 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1200 - accuracy: 0.9653 - val_loss: 0.2649 - val_accuracy: 0.9103 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9604 - val_loss: 0.2364 - val_accuracy: 0.9247 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1229 - accuracy: 0.9580 - val_loss: 0.2355 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 192: 300.20 sec -Time taken for epoch(SUBo) 192: 245.83 sec -<---------------------------------------|Epoch [192] END|---------------------------------------> - -Epoch: 193/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1544 - accuracy: 0.9478 - val_loss: 0.2400 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1478 - accuracy: 0.9507 - val_loss: 0.2931 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.2789 - val_accuracy: 0.9327 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9585 - val_loss: 0.2220 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1067 - accuracy: 0.9712 - val_loss: 0.2248 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0902 - accuracy: 0.9751 - val_loss: 0.2198 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 193: 298.24 sec -Time taken for epoch(SUBo) 193: 245.22 sec -<---------------------------------------|Epoch [193] END|---------------------------------------> - -Epoch: 194/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.2151 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1429 - accuracy: 0.9595 - val_loss: 0.2100 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.2180 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1083 - accuracy: 0.9683 - val_loss: 0.2342 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1105 - accuracy: 0.9683 - val_loss: 0.2624 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0829 - accuracy: 0.9741 - val_loss: 0.2530 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 194: 299.52 sec -Time taken for epoch(SUBo) 194: 245.69 sec -<---------------------------------------|Epoch [194] END|---------------------------------------> - -Epoch: 195/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 159ms/step - loss: 0.1158 - accuracy: 0.9673 - val_loss: 0.2753 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.1058 - accuracy: 0.9648 - val_loss: 0.2734 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0861 - accuracy: 0.9756 - val_loss: 0.2831 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0798 - accuracy: 0.9775 - val_loss: 0.2666 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0687 - accuracy: 0.9824 - val_loss: 0.3035 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 195: 299.74 sec -Time taken for epoch(SUBo) 195: 244.18 sec -<---------------------------------------|Epoch [195] END|---------------------------------------> - -Epoch: 196/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1189 - accuracy: 0.9604 - val_loss: 0.2907 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1172 - accuracy: 0.9658 - val_loss: 0.2743 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1052 - accuracy: 0.9663 - val_loss: 0.2412 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1047 - accuracy: 0.9653 - val_loss: 0.4034 - val_accuracy: 0.9006 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1205 - accuracy: 0.9580 - val_loss: 0.3797 - val_accuracy: 0.9199 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1042 - accuracy: 0.9678 - val_loss: 0.3400 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 196: 300.20 sec -Time taken for epoch(SUBo) 196: 246.37 sec -<---------------------------------------|Epoch [196] END|---------------------------------------> - -Epoch: 197/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1318 - accuracy: 0.9595 - val_loss: 0.2433 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1237 - accuracy: 0.9624 - val_loss: 0.2380 - val_accuracy: 0.9311 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9600 - val_loss: 0.2326 - val_accuracy: 0.9279 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2398 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0943 - accuracy: 0.9751 - val_loss: 0.2242 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9736 - val_loss: 0.2357 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 197: 297.68 sec -Time taken for epoch(SUBo) 197: 246.24 sec -<---------------------------------------|Epoch [197] END|---------------------------------------> - -Epoch: 198/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1174 - accuracy: 0.9658 - val_loss: 0.2696 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9575 - val_loss: 0.2424 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0977 - accuracy: 0.9707 - val_loss: 0.2852 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0980 - accuracy: 0.9688 - val_loss: 0.2780 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0881 - accuracy: 0.9736 - val_loss: 0.2471 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0809 - accuracy: 0.9751 - val_loss: 0.2606 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 198: 297.77 sec -Time taken for epoch(SUBo) 198: 246.78 sec -<---------------------------------------|Epoch [198] END|---------------------------------------> - -Epoch: 199/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1334 - accuracy: 0.9609 - val_loss: 0.2220 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1240 - accuracy: 0.9604 - val_loss: 0.2392 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1112 - accuracy: 0.9658 - val_loss: 0.2233 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1175 - accuracy: 0.9673 - val_loss: 0.2212 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1032 - accuracy: 0.9678 - val_loss: 0.2742 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1011 - accuracy: 0.9663 - val_loss: 0.2787 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 199: 298.50 sec -Time taken for epoch(SUBo) 199: 246.76 sec -<---------------------------------------|Epoch [199] END|---------------------------------------> - -Epoch: 200/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1285 - accuracy: 0.9580 - val_loss: 0.3062 - val_accuracy: 0.9103 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1324 - accuracy: 0.9570 - val_loss: 0.2178 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1266 - accuracy: 0.9624 - val_loss: 0.2289 - val_accuracy: 0.9327 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1193 - accuracy: 0.9565 - val_loss: 0.2471 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1040 - accuracy: 0.9673 - val_loss: 0.2422 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0873 - accuracy: 0.9741 - val_loss: 0.2505 - val_accuracy: 0.9311 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 200: 298.67 sec -Time taken for epoch(SUBo) 200: 246.74 sec -<---------------------------------------|Epoch [200] END|---------------------------------------> - -Epoch: 201/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1427 - accuracy: 0.9551 - val_loss: 0.2224 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1369 - accuracy: 0.9541 - val_loss: 0.2401 - val_accuracy: 0.9295 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1309 - accuracy: 0.9595 - val_loss: 0.2131 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1004 - accuracy: 0.9683 - val_loss: 0.2495 - val_accuracy: 0.9311 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0969 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0972 - accuracy: 0.9697 - val_loss: 0.2479 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 201: 297.63 sec -Time taken for epoch(SUBo) 201: 245.98 sec -<---------------------------------------|Epoch [201] END|---------------------------------------> - -Epoch: 202/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1129 - accuracy: 0.9663 - val_loss: 0.2707 - val_accuracy: 0.9327 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1298 - accuracy: 0.9600 - val_loss: 0.2119 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1173 - accuracy: 0.9644 - val_loss: 0.2111 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1074 - accuracy: 0.9712 - val_loss: 0.1881 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0924 - accuracy: 0.9702 - val_loss: 0.2089 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9805 - val_loss: 0.2168 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 202: 298.33 sec -Time taken for epoch(SUBo) 202: 246.64 sec -<---------------------------------------|Epoch [202] END|---------------------------------------> - -Epoch: 203/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1369 - accuracy: 0.9561 - val_loss: 0.2180 - val_accuracy: 0.9343 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1303 - accuracy: 0.9541 - val_loss: 0.2391 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1245 - accuracy: 0.9634 - val_loss: 0.2390 - val_accuracy: 0.9359 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1135 - accuracy: 0.9648 - val_loss: 0.2664 - val_accuracy: 0.9279 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0981 - accuracy: 0.9727 - val_loss: 0.2374 - val_accuracy: 0.9359 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9722 - val_loss: 0.2165 - val_accuracy: 0.9375 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 203: 296.86 sec -Time taken for epoch(SUBo) 203: 245.14 sec -<---------------------------------------|Epoch [203] END|---------------------------------------> - -Epoch: 204/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.2079 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1075 - accuracy: 0.9648 - val_loss: 0.2058 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0978 - accuracy: 0.9673 - val_loss: 0.2125 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1015 - accuracy: 0.9722 - val_loss: 0.2370 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0780 - accuracy: 0.9775 - val_loss: 0.2245 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0684 - accuracy: 0.9814 - val_loss: 0.2192 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 204: 298.03 sec -Time taken for epoch(SUBo) 204: 246.30 sec -<---------------------------------------|Epoch [204] END|---------------------------------------> - -Epoch: 205/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1153 - accuracy: 0.9614 - val_loss: 0.2277 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1168 - accuracy: 0.9629 - val_loss: 0.2214 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1209 - accuracy: 0.9629 - val_loss: 0.1874 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1025 - accuracy: 0.9692 - val_loss: 0.2265 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0891 - accuracy: 0.9766 - val_loss: 0.1875 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0753 - accuracy: 0.9805 - val_loss: 0.2138 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 205: 297.87 sec -Time taken for epoch(SUBo) 205: 245.90 sec -<---------------------------------------|Epoch [205] END|---------------------------------------> - -Epoch: 206/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1070 - accuracy: 0.9697 - val_loss: 0.2057 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1039 - accuracy: 0.9673 - val_loss: 0.2215 - val_accuracy: 0.9391 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0855 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.3037 - val_accuracy: 0.9359 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9766 - val_loss: 0.2560 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0760 - accuracy: 0.9766 - val_loss: 0.2418 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 206: 297.94 sec -Time taken for epoch(SUBo) 206: 246.21 sec -<---------------------------------------|Epoch [206] END|---------------------------------------> - -Epoch: 207/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1259 - accuracy: 0.9658 - val_loss: 0.2366 - val_accuracy: 0.9359 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1204 - accuracy: 0.9644 - val_loss: 0.2283 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1144 - accuracy: 0.9624 - val_loss: 0.1889 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0992 - accuracy: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9407 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0875 - accuracy: 0.9775 - val_loss: 0.2601 - val_accuracy: 0.9343 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0808 - accuracy: 0.9800 - val_loss: 0.2478 - val_accuracy: 0.9343 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 207: 298.66 sec -Time taken for epoch(SUBo) 207: 246.51 sec -<---------------------------------------|Epoch [207] END|---------------------------------------> - -Epoch: 208/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1141 - accuracy: 0.9648 - val_loss: 0.2134 - val_accuracy: 0.9407 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1996 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0942 - accuracy: 0.9697 - val_loss: 0.1941 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0885 - accuracy: 0.9741 - val_loss: 0.2165 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0837 - accuracy: 0.9741 - val_loss: 0.2150 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0726 - accuracy: 0.9829 - val_loss: 0.2024 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 208: 298.91 sec -Time taken for epoch(SUBo) 208: 246.69 sec -<---------------------------------------|Epoch [208] END|---------------------------------------> - -Epoch: 209/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1141 - accuracy: 0.9639 - val_loss: 0.2234 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9629 - val_loss: 0.2288 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1251 - accuracy: 0.9624 - val_loss: 0.2119 - val_accuracy: 0.9407 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.2092 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9678 - val_loss: 0.2370 - val_accuracy: 0.9327 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0761 - accuracy: 0.9771 - val_loss: 0.2383 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 209: 296.92 sec -Time taken for epoch(SUBo) 209: 245.42 sec -<---------------------------------------|Epoch [209] END|---------------------------------------> - -Epoch: 210/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1219 - accuracy: 0.9619 - val_loss: 0.2331 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.2102 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1038 - accuracy: 0.9658 - val_loss: 0.1857 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9727 - val_loss: 0.2113 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1070 - accuracy: 0.9668 - val_loss: 0.2461 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0851 - accuracy: 0.9766 - val_loss: 0.2336 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 210: 295.74 sec -Time taken for epoch(SUBo) 210: 245.46 sec -<---------------------------------------|Epoch [210] END|---------------------------------------> - -Epoch: 211/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1203 - accuracy: 0.9658 - val_loss: 0.1951 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9673 - val_loss: 0.1898 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9423 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0830 - accuracy: 0.9751 - val_loss: 0.1828 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0600 - accuracy: 0.9829 - val_loss: 0.2026 - val_accuracy: 0.9423 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0587 - accuracy: 0.9854 - val_loss: 0.1957 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 211: 295.87 sec -Time taken for epoch(SUBo) 211: 245.59 sec -<---------------------------------------|Epoch [211] END|---------------------------------------> - -Epoch: 212/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9746 - val_loss: 0.1699 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1037 - accuracy: 0.9673 - val_loss: 0.2054 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0907 - accuracy: 0.9731 - val_loss: 0.2072 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0802 - accuracy: 0.9771 - val_loss: 0.1906 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0749 - accuracy: 0.9814 - val_loss: 0.1856 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0661 - accuracy: 0.9824 - val_loss: 0.1860 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 212: 295.86 sec -Time taken for epoch(SUBo) 212: 245.69 sec -<---------------------------------------|Epoch [212] END|---------------------------------------> - -Epoch: 213/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1047 - accuracy: 0.9688 - val_loss: 0.1803 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0977 - accuracy: 0.9746 - val_loss: 0.1586 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9722 - val_loss: 0.1882 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1002 - accuracy: 0.9756 - val_loss: 0.2034 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0865 - accuracy: 0.9766 - val_loss: 0.2175 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0730 - accuracy: 0.9790 - val_loss: 0.2228 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 213: 295.87 sec -Time taken for epoch(SUBo) 213: 245.31 sec -<---------------------------------------|Epoch [213] END|---------------------------------------> - -Epoch: 214/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1225 - accuracy: 0.9619 - val_loss: 0.1941 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1188 - accuracy: 0.9658 - val_loss: 0.1750 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1020 - accuracy: 0.9644 - val_loss: 0.2022 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0990 - accuracy: 0.9668 - val_loss: 0.1984 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0992 - accuracy: 0.9722 - val_loss: 0.2096 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0697 - accuracy: 0.9814 - val_loss: 0.2177 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 214: 295.73 sec -Time taken for epoch(SUBo) 214: 245.40 sec -<---------------------------------------|Epoch [214] END|---------------------------------------> - -Epoch: 215/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2052 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1099 - accuracy: 0.9688 - val_loss: 0.2122 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0904 - accuracy: 0.9712 - val_loss: 0.2057 - val_accuracy: 0.9455 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0789 - accuracy: 0.9756 - val_loss: 0.2348 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0699 - accuracy: 0.9834 - val_loss: 0.2055 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.2412 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 215: 296.11 sec -Time taken for epoch(SUBo) 215: 245.32 sec -<---------------------------------------|Epoch [215] END|---------------------------------------> - -Epoch: 216/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1354 - accuracy: 0.9619 - val_loss: 1.9127 - val_accuracy: 0.6250 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.6524 - accuracy: 0.6860 - val_loss: 0.5187 - val_accuracy: 0.8253 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.4618 - accuracy: 0.8057 - val_loss: 0.4150 - val_accuracy: 0.9103 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3662 - accuracy: 0.8638 - val_loss: 0.2908 - val_accuracy: 0.9263 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.3131 - accuracy: 0.8921 - val_loss: 0.3339 - val_accuracy: 0.9263 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2602 - accuracy: 0.9121 - val_loss: 0.3118 - val_accuracy: 0.9279 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 216: 294.77 sec -Time taken for epoch(SUBo) 216: 244.51 sec -<---------------------------------------|Epoch [216] END|---------------------------------------> - -Epoch: 217/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.3051 - accuracy: 0.8945 - val_loss: 0.2281 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2581 - accuracy: 0.9053 - val_loss: 0.2585 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.2153 - accuracy: 0.9385 - val_loss: 0.1958 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1935 - accuracy: 0.9463 - val_loss: 0.1896 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1759 - accuracy: 0.9492 - val_loss: 0.2038 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.2104 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 217: 295.94 sec -Time taken for epoch(SUBo) 217: 245.61 sec -<---------------------------------------|Epoch [217] END|---------------------------------------> - -Epoch: 218/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.2018 - accuracy: 0.9331 - val_loss: 0.2546 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1969 - accuracy: 0.9355 - val_loss: 0.2012 - val_accuracy: 0.9471 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1747 - accuracy: 0.9453 - val_loss: 0.1932 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1722 - accuracy: 0.9507 - val_loss: 0.2019 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1437 - accuracy: 0.9536 - val_loss: 0.2124 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1302 - accuracy: 0.9609 - val_loss: 0.2347 - val_accuracy: 0.9391 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 218: 295.43 sec -Time taken for epoch(SUBo) 218: 245.14 sec -<---------------------------------------|Epoch [218] END|---------------------------------------> - -Epoch: 219/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1756 - accuracy: 0.9478 - val_loss: 0.1971 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1803 - accuracy: 0.9414 - val_loss: 0.1779 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1618 - accuracy: 0.9424 - val_loss: 0.2014 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1546 - accuracy: 0.9600 - val_loss: 0.2209 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1289 - accuracy: 0.9639 - val_loss: 0.2224 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 219: 293.27 sec -Time taken for epoch(SUBo) 219: 243.09 sec -<---------------------------------------|Epoch [219] END|---------------------------------------> - -Epoch: 220/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 156ms/step - loss: 0.1384 - accuracy: 0.9580 - val_loss: 0.1899 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1636 - accuracy: 0.9497 - val_loss: 0.1965 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9561 - val_loss: 0.1807 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1192 - accuracy: 0.9629 - val_loss: 0.2034 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 39s 152ms/step - loss: 0.1268 - accuracy: 0.9585 - val_loss: 0.1812 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.1890 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 220: 289.19 sec -Time taken for epoch(SUBo) 220: 239.59 sec -<---------------------------------------|Epoch [220] END|---------------------------------------> - -Epoch: 221/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1293 - accuracy: 0.9604 - val_loss: 0.2001 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1258 - accuracy: 0.9629 - val_loss: 0.2138 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9629 - val_loss: 0.2242 - val_accuracy: 0.9471 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1100 - accuracy: 0.9712 - val_loss: 0.2425 - val_accuracy: 0.9391 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1082 - accuracy: 0.9712 - val_loss: 0.2177 - val_accuracy: 0.9455 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0903 - accuracy: 0.9751 - val_loss: 0.2145 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 221: 303.15 sec -Time taken for epoch(SUBo) 221: 246.93 sec -<---------------------------------------|Epoch [221] END|---------------------------------------> - -Epoch: 222/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2076 - val_accuracy: 0.9375 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1585 - accuracy: 0.9556 - val_loss: 0.2135 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1446 - accuracy: 0.9575 - val_loss: 0.2137 - val_accuracy: 0.9375 -Epoch 4/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1215 - accuracy: 0.9663 - val_loss: 0.2196 - val_accuracy: 0.9343 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1310 - accuracy: 0.9609 - val_loss: 0.2567 - val_accuracy: 0.9295 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1038 - accuracy: 0.9727 - val_loss: 0.2416 - val_accuracy: 0.9327 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 222: 299.03 sec -Time taken for epoch(SUBo) 222: 248.17 sec -<---------------------------------------|Epoch [222] END|---------------------------------------> - -Epoch: 223/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 165ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2650 - val_accuracy: 0.9311 -Epoch 2/6 -256/256 [==============================] - 42s 165ms/step - loss: 0.1193 - accuracy: 0.9570 - val_loss: 0.1668 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.1817 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.2031 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 43s 166ms/step - loss: 0.0876 - accuracy: 0.9751 - val_loss: 0.1877 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.0826 - accuracy: 0.9766 - val_loss: 0.1862 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 223: 312.69 sec -Time taken for epoch(SUBo) 223: 256.34 sec -<---------------------------------------|Epoch [223] END|---------------------------------------> - -Epoch: 224/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1238 - accuracy: 0.9668 - val_loss: 0.1797 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1198 - accuracy: 0.9624 - val_loss: 0.1924 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1028 - accuracy: 0.9712 - val_loss: 0.2374 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1065 - accuracy: 0.9722 - val_loss: 0.2279 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0899 - accuracy: 0.9771 - val_loss: 0.1902 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9795 - val_loss: 0.1907 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 224: 301.18 sec -Time taken for epoch(SUBo) 224: 248.32 sec -<---------------------------------------|Epoch [224] END|---------------------------------------> - -Epoch: 225/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1296 - accuracy: 0.9609 - val_loss: 0.1972 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.1699 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1233 - accuracy: 0.9624 - val_loss: 0.2114 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1953 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0820 - accuracy: 0.9780 - val_loss: 0.2077 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0815 - accuracy: 0.9814 - val_loss: 0.2196 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 225: 302.66 sec -Time taken for epoch(SUBo) 225: 248.69 sec -<---------------------------------------|Epoch [225] END|---------------------------------------> - -Epoch: 226/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1353 - accuracy: 0.9604 - val_loss: 0.2359 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.2577 - val_accuracy: 0.9359 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2211 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1084 - accuracy: 0.9707 - val_loss: 0.1719 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1007 - accuracy: 0.9712 - val_loss: 0.1720 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0895 - accuracy: 0.9751 - val_loss: 0.1756 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 226: 313.16 sec -Time taken for epoch(SUBo) 226: 251.41 sec -<---------------------------------------|Epoch [226] END|---------------------------------------> - -Epoch: 227/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1123 - accuracy: 0.9639 - val_loss: 0.1721 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1115 - accuracy: 0.9653 - val_loss: 0.2263 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1077 - accuracy: 0.9639 - val_loss: 0.1975 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0943 - accuracy: 0.9717 - val_loss: 0.2010 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0949 - accuracy: 0.9736 - val_loss: 0.1780 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0812 - accuracy: 0.9771 - val_loss: 0.1900 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 227: 306.72 sec -Time taken for epoch(SUBo) 227: 248.78 sec -<---------------------------------------|Epoch [227] END|---------------------------------------> - -Epoch: 228/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1398 - accuracy: 0.9546 - val_loss: 0.1847 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1483 - accuracy: 0.9551 - val_loss: 0.1827 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1162 - accuracy: 0.9678 - val_loss: 0.2110 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1037 - accuracy: 0.9639 - val_loss: 0.1890 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0836 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.0876 - accuracy: 0.9746 - val_loss: 0.1758 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 228: 307.11 sec -Time taken for epoch(SUBo) 228: 249.60 sec -<---------------------------------------|Epoch [228] END|---------------------------------------> - -Epoch: 229/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 162ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.1633 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1031 - accuracy: 0.9702 - val_loss: 0.1893 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1045 - accuracy: 0.9717 - val_loss: 0.1849 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0950 - accuracy: 0.9780 - val_loss: 0.1626 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0764 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0747 - accuracy: 0.9795 - val_loss: 0.1604 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 229: 302.46 sec -Time taken for epoch(SUBo) 229: 249.29 sec -<---------------------------------------|Epoch [229] END|---------------------------------------> - -Epoch: 230/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1203 - accuracy: 0.9648 - val_loss: 0.1849 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.1080 - accuracy: 0.9639 - val_loss: 0.1861 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0951 - accuracy: 0.9697 - val_loss: 0.2135 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.1004 - accuracy: 0.9712 - val_loss: 0.2054 - val_accuracy: 0.9439 -Epoch 5/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0709 - accuracy: 0.9771 - val_loss: 0.2113 - val_accuracy: 0.9439 -Epoch 6/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0771 - accuracy: 0.9766 - val_loss: 0.2083 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 230: 299.92 sec -Time taken for epoch(SUBo) 230: 248.71 sec -<---------------------------------------|Epoch [230] END|---------------------------------------> - -Epoch: 231/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 167ms/step - loss: 0.0909 - accuracy: 0.9707 - val_loss: 0.1829 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0896 - accuracy: 0.9746 - val_loss: 0.1859 - val_accuracy: 0.9423 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0809 - accuracy: 0.9766 - val_loss: 0.1953 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0791 - accuracy: 0.9780 - val_loss: 0.1783 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0703 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0497 - accuracy: 0.9863 - val_loss: 0.1703 - val_accuracy: 0.9455 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 231: 304.17 sec -Time taken for epoch(SUBo) 231: 253.03 sec -<---------------------------------------|Epoch [231] END|---------------------------------------> - -Epoch: 232/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 165ms/step - loss: 0.1107 - accuracy: 0.9683 - val_loss: 0.1718 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.1952 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1595 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0998 - accuracy: 0.9722 - val_loss: 0.1685 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.0773 - accuracy: 0.9795 - val_loss: 0.2042 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0897 - accuracy: 0.9780 - val_loss: 0.1887 - val_accuracy: 0.9407 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 232: 312.43 sec -Time taken for epoch(SUBo) 232: 254.46 sec -<---------------------------------------|Epoch [232] END|---------------------------------------> - -Epoch: 233/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 48s 167ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.1891 - val_accuracy: 0.9391 -Epoch 2/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.1052 - accuracy: 0.9688 - val_loss: 0.1659 - val_accuracy: 0.9455 -Epoch 3/6 -256/256 [==============================] - 42s 164ms/step - loss: 0.1140 - accuracy: 0.9688 - val_loss: 0.1445 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0954 - accuracy: 0.9717 - val_loss: 0.1710 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.0933 - accuracy: 0.9761 - val_loss: 0.1612 - val_accuracy: 0.9519 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0744 - accuracy: 0.9814 - val_loss: 0.1741 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 233: 313.69 sec -Time taken for epoch(SUBo) 233: 256.06 sec -<---------------------------------------|Epoch [233] END|---------------------------------------> - -Epoch: 234/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 167ms/step - loss: 0.1023 - accuracy: 0.9683 - val_loss: 0.1438 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 42s 162ms/step - loss: 0.0962 - accuracy: 0.9707 - val_loss: 0.2408 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 42s 162ms/step - loss: 0.0875 - accuracy: 0.9736 - val_loss: 0.1795 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 42s 163ms/step - loss: 0.0846 - accuracy: 0.9722 - val_loss: 0.1669 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 162ms/step - loss: 0.0591 - accuracy: 0.9844 - val_loss: 0.1704 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0565 - accuracy: 0.9873 - val_loss: 0.1818 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 234: 311.13 sec -Time taken for epoch(SUBo) 234: 255.01 sec -<---------------------------------------|Epoch [234] END|---------------------------------------> - -Epoch: 235/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1210 - accuracy: 0.9629 - val_loss: 0.1778 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.1125 - accuracy: 0.9663 - val_loss: 0.1453 - val_accuracy: 0.9519 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.1608 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0843 - accuracy: 0.9775 - val_loss: 0.1615 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.1832 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0672 - accuracy: 0.9800 - val_loss: 0.1808 - val_accuracy: 0.9439 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 235: 300.21 sec -Time taken for epoch(SUBo) 235: 251.11 sec -<---------------------------------------|Epoch [235] END|---------------------------------------> - -Epoch: 236/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 164ms/step - loss: 0.1133 - accuracy: 0.9639 - val_loss: 0.1626 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0993 - accuracy: 0.9648 - val_loss: 0.1585 - val_accuracy: 0.9583 -Epoch 3/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0924 - accuracy: 0.9717 - val_loss: 0.1581 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1336 - val_accuracy: 0.9583 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0724 - accuracy: 0.9790 - val_loss: 0.1694 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0585 - accuracy: 0.9839 - val_loss: 0.1735 - val_accuracy: 0.9503 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 236: 302.45 sec -Time taken for epoch(SUBo) 236: 251.67 sec -<---------------------------------------|Epoch [236] END|---------------------------------------> - -Epoch: 237/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1015 - accuracy: 0.9663 - val_loss: 0.1594 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0919 - accuracy: 0.9736 - val_loss: 0.1593 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0886 - accuracy: 0.9746 - val_loss: 0.1714 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0809 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9503 -Epoch 5/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0690 - accuracy: 0.9829 - val_loss: 0.2800 - val_accuracy: 0.9375 -Epoch 6/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0600 - accuracy: 0.9873 - val_loss: 0.2560 - val_accuracy: 0.9359 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 237: 301.88 sec -Time taken for epoch(SUBo) 237: 251.53 sec -<---------------------------------------|Epoch [237] END|---------------------------------------> - -Epoch: 238/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 47s 166ms/step - loss: 0.1051 - accuracy: 0.9663 - val_loss: 0.2133 - val_accuracy: 0.9423 -Epoch 2/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0934 - accuracy: 0.9717 - val_loss: 0.2560 - val_accuracy: 0.9375 -Epoch 3/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0785 - accuracy: 0.9790 - val_loss: 0.2045 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 41s 161ms/step - loss: 0.0702 - accuracy: 0.9790 - val_loss: 0.2433 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 160ms/step - loss: 0.0706 - accuracy: 0.9800 - val_loss: 0.1769 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0689 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 238: 307.10 sec -Time taken for epoch(SUBo) 238: 252.62 sec -<---------------------------------------|Epoch [238] END|---------------------------------------> - -Epoch: 239/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 46s 163ms/step - loss: 0.1147 - accuracy: 0.9673 - val_loss: 0.1823 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0958 - accuracy: 0.9751 - val_loss: 0.2081 - val_accuracy: 0.9407 -Epoch 3/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0865 - accuracy: 0.9775 - val_loss: 0.2058 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0716 - accuracy: 0.9795 - val_loss: 0.2068 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2146 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 41s 158ms/step - loss: 0.0562 - accuracy: 0.9834 - val_loss: 0.2186 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 239: 303.13 sec -Time taken for epoch(SUBo) 239: 249.48 sec -<---------------------------------------|Epoch [239] END|---------------------------------------> - -Epoch: 240/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.1219 - accuracy: 0.9595 - val_loss: 0.1957 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1010 - accuracy: 0.9717 - val_loss: 0.2189 - val_accuracy: 0.9327 -Epoch 3/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0829 - accuracy: 0.9756 - val_loss: 0.2015 - val_accuracy: 0.9439 -Epoch 4/6 -256/256 [==============================] - 41s 159ms/step - loss: 0.0715 - accuracy: 0.9780 - val_loss: 0.2191 - val_accuracy: 0.9487 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0614 - accuracy: 0.9839 - val_loss: 0.2335 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0522 - accuracy: 0.9858 - val_loss: 0.2491 - val_accuracy: 0.9295 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 240: 296.96 sec -Time taken for epoch(SUBo) 240: 247.49 sec -<---------------------------------------|Epoch [240] END|---------------------------------------> - -Epoch: 241/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0874 - accuracy: 0.9731 - val_loss: 0.2011 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0942 - accuracy: 0.9731 - val_loss: 0.1900 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0867 - accuracy: 0.9731 - val_loss: 0.2119 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0814 - accuracy: 0.9727 - val_loss: 0.2344 - val_accuracy: 0.9455 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0619 - accuracy: 0.9834 - val_loss: 0.2379 - val_accuracy: 0.9487 -Epoch 6/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0528 - accuracy: 0.9868 - val_loss: 0.2390 - val_accuracy: 0.9423 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 241: 301.50 sec -Time taken for epoch(SUBo) 241: 244.85 sec -<---------------------------------------|Epoch [241] END|---------------------------------------> - -Epoch: 242/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 157ms/step - loss: 0.1068 - accuracy: 0.9692 - val_loss: 0.2088 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.0962 - accuracy: 0.9692 - val_loss: 0.2827 - val_accuracy: 0.9343 -Epoch 3/6 -256/256 [==============================] - 39s 153ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.2028 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0831 - accuracy: 0.9761 - val_loss: 0.2217 - val_accuracy: 0.9551 -Epoch 5/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0908 - accuracy: 0.9775 - val_loss: 0.2048 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0678 - accuracy: 0.9814 - val_loss: 0.1931 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 242: 289.32 sec -Time taken for epoch(SUBo) 242: 241.25 sec -<---------------------------------------|Epoch [242] END|---------------------------------------> - -Epoch: 243/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 44s 158ms/step - loss: 0.1125 - accuracy: 0.9692 - val_loss: 0.1588 - val_accuracy: 0.9487 -Epoch 2/6 -256/256 [==============================] - 40s 154ms/step - loss: 0.0962 - accuracy: 0.9668 - val_loss: 0.1660 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0947 - accuracy: 0.9717 - val_loss: 0.2053 - val_accuracy: 0.9343 -Epoch 4/6 -256/256 [==============================] - 39s 154ms/step - loss: 0.0780 - accuracy: 0.9756 - val_loss: 0.1659 - val_accuracy: 0.9471 -Epoch 5/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0762 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9407 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0544 - accuracy: 0.9844 - val_loss: 0.1827 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 243: 289.77 sec -Time taken for epoch(SUBo) 243: 242.82 sec -<---------------------------------------|Epoch [243] END|---------------------------------------> - -Epoch: 244/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1976 - val_accuracy: 0.9439 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0864 - accuracy: 0.9775 - val_loss: 0.2101 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1914 - val_accuracy: 0.9487 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2286 - val_accuracy: 0.9375 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0735 - accuracy: 0.9819 - val_loss: 0.2039 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0471 - accuracy: 0.9897 - val_loss: 0.2055 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 244: 292.32 sec -Time taken for epoch(SUBo) 244: 245.77 sec -<---------------------------------------|Epoch [244] END|---------------------------------------> - -Epoch: 245/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1215 - accuracy: 0.9648 - val_loss: 0.1895 - val_accuracy: 0.9455 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.1283 - accuracy: 0.9629 - val_loss: 0.1734 - val_accuracy: 0.9439 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0933 - accuracy: 0.9731 - val_loss: 0.1550 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1631 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0857 - accuracy: 0.9731 - val_loss: 0.1576 - val_accuracy: 0.9583 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0706 - accuracy: 0.9824 - val_loss: 0.1603 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 245: 293.35 sec -Time taken for epoch(SUBo) 245: 246.05 sec -<---------------------------------------|Epoch [245] END|---------------------------------------> - -Epoch: 246/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0914 - accuracy: 0.9771 - val_loss: 0.1657 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9697 - val_loss: 0.1844 - val_accuracy: 0.9503 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9756 - val_loss: 0.1675 - val_accuracy: 0.9567 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0649 - accuracy: 0.9800 - val_loss: 0.1947 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0551 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0458 - accuracy: 0.9897 - val_loss: 0.1977 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 246: 292.03 sec -Time taken for epoch(SUBo) 246: 245.80 sec -<---------------------------------------|Epoch [246] END|---------------------------------------> - -Epoch: 247/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 162ms/step - loss: 0.0973 - accuracy: 0.9727 - val_loss: 0.1630 - val_accuracy: 0.9503 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0989 - accuracy: 0.9702 - val_loss: 0.1590 - val_accuracy: 0.9551 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0671 - accuracy: 0.9800 - val_loss: 0.1650 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0731 - accuracy: 0.9805 - val_loss: 0.1396 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9854 - val_loss: 0.1649 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0560 - accuracy: 0.9883 - val_loss: 0.1677 - val_accuracy: 0.9535 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 247: 294.16 sec -Time taken for epoch(SUBo) 247: 247.01 sec -<---------------------------------------|Epoch [247] END|---------------------------------------> - -Epoch: 248/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0900 - accuracy: 0.9756 - val_loss: 0.1515 - val_accuracy: 0.9551 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9761 - val_loss: 0.1617 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9519 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0630 - accuracy: 0.9858 - val_loss: 0.1660 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0626 - accuracy: 0.9834 - val_loss: 0.1958 - val_accuracy: 0.9535 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0587 - accuracy: 0.9849 - val_loss: 0.1824 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 248: 293.13 sec -Time taken for epoch(SUBo) 248: 246.15 sec -<---------------------------------------|Epoch [248] END|---------------------------------------> - -Epoch: 249/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.1567 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.1036 - accuracy: 0.9717 - val_loss: 0.1435 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0755 - accuracy: 0.9780 - val_loss: 0.1969 - val_accuracy: 0.9503 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0742 - accuracy: 0.9775 - val_loss: 0.1623 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9551 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0557 - accuracy: 0.9844 - val_loss: 0.1914 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 249: 293.33 sec -Time taken for epoch(SUBo) 249: 245.80 sec -<---------------------------------------|Epoch [249] END|---------------------------------------> - -Epoch: 250/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.1097 - accuracy: 0.9658 - val_loss: 0.1761 - val_accuracy: 0.9519 -Epoch 2/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0999 - accuracy: 0.9697 - val_loss: 0.1736 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 155ms/step - loss: 0.0943 - accuracy: 0.9673 - val_loss: 0.1766 - val_accuracy: 0.9535 -Epoch 4/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.1743 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0821 - accuracy: 0.9727 - val_loss: 0.1941 - val_accuracy: 0.9503 -Epoch 6/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0683 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9487 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 250: 292.13 sec -Time taken for epoch(SUBo) 250: 244.59 sec -<---------------------------------------|Epoch [250] END|---------------------------------------> - -Epoch: 251/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9707 - val_loss: 0.1764 - val_accuracy: 0.9471 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0835 - accuracy: 0.9736 - val_loss: 0.1675 - val_accuracy: 0.9567 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9785 - val_loss: 0.1513 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0704 - accuracy: 0.9800 - val_loss: 0.1564 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0581 - accuracy: 0.9839 - val_loss: 0.1602 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0579 - accuracy: 0.9849 - val_loss: 0.1547 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.146798238158226. Not saving model. -Time taken for epoch(FULL) 251: 292.96 sec -Time taken for epoch(SUBo) 251: 246.01 sec -<---------------------------------------|Epoch [251] END|---------------------------------------> - -Epoch: 252/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0999 - accuracy: 0.9707 - val_loss: 0.1387 - val_accuracy: 0.9567 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0826 - accuracy: 0.9756 - val_loss: 0.1897 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0722 - accuracy: 0.9775 - val_loss: 0.1514 - val_accuracy: 0.9615 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.1432 - val_accuracy: 0.9599 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0720 - accuracy: 0.9814 - val_loss: 0.1414 - val_accuracy: 0.9599 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0650 - accuracy: 0.9795 - val_loss: 0.1418 - val_accuracy: 0.9583 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.146798238158226 to 0.14178654551506042. Saving model. -Time taken for epoch(FULL) 252: 295.30 sec -Time taken for epoch(SUBo) 252: 246.41 sec -<---------------------------------------|Epoch [252] END|---------------------------------------> - -Epoch: 253/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1538 - val_accuracy: 0.9599 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0866 - accuracy: 0.9761 - val_loss: 0.1447 - val_accuracy: 0.9599 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0777 - accuracy: 0.9800 - val_loss: 0.1519 - val_accuracy: 0.9583 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0644 - accuracy: 0.9829 - val_loss: 0.1863 - val_accuracy: 0.9423 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0585 - accuracy: 0.9868 - val_loss: 0.1939 - val_accuracy: 0.9471 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0511 - accuracy: 0.9878 - val_loss: 0.1766 - val_accuracy: 0.9471 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 253: 293.56 sec -Time taken for epoch(SUBo) 253: 246.59 sec -<---------------------------------------|Epoch [253] END|---------------------------------------> - -Epoch: 254/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.1089 - accuracy: 0.9673 - val_loss: 0.1512 - val_accuracy: 0.9583 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0968 - accuracy: 0.9653 - val_loss: 0.1482 - val_accuracy: 0.9535 -Epoch 3/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0950 - accuracy: 0.9658 - val_loss: 0.1955 - val_accuracy: 0.9391 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0852 - accuracy: 0.9756 - val_loss: 0.1505 - val_accuracy: 0.9567 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0796 - accuracy: 0.9795 - val_loss: 0.1484 - val_accuracy: 0.9567 -Epoch 6/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0683 - accuracy: 0.9810 - val_loss: 0.1534 - val_accuracy: 0.9567 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 254: 293.79 sec -Time taken for epoch(SUBo) 254: 246.40 sec -<---------------------------------------|Epoch [254] END|---------------------------------------> - -Epoch: 255/256 | [Fine tuning] -Shuffling data... -Taking a subset of [2048]... -Augmenting data... -Setting model OneCycleLr::maxlr to [0.001500]... -Setting model subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -256/256 [==============================] - 45s 161ms/step - loss: 0.0860 - accuracy: 0.9746 - val_loss: 0.1747 - val_accuracy: 0.9535 -Epoch 2/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9727 - val_loss: 0.1806 - val_accuracy: 0.9487 -Epoch 3/6 -256/256 [==============================] - 40s 156ms/step - loss: 0.0816 - accuracy: 0.9756 - val_loss: 0.1677 - val_accuracy: 0.9551 -Epoch 4/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9834 - val_loss: 0.1808 - val_accuracy: 0.9535 -Epoch 5/6 -256/256 [==============================] - 40s 157ms/step - loss: 0.0564 - accuracy: 0.9844 - val_loss: 0.2127 - val_accuracy: 0.9391 -Epoch 6/6 -256/256 [==============================] - 40s 158ms/step - loss: 0.0513 - accuracy: 0.9883 - val_loss: 0.1953 - val_accuracy: 0.9519 -Subset training done. -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.14178654551506042. Not saving model. -Time taken for epoch(FULL) 255: 293.85 sec -Time taken for epoch(SUBo) 255: 246.35 sec -<---------------------------------------|Epoch [255] END|---------------------------------------> -Training done. - +Training the model... + +Epoch: 1/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 55s 166ms/step - loss: 20.4375 - accuracy: 0.6216 - val_loss: 15.7907 - val_accuracy: 0.8157 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 10.5470 - accuracy: 0.7383 - val_loss: 6.2110 - val_accuracy: 0.8205 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 4.1127 - accuracy: 0.7842 - val_loss: 2.5556 - val_accuracy: 0.8702 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 1.8795 - accuracy: 0.8096 - val_loss: 1.2612 - val_accuracy: 0.8718 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 1.0468 - accuracy: 0.8398 - val_loss: 0.8444 - val_accuracy: 0.8686 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.7275 - accuracy: 0.8555 - val_loss: 0.6163 - val_accuracy: 0.8766 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.5332 - accuracy: 0.8926 - val_loss: 0.5743 - val_accuracy: 0.8878 +Epoch 8/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.4697 - accuracy: 0.8979 - val_loss: 0.5413 - val_accuracy: 0.8702 +Subset training done. +Improved model accuracy from 0 to 0.870192289352417. Saving model. +Improved model loss from inf to 0.5412302017211914. Saving model. +Time taken for epoch(FULL) 1: 386.46 sec +Time taken for epoch(SUBo) 1: 333.70 sec +<---------------------------------------|Epoch [1] END|---------------------------------------> + +Epoch: 2/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.5620 - accuracy: 0.8359 - val_loss: 0.5067 - val_accuracy: 0.8446 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.5338 - accuracy: 0.8403 - val_loss: 0.6622 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.5047 - accuracy: 0.8418 - val_loss: 0.3689 - val_accuracy: 0.8926 +Epoch 4/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.4192 - accuracy: 0.8638 - val_loss: 0.4566 - val_accuracy: 0.8686 +Epoch 5/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.4020 - accuracy: 0.8677 - val_loss: 0.3214 - val_accuracy: 0.8670 +Epoch 6/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3681 - accuracy: 0.8813 - val_loss: 0.3148 - val_accuracy: 0.9199 +Epoch 7/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3198 - accuracy: 0.8931 - val_loss: 0.2567 - val_accuracy: 0.9279 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2715 - accuracy: 0.9180 - val_loss: 0.2393 - val_accuracy: 0.9311 +Subset training done. +Improved model accuracy from 0.870192289352417 to 0.9310897588729858. Saving model. +Improved model loss from 0.5412302017211914 to 0.23925769329071045. Saving model. +Time taken for epoch(FULL) 2: 381.03 sec +Time taken for epoch(SUBo) 2: 325.77 sec +<---------------------------------------|Epoch [2] END|---------------------------------------> + +Epoch: 3/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 161ms/step - loss: 0.3640 - accuracy: 0.8696 - val_loss: 0.3126 - val_accuracy: 0.9247 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3588 - accuracy: 0.8735 - val_loss: 0.3768 - val_accuracy: 0.9295 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3830 - accuracy: 0.8730 - val_loss: 0.4670 - val_accuracy: 0.9391 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3658 - accuracy: 0.8887 - val_loss: 0.2308 - val_accuracy: 0.9359 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3717 - accuracy: 0.8779 - val_loss: 0.2747 - val_accuracy: 0.9199 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3136 - accuracy: 0.9028 - val_loss: 0.3153 - val_accuracy: 0.9022 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2696 - accuracy: 0.9136 - val_loss: 0.2452 - val_accuracy: 0.9247 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2407 - accuracy: 0.9243 - val_loss: 0.2541 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9310897588729858. Not saving model. +Model loss did not improve from 0.23925769329071045. Not saving model. +Time taken for epoch(FULL) 3: 376.52 sec +Time taken for epoch(SUBo) 3: 324.58 sec +<---------------------------------------|Epoch [3] END|---------------------------------------> + +Epoch: 4/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.3534 - accuracy: 0.8784 - val_loss: 0.2325 - val_accuracy: 0.9215 +Epoch 2/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.3861 - accuracy: 0.8584 - val_loss: 0.4468 - val_accuracy: 0.9103 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3696 - accuracy: 0.8765 - val_loss: 0.4794 - val_accuracy: 0.9038 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3680 - accuracy: 0.8828 - val_loss: 0.2781 - val_accuracy: 0.9231 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2897 - accuracy: 0.9165 - val_loss: 0.2823 - val_accuracy: 0.9327 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2801 - accuracy: 0.9165 - val_loss: 0.2447 - val_accuracy: 0.9071 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2460 - accuracy: 0.9326 - val_loss: 0.2840 - val_accuracy: 0.9359 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1982 - accuracy: 0.9414 - val_loss: 0.2283 - val_accuracy: 0.9343 +Subset training done. +Improved model accuracy from 0.9310897588729858 to 0.9342948794364929. Saving model. +Improved model loss from 0.23925769329071045 to 0.22827185690402985. Saving model. +Time taken for epoch(FULL) 4: 379.86 sec +Time taken for epoch(SUBo) 4: 325.33 sec +<---------------------------------------|Epoch [4] END|---------------------------------------> + +Epoch: 5/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.3226 - accuracy: 0.8950 - val_loss: 0.3022 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3538 - accuracy: 0.8862 - val_loss: 0.3310 - val_accuracy: 0.9279 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3201 - accuracy: 0.8892 - val_loss: 0.2884 - val_accuracy: 0.9071 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3229 - accuracy: 0.9009 - val_loss: 0.5201 - val_accuracy: 0.7340 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3079 - accuracy: 0.8926 - val_loss: 0.2863 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2670 - accuracy: 0.9141 - val_loss: 0.2587 - val_accuracy: 0.9151 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2639 - accuracy: 0.9209 - val_loss: 0.2800 - val_accuracy: 0.9054 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1925 - accuracy: 0.9541 - val_loss: 0.2547 - val_accuracy: 0.9087 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 5: 375.38 sec +Time taken for epoch(SUBo) 5: 323.81 sec +<---------------------------------------|Epoch [5] END|---------------------------------------> + +Epoch: 6/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2908 - accuracy: 0.8994 - val_loss: 0.3886 - val_accuracy: 0.9151 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2973 - accuracy: 0.8984 - val_loss: 0.4025 - val_accuracy: 0.7917 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3093 - accuracy: 0.8945 - val_loss: 0.9113 - val_accuracy: 0.6907 +Epoch 4/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2903 - accuracy: 0.9048 - val_loss: 0.3253 - val_accuracy: 0.8766 +Epoch 5/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2965 - accuracy: 0.9131 - val_loss: 0.3971 - val_accuracy: 0.8798 +Epoch 6/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2341 - accuracy: 0.9238 - val_loss: 0.3240 - val_accuracy: 0.9071 +Epoch 7/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.2202 - accuracy: 0.9316 - val_loss: 0.3072 - val_accuracy: 0.9151 +Epoch 8/8 +256/256 [==============================] - 40s 157ms/step - loss: 0.1613 - accuracy: 0.9614 - val_loss: 0.3554 - val_accuracy: 0.9167 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 6: 377.12 sec +Time taken for epoch(SUBo) 6: 325.55 sec +<---------------------------------------|Epoch [6] END|---------------------------------------> + +Epoch: 7/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 161ms/step - loss: 0.2840 - accuracy: 0.9102 - val_loss: 0.3625 - val_accuracy: 0.8878 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3095 - accuracy: 0.9033 - val_loss: 0.3963 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3532 - accuracy: 0.8887 - val_loss: 0.2555 - val_accuracy: 0.9263 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3018 - accuracy: 0.8979 - val_loss: 0.2644 - val_accuracy: 0.9375 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3154 - accuracy: 0.9048 - val_loss: 0.4598 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2509 - accuracy: 0.9312 - val_loss: 0.2478 - val_accuracy: 0.9295 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1902 - accuracy: 0.9478 - val_loss: 0.2697 - val_accuracy: 0.9311 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1628 - accuracy: 0.9531 - val_loss: 0.2426 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9342948794364929. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 7: 376.09 sec +Time taken for epoch(SUBo) 7: 324.32 sec +<---------------------------------------|Epoch [7] END|---------------------------------------> + +Epoch: 8/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2691 - accuracy: 0.9106 - val_loss: 0.4805 - val_accuracy: 0.9071 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3014 - accuracy: 0.8994 - val_loss: 0.2715 - val_accuracy: 0.8926 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3387 - accuracy: 0.8818 - val_loss: 0.3345 - val_accuracy: 0.8542 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3069 - accuracy: 0.9072 - val_loss: 0.3671 - val_accuracy: 0.9359 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2874 - accuracy: 0.9058 - val_loss: 0.2579 - val_accuracy: 0.9343 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2255 - accuracy: 0.9399 - val_loss: 0.3501 - val_accuracy: 0.9375 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1835 - accuracy: 0.9492 - val_loss: 0.2757 - val_accuracy: 0.9407 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1739 - accuracy: 0.9492 - val_loss: 0.2712 - val_accuracy: 0.9391 +Subset training done. +Improved model accuracy from 0.9342948794364929 to 0.9391025900840759. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 8: 377.68 sec +Time taken for epoch(SUBo) 8: 324.30 sec +<---------------------------------------|Epoch [8] END|---------------------------------------> + +Epoch: 9/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2921 - accuracy: 0.9077 - val_loss: 0.3537 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2866 - accuracy: 0.9082 - val_loss: 0.3213 - val_accuracy: 0.9359 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2978 - accuracy: 0.8999 - val_loss: 0.3623 - val_accuracy: 0.9199 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2635 - accuracy: 0.9209 - val_loss: 0.4593 - val_accuracy: 0.8942 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2444 - accuracy: 0.9287 - val_loss: 0.3207 - val_accuracy: 0.9215 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2366 - accuracy: 0.9277 - val_loss: 0.3259 - val_accuracy: 0.9167 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1802 - accuracy: 0.9478 - val_loss: 0.3234 - val_accuracy: 0.9231 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9648 - val_loss: 0.2856 - val_accuracy: 0.9247 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 9: 373.86 sec +Time taken for epoch(SUBo) 9: 323.15 sec +<---------------------------------------|Epoch [9] END|---------------------------------------> + +Epoch: 10/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2630 - accuracy: 0.9165 - val_loss: 0.2739 - val_accuracy: 0.9311 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3113 - accuracy: 0.9082 - val_loss: 0.3775 - val_accuracy: 0.9054 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2991 - accuracy: 0.9102 - val_loss: 0.4075 - val_accuracy: 0.9247 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2560 - accuracy: 0.9365 - val_loss: 0.3893 - val_accuracy: 0.9103 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2622 - accuracy: 0.9360 - val_loss: 0.3810 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2561 - accuracy: 0.9360 - val_loss: 0.3800 - val_accuracy: 0.9215 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1804 - accuracy: 0.9561 - val_loss: 0.2602 - val_accuracy: 0.9295 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1792 - accuracy: 0.9565 - val_loss: 0.3396 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 10: 375.11 sec +Time taken for epoch(SUBo) 10: 323.65 sec +<---------------------------------------|Epoch [10] END|---------------------------------------> + +Epoch: 11/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2826 - accuracy: 0.9058 - val_loss: 0.2663 - val_accuracy: 0.9183 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.3059 - accuracy: 0.9033 - val_loss: 0.3297 - val_accuracy: 0.9199 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.3283 - accuracy: 0.9102 - val_loss: 0.3599 - val_accuracy: 0.9375 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2491 - accuracy: 0.9375 - val_loss: 0.3099 - val_accuracy: 0.9327 +Epoch 5/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.2357 - accuracy: 0.9336 - val_loss: 0.4078 - val_accuracy: 0.9167 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2435 - accuracy: 0.9365 - val_loss: 0.2847 - val_accuracy: 0.9359 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1802 - accuracy: 0.9575 - val_loss: 0.3534 - val_accuracy: 0.9295 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1446 - accuracy: 0.9644 - val_loss: 0.3434 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 11: 374.31 sec +Time taken for epoch(SUBo) 11: 322.89 sec +<---------------------------------------|Epoch [11] END|---------------------------------------> + +Epoch: 12/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 160ms/step - loss: 0.2543 - accuracy: 0.9263 - val_loss: 0.4121 - val_accuracy: 0.9327 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2741 - accuracy: 0.9258 - val_loss: 0.3493 - val_accuracy: 0.9359 +Epoch 3/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2763 - accuracy: 0.9307 - val_loss: 0.3661 - val_accuracy: 0.9359 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2352 - accuracy: 0.9414 - val_loss: 0.3281 - val_accuracy: 0.9215 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2251 - accuracy: 0.9453 - val_loss: 0.2411 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1783 - accuracy: 0.9595 - val_loss: 0.3297 - val_accuracy: 0.9247 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1812 - accuracy: 0.9619 - val_loss: 0.2638 - val_accuracy: 0.9087 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9702 - val_loss: 0.2747 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9391025900840759. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 12: 375.91 sec +Time taken for epoch(SUBo) 12: 324.60 sec +<---------------------------------------|Epoch [12] END|---------------------------------------> + +Epoch: 13/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 159ms/step - loss: 0.2384 - accuracy: 0.9326 - val_loss: 0.2895 - val_accuracy: 0.9231 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2609 - accuracy: 0.9287 - val_loss: 0.2950 - val_accuracy: 0.9119 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2872 - accuracy: 0.9277 - val_loss: 0.3571 - val_accuracy: 0.9087 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2855 - accuracy: 0.9229 - val_loss: 0.5538 - val_accuracy: 0.9087 +Epoch 5/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2314 - accuracy: 0.9478 - val_loss: 0.2693 - val_accuracy: 0.9311 +Epoch 6/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1893 - accuracy: 0.9546 - val_loss: 0.2341 - val_accuracy: 0.9343 +Epoch 7/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1685 - accuracy: 0.9600 - val_loss: 0.2727 - val_accuracy: 0.9439 +Epoch 8/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.1422 - accuracy: 0.9736 - val_loss: 0.2968 - val_accuracy: 0.9407 +Subset training done. +Improved model accuracy from 0.9391025900840759 to 0.9407051205635071. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 13: 376.01 sec +Time taken for epoch(SUBo) 13: 323.00 sec +<---------------------------------------|Epoch [13] END|---------------------------------------> + +Epoch: 14/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 45s 159ms/step - loss: 0.2536 - accuracy: 0.9341 - val_loss: 0.3728 - val_accuracy: 0.9295 +Epoch 2/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2549 - accuracy: 0.9272 - val_loss: 0.2704 - val_accuracy: 0.9279 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2345 - accuracy: 0.9419 - val_loss: 0.3342 - val_accuracy: 0.9327 +Epoch 4/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2225 - accuracy: 0.9541 - val_loss: 0.3081 - val_accuracy: 0.9151 +Epoch 5/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.2233 - accuracy: 0.9443 - val_loss: 0.2983 - val_accuracy: 0.9263 +Epoch 6/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1875 - accuracy: 0.9521 - val_loss: 0.2882 - val_accuracy: 0.9327 +Epoch 7/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1461 - accuracy: 0.9673 - val_loss: 0.2289 - val_accuracy: 0.9359 +Epoch 8/8 +256/256 [==============================] - 40s 156ms/step - loss: 0.1285 - accuracy: 0.9717 - val_loss: 0.2355 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9407051205635071. Not saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 14: 376.09 sec +Time taken for epoch(SUBo) 14: 324.46 sec +<---------------------------------------|Epoch [14] END|---------------------------------------> + +Epoch: 15/256 | [Learning the patterns] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [8]... +Training on subset... +Epoch 1/8 +256/256 [==============================] - 44s 158ms/step - loss: 0.2348 - accuracy: 0.9341 - val_loss: 0.3880 - val_accuracy: 0.9263 +Epoch 2/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2625 - accuracy: 0.9224 - val_loss: 0.3617 - val_accuracy: 0.9327 +Epoch 3/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2578 - accuracy: 0.9292 - val_loss: 0.3288 - val_accuracy: 0.9263 +Epoch 4/8 +256/256 [==============================] - 40s 155ms/step - loss: 0.2543 - accuracy: 0.9302 - val_loss: 0.3120 - val_accuracy: 0.9038 +Epoch 5/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.3444 - accuracy: 0.9067 - val_loss: 0.2470 - val_accuracy: 0.9391 +Epoch 6/8 +256/256 [==============================] - 40s 154ms/step - loss: 0.2173 - accuracy: 0.9424 - val_loss: 0.3219 - val_accuracy: 0.9343 +Epoch 7/8 +256/256 [==============================] - 39s 154ms/step - loss: 0.1908 - accuracy: 0.9526 - val_loss: 0.2278 - val_accuracy: 0.9407 +Epoch 8/8 +256/256 [==============================] - 39s 154ms/step - loss: 0.1584 - accuracy: 0.9600 - val_loss: 0.2384 - val_accuracy: 0.9439 +Subset training done. +Improved model accuracy from 0.9407051205635071 to 0.9439102411270142. Saving model. +Model loss did not improve from 0.22827185690402985. Not saving model. +Time taken for epoch(FULL) 15: 374.28 sec +Time taken for epoch(SUBo) 15: 321.58 sec +<---------------------------------------|Epoch [15] END|---------------------------------------> + +Epoch: 16/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.010000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.2960 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.3020 - accuracy: 0.9111 - val_loss: 0.3527 - val_accuracy: 0.8622 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2673 - accuracy: 0.9238 - val_loss: 0.5715 - val_accuracy: 0.7340 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2500 - accuracy: 0.9277 - val_loss: 0.5034 - val_accuracy: 0.7484 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2083 - accuracy: 0.9478 - val_loss: 0.2478 - val_accuracy: 0.9071 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1548 - accuracy: 0.9624 - val_loss: 0.2110 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Improved model loss from 0.22827185690402985 to 0.21101020276546478. Saving model. +Time taken for epoch(FULL) 16: 297.03 sec +Time taken for epoch(SUBo) 16: 244.35 sec +<---------------------------------------|Epoch [16] END|---------------------------------------> + +Epoch: 17/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.009500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.2628 - accuracy: 0.9282 - val_loss: 0.2316 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2760 - accuracy: 0.9209 - val_loss: 0.3350 - val_accuracy: 0.9279 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2708 - accuracy: 0.9189 - val_loss: 0.3418 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.2240 - accuracy: 0.9419 - val_loss: 0.2829 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1962 - accuracy: 0.9526 - val_loss: 0.2832 - val_accuracy: 0.8926 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1605 - accuracy: 0.9609 - val_loss: 0.2716 - val_accuracy: 0.8974 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 17: 294.63 sec +Time taken for epoch(SUBo) 17: 243.78 sec +<---------------------------------------|Epoch [17] END|---------------------------------------> + +Epoch: 18/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.009000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2515 - accuracy: 0.9263 - val_loss: 0.2493 - val_accuracy: 0.9199 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3009 - accuracy: 0.9219 - val_loss: 0.3727 - val_accuracy: 0.8894 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2677 - accuracy: 0.9224 - val_loss: 0.3309 - val_accuracy: 0.9151 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.2292 - accuracy: 0.9395 - val_loss: 0.2943 - val_accuracy: 0.8910 +Epoch 5/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.1811 - accuracy: 0.9556 - val_loss: 0.2777 - val_accuracy: 0.9087 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1560 - accuracy: 0.9663 - val_loss: 0.2857 - val_accuracy: 0.9215 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 18: 293.64 sec +Time taken for epoch(SUBo) 18: 242.80 sec +<---------------------------------------|Epoch [18] END|---------------------------------------> + +Epoch: 19/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.008500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.2747 - accuracy: 0.9248 - val_loss: 0.2540 - val_accuracy: 0.9038 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.3029 - accuracy: 0.9082 - val_loss: 0.2379 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2496 - accuracy: 0.9297 - val_loss: 0.2431 - val_accuracy: 0.9103 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2807 - accuracy: 0.9087 - val_loss: 0.2517 - val_accuracy: 0.8958 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1925 - accuracy: 0.9463 - val_loss: 0.2512 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9639 - val_loss: 0.2388 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.21101020276546478. Not saving model. +Time taken for epoch(FULL) 19: 287.17 sec +Time taken for epoch(SUBo) 19: 237.22 sec +<---------------------------------------|Epoch [19] END|---------------------------------------> + +Epoch: 20/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.008000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2361 - accuracy: 0.9326 - val_loss: 0.3213 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2437 - accuracy: 0.9282 - val_loss: 0.3130 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2265 - accuracy: 0.9390 - val_loss: 0.7231 - val_accuracy: 0.5817 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2058 - accuracy: 0.9463 - val_loss: 0.2048 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1747 - accuracy: 0.9585 - val_loss: 0.2309 - val_accuracy: 0.9135 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1581 - accuracy: 0.9624 - val_loss: 0.2022 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Improved model loss from 0.21101020276546478 to 0.20221146941184998. Saving model. +Time taken for epoch(FULL) 20: 287.49 sec +Time taken for epoch(SUBo) 20: 236.52 sec +<---------------------------------------|Epoch [20] END|---------------------------------------> + +Epoch: 21/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.007500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2639 - accuracy: 0.9204 - val_loss: 0.3842 - val_accuracy: 0.8542 +Epoch 2/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.2602 - accuracy: 0.9224 - val_loss: 0.2024 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2491 - accuracy: 0.9204 - val_loss: 0.3014 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2034 - accuracy: 0.9521 - val_loss: 0.2709 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2075 - accuracy: 0.9429 - val_loss: 0.3214 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1429 - accuracy: 0.9648 - val_loss: 0.2890 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 21: 285.90 sec +Time taken for epoch(SUBo) 21: 236.69 sec +<---------------------------------------|Epoch [21] END|---------------------------------------> + +Epoch: 22/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.007000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2293 - accuracy: 0.9360 - val_loss: 0.1936 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2278 - accuracy: 0.9341 - val_loss: 0.2616 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2114 - accuracy: 0.9438 - val_loss: 0.2647 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2235 - accuracy: 0.9453 - val_loss: 0.2567 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1777 - accuracy: 0.9541 - val_loss: 0.2569 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1626 - accuracy: 0.9575 - val_loss: 0.2484 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 22: 285.94 sec +Time taken for epoch(SUBo) 22: 236.66 sec +<---------------------------------------|Epoch [22] END|---------------------------------------> + +Epoch: 23/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.006500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2132 - accuracy: 0.9385 - val_loss: 0.2144 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2413 - accuracy: 0.9360 - val_loss: 0.5426 - val_accuracy: 0.8750 +Epoch 3/6 +256/256 [==============================] - 38s 149ms/step - loss: 0.2458 - accuracy: 0.9375 - val_loss: 0.2533 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1869 - accuracy: 0.9453 - val_loss: 0.2258 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9663 - val_loss: 0.2642 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 38s 149ms/step - loss: 0.1227 - accuracy: 0.9746 - val_loss: 0.2471 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9439102411270142. Not saving model. +Model loss did not improve from 0.20221146941184998. Not saving model. +Time taken for epoch(FULL) 23: 284.47 sec +Time taken for epoch(SUBo) 23: 235.20 sec +<---------------------------------------|Epoch [23] END|---------------------------------------> + +Epoch: 24/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.006000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2147 - accuracy: 0.9365 - val_loss: 0.2431 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2151 - accuracy: 0.9385 - val_loss: 0.2308 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2120 - accuracy: 0.9380 - val_loss: 0.2704 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1936 - accuracy: 0.9453 - val_loss: 0.2529 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1498 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1086 - accuracy: 0.9756 - val_loss: 0.1858 - val_accuracy: 0.9471 +Subset training done. +Improved model accuracy from 0.9439102411270142 to 0.9471153616905212. Saving model. +Improved model loss from 0.20221146941184998 to 0.1857679933309555. Saving model. +Time taken for epoch(FULL) 24: 288.85 sec +Time taken for epoch(SUBo) 24: 236.73 sec +<---------------------------------------|Epoch [24] END|---------------------------------------> + +Epoch: 25/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.005500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.2106 - accuracy: 0.9414 - val_loss: 0.2085 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2304 - accuracy: 0.9326 - val_loss: 0.2498 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2059 - accuracy: 0.9482 - val_loss: 0.3972 - val_accuracy: 0.9247 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1980 - accuracy: 0.9458 - val_loss: 0.2653 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9731 - val_loss: 0.2222 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1402 - accuracy: 0.9604 - val_loss: 0.2944 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 25: 285.39 sec +Time taken for epoch(SUBo) 25: 236.55 sec +<---------------------------------------|Epoch [25] END|---------------------------------------> + +Epoch: 26/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.005000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2292 - accuracy: 0.9341 - val_loss: 0.2645 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2017 - accuracy: 0.9414 - val_loss: 0.2456 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.2125 - accuracy: 0.9341 - val_loss: 0.3309 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1715 - accuracy: 0.9536 - val_loss: 0.2653 - val_accuracy: 0.9183 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9658 - val_loss: 0.2156 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9741 - val_loss: 0.2134 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 26: 285.12 sec +Time taken for epoch(SUBo) 26: 236.07 sec +<---------------------------------------|Epoch [26] END|---------------------------------------> + +Epoch: 27/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.004500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1868 - accuracy: 0.9463 - val_loss: 0.1853 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2043 - accuracy: 0.9351 - val_loss: 0.3479 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1911 - accuracy: 0.9453 - val_loss: 0.2130 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1510 - accuracy: 0.9600 - val_loss: 0.2097 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1655 - accuracy: 0.9561 - val_loss: 0.1885 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1346 - accuracy: 0.9692 - val_loss: 0.1939 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9471153616905212. Not saving model. +Model loss did not improve from 0.1857679933309555. Not saving model. +Time taken for epoch(FULL) 27: 285.71 sec +Time taken for epoch(SUBo) 27: 236.48 sec +<---------------------------------------|Epoch [27] END|---------------------------------------> + +Epoch: 28/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.004000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2180 - accuracy: 0.9360 - val_loss: 0.1893 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2060 - accuracy: 0.9385 - val_loss: 0.1826 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1867 - accuracy: 0.9448 - val_loss: 0.1701 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1611 - accuracy: 0.9614 - val_loss: 0.1821 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1444 - accuracy: 0.9609 - val_loss: 0.1652 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1409 - accuracy: 0.9644 - val_loss: 0.1546 - val_accuracy: 0.9567 +Subset training done. +Improved model accuracy from 0.9471153616905212 to 0.9567307829856873. Saving model. +Improved model loss from 0.1857679933309555 to 0.15460731089115143. Saving model. +Time taken for epoch(FULL) 28: 288.65 sec +Time taken for epoch(SUBo) 28: 236.43 sec +<---------------------------------------|Epoch [28] END|---------------------------------------> + +Epoch: 29/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.003500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1936 - accuracy: 0.9404 - val_loss: 0.1560 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1892 - accuracy: 0.9390 - val_loss: 0.1654 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1752 - accuracy: 0.9541 - val_loss: 0.2738 - val_accuracy: 0.8926 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1570 - accuracy: 0.9561 - val_loss: 0.1721 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1441 - accuracy: 0.9639 - val_loss: 0.1639 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1111 - accuracy: 0.9692 - val_loss: 0.1661 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 29: 285.51 sec +Time taken for epoch(SUBo) 29: 236.35 sec +<---------------------------------------|Epoch [29] END|---------------------------------------> + +Epoch: 30/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.003000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1650 - accuracy: 0.9531 - val_loss: 0.1881 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1823 - accuracy: 0.9468 - val_loss: 0.2431 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1812 - accuracy: 0.9473 - val_loss: 0.1803 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1608 - accuracy: 0.9546 - val_loss: 0.1606 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9609 - val_loss: 0.1624 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1155 - accuracy: 0.9702 - val_loss: 0.1665 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 30: 285.48 sec +Time taken for epoch(SUBo) 30: 236.40 sec +<---------------------------------------|Epoch [30] END|---------------------------------------> + +Epoch: 31/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.002500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1981 - accuracy: 0.9370 - val_loss: 0.1560 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1661 - accuracy: 0.9482 - val_loss: 0.1612 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1624 - accuracy: 0.9517 - val_loss: 0.1743 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1685 - accuracy: 0.9517 - val_loss: 0.1903 - val_accuracy: 0.9247 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1254 - accuracy: 0.9644 - val_loss: 0.1866 - val_accuracy: 0.9231 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9707 - val_loss: 0.1807 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 31: 285.66 sec +Time taken for epoch(SUBo) 31: 236.69 sec +<---------------------------------------|Epoch [31] END|---------------------------------------> + +Epoch: 32/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.002000]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1669 - accuracy: 0.9502 - val_loss: 0.1911 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1540 - accuracy: 0.9531 - val_loss: 0.1633 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9624 - val_loss: 0.1597 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1643 - accuracy: 0.9551 - val_loss: 0.1712 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9585 - val_loss: 0.1951 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1076 - accuracy: 0.9658 - val_loss: 0.1953 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 32: 285.69 sec +Time taken for epoch(SUBo) 32: 236.38 sec +<---------------------------------------|Epoch [32] END|---------------------------------------> + +Epoch: 33/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1745 - accuracy: 0.9463 - val_loss: 0.1852 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1641 - accuracy: 0.9512 - val_loss: 0.1889 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1578 - accuracy: 0.9512 - val_loss: 0.1950 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1493 - accuracy: 0.9507 - val_loss: 0.1669 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.1736 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1185 - accuracy: 0.9658 - val_loss: 0.1680 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15460731089115143. Not saving model. +Time taken for epoch(FULL) 33: 286.31 sec +Time taken for epoch(SUBo) 33: 236.60 sec +<---------------------------------------|Epoch [33] END|---------------------------------------> + +Epoch: 34/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1615 - accuracy: 0.9521 - val_loss: 0.1627 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1649 - accuracy: 0.9521 - val_loss: 0.2083 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1949 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1419 - accuracy: 0.9517 - val_loss: 0.1563 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1317 - accuracy: 0.9565 - val_loss: 0.1606 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1158 - accuracy: 0.9688 - val_loss: 0.1512 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Improved model loss from 0.15460731089115143 to 0.15118563175201416. Saving model. +Time taken for epoch(FULL) 34: 287.71 sec +Time taken for epoch(SUBo) 34: 236.71 sec +<---------------------------------------|Epoch [34] END|---------------------------------------> + +Epoch: 35/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1739 - accuracy: 0.9443 - val_loss: 0.1441 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2022 - accuracy: 0.9336 - val_loss: 0.1491 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1754 - accuracy: 0.9458 - val_loss: 0.1782 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1629 - accuracy: 0.9458 - val_loss: 0.1656 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1582 - accuracy: 0.9546 - val_loss: 0.1640 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9531 - val_loss: 0.1650 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 35: 287.73 sec +Time taken for epoch(SUBo) 35: 237.55 sec +<---------------------------------------|Epoch [35] END|---------------------------------------> + +Epoch: 36/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9526 - val_loss: 0.1498 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1602 - accuracy: 0.9468 - val_loss: 0.1686 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9521 - val_loss: 0.1585 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1418 - accuracy: 0.9561 - val_loss: 0.1683 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9604 - val_loss: 0.1843 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1206 - accuracy: 0.9644 - val_loss: 0.1951 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 36: 287.50 sec +Time taken for epoch(SUBo) 36: 237.39 sec +<---------------------------------------|Epoch [36] END|---------------------------------------> + +Epoch: 37/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1843 - accuracy: 0.9414 - val_loss: 0.1578 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1683 - accuracy: 0.9497 - val_loss: 0.1731 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1612 - accuracy: 0.9463 - val_loss: 0.2032 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1507 - accuracy: 0.9521 - val_loss: 0.1985 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1510 - accuracy: 0.9590 - val_loss: 0.1618 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1361 - accuracy: 0.9595 - val_loss: 0.1653 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 37: 287.80 sec +Time taken for epoch(SUBo) 37: 237.45 sec +<---------------------------------------|Epoch [37] END|---------------------------------------> + +Epoch: 38/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1649 - accuracy: 0.9487 - val_loss: 0.1677 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1775 - accuracy: 0.9438 - val_loss: 0.1582 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1564 - accuracy: 0.9526 - val_loss: 0.1516 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1513 - accuracy: 0.9541 - val_loss: 0.1526 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1408 - accuracy: 0.9595 - val_loss: 0.1522 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1186 - accuracy: 0.9634 - val_loss: 0.1668 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 38: 287.81 sec +Time taken for epoch(SUBo) 38: 237.65 sec +<---------------------------------------|Epoch [38] END|---------------------------------------> + +Epoch: 39/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1748 - accuracy: 0.9414 - val_loss: 0.1468 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9521 - val_loss: 0.1940 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1527 - accuracy: 0.9536 - val_loss: 0.1679 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1440 - accuracy: 0.9521 - val_loss: 0.2192 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1304 - accuracy: 0.9570 - val_loss: 0.1655 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1088 - accuracy: 0.9697 - val_loss: 0.1865 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 39: 288.44 sec +Time taken for epoch(SUBo) 39: 237.93 sec +<---------------------------------------|Epoch [39] END|---------------------------------------> + +Epoch: 40/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1613 - accuracy: 0.9502 - val_loss: 0.1476 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1465 - accuracy: 0.9590 - val_loss: 0.1613 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1391 - accuracy: 0.9609 - val_loss: 0.1533 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1231 - accuracy: 0.9648 - val_loss: 0.1602 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1393 - accuracy: 0.9609 - val_loss: 0.1537 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1065 - accuracy: 0.9727 - val_loss: 0.1562 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 40: 287.78 sec +Time taken for epoch(SUBo) 40: 237.78 sec +<---------------------------------------|Epoch [40] END|---------------------------------------> + +Epoch: 41/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1631 - accuracy: 0.9478 - val_loss: 0.1572 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1542 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1441 - accuracy: 0.9531 - val_loss: 0.1653 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9614 - val_loss: 0.1968 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9575 - val_loss: 0.1599 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1870 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 41: 287.23 sec +Time taken for epoch(SUBo) 41: 237.14 sec +<---------------------------------------|Epoch [41] END|---------------------------------------> + +Epoch: 42/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1298 - accuracy: 0.9531 - val_loss: 0.2101 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1359 - accuracy: 0.9565 - val_loss: 0.1721 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1334 - accuracy: 0.9614 - val_loss: 0.1705 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9678 - val_loss: 0.1819 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1071 - accuracy: 0.9678 - val_loss: 0.1882 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0998 - accuracy: 0.9712 - val_loss: 0.2143 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 42: 287.96 sec +Time taken for epoch(SUBo) 42: 237.29 sec +<---------------------------------------|Epoch [42] END|---------------------------------------> + +Epoch: 43/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1517 - accuracy: 0.9556 - val_loss: 0.1814 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.2212 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2157 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2624 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1266 - accuracy: 0.9648 - val_loss: 0.2113 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2185 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 43: 288.58 sec +Time taken for epoch(SUBo) 43: 237.71 sec +<---------------------------------------|Epoch [43] END|---------------------------------------> + +Epoch: 44/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1549 - accuracy: 0.9507 - val_loss: 0.1907 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1406 - accuracy: 0.9561 - val_loss: 0.1945 - val_accuracy: 0.9279 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9556 - val_loss: 0.2094 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1300 - accuracy: 0.9619 - val_loss: 0.2000 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1146 - accuracy: 0.9678 - val_loss: 0.2591 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1207 - accuracy: 0.9648 - val_loss: 0.2343 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 44: 288.18 sec +Time taken for epoch(SUBo) 44: 237.53 sec +<---------------------------------------|Epoch [44] END|---------------------------------------> + +Epoch: 45/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1691 - accuracy: 0.9507 - val_loss: 0.1829 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1517 - accuracy: 0.9570 - val_loss: 0.1635 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1363 - accuracy: 0.9609 - val_loss: 0.2010 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1235 - accuracy: 0.9624 - val_loss: 0.1995 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1312 - accuracy: 0.9600 - val_loss: 0.2820 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1434 - accuracy: 0.9512 - val_loss: 0.2766 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 45: 288.43 sec +Time taken for epoch(SUBo) 45: 237.92 sec +<---------------------------------------|Epoch [45] END|---------------------------------------> + +Epoch: 46/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1684 - accuracy: 0.9468 - val_loss: 0.3024 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1606 - accuracy: 0.9478 - val_loss: 0.3133 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1545 - accuracy: 0.9585 - val_loss: 0.2165 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1639 - accuracy: 0.9468 - val_loss: 0.2465 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.2787 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1406 - accuracy: 0.9551 - val_loss: 0.2559 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 46: 288.00 sec +Time taken for epoch(SUBo) 46: 237.42 sec +<---------------------------------------|Epoch [46] END|---------------------------------------> + +Epoch: 47/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1874 - accuracy: 0.9414 - val_loss: 0.2024 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1816 - accuracy: 0.9487 - val_loss: 0.2076 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1674 - accuracy: 0.9434 - val_loss: 0.3245 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1442 - accuracy: 0.9604 - val_loss: 0.2564 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1221 - accuracy: 0.9609 - val_loss: 0.3057 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1317 - accuracy: 0.9556 - val_loss: 0.2604 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 47: 287.69 sec +Time taken for epoch(SUBo) 47: 236.59 sec +<---------------------------------------|Epoch [47] END|---------------------------------------> + +Epoch: 48/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1541 - accuracy: 0.9453 - val_loss: 0.2779 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1480 - accuracy: 0.9526 - val_loss: 0.2490 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1341 - accuracy: 0.9614 - val_loss: 0.2237 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1366 - accuracy: 0.9570 - val_loss: 0.2314 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2416 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1106 - accuracy: 0.9644 - val_loss: 0.2330 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 48: 286.84 sec +Time taken for epoch(SUBo) 48: 236.62 sec +<---------------------------------------|Epoch [48] END|---------------------------------------> + +Epoch: 49/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1551 - accuracy: 0.9561 - val_loss: 0.2252 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9570 - val_loss: 0.2131 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1401 - accuracy: 0.9580 - val_loss: 0.1908 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1271 - accuracy: 0.9639 - val_loss: 0.2179 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1260 - accuracy: 0.9634 - val_loss: 0.2022 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1087 - accuracy: 0.9717 - val_loss: 0.1932 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 49: 286.33 sec +Time taken for epoch(SUBo) 49: 236.60 sec +<---------------------------------------|Epoch [49] END|---------------------------------------> + +Epoch: 50/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1449 - accuracy: 0.9521 - val_loss: 0.1748 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1448 - accuracy: 0.9507 - val_loss: 0.2003 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1395 - accuracy: 0.9521 - val_loss: 0.2190 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9390 - val_loss: 0.2207 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9521 - val_loss: 0.2131 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1572 - accuracy: 0.9478 - val_loss: 0.2142 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 50: 286.59 sec +Time taken for epoch(SUBo) 50: 236.86 sec +<---------------------------------------|Epoch [50] END|---------------------------------------> + +Epoch: 51/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1601 - accuracy: 0.9497 - val_loss: 0.1783 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1519 - accuracy: 0.9517 - val_loss: 0.2485 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1687 - accuracy: 0.9521 - val_loss: 0.2295 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1445 - accuracy: 0.9600 - val_loss: 0.2580 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1283 - accuracy: 0.9619 - val_loss: 0.2596 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1248 - accuracy: 0.9624 - val_loss: 0.2709 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 51: 286.23 sec +Time taken for epoch(SUBo) 51: 236.38 sec +<---------------------------------------|Epoch [51] END|---------------------------------------> + +Epoch: 52/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1478 - accuracy: 0.9512 - val_loss: 0.2317 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1364 - accuracy: 0.9614 - val_loss: 0.2805 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9634 - val_loss: 0.2886 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2800 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1081 - accuracy: 0.9712 - val_loss: 0.2406 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9702 - val_loss: 0.2587 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 52: 286.99 sec +Time taken for epoch(SUBo) 52: 236.83 sec +<---------------------------------------|Epoch [52] END|---------------------------------------> + +Epoch: 53/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2206 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1726 - accuracy: 0.9468 - val_loss: 0.2399 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1522 - accuracy: 0.9546 - val_loss: 0.2213 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1520 - accuracy: 0.9546 - val_loss: 0.1943 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1258 - accuracy: 0.9580 - val_loss: 0.1851 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9541 - val_loss: 0.1898 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 53: 287.29 sec +Time taken for epoch(SUBo) 53: 237.19 sec +<---------------------------------------|Epoch [53] END|---------------------------------------> + +Epoch: 54/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1649 - accuracy: 0.9429 - val_loss: 0.2123 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1791 - accuracy: 0.9424 - val_loss: 0.2041 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1739 - accuracy: 0.9429 - val_loss: 0.2438 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1467 - accuracy: 0.9521 - val_loss: 0.2370 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1384 - accuracy: 0.9541 - val_loss: 0.3072 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1439 - accuracy: 0.9580 - val_loss: 0.2901 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 54: 287.00 sec +Time taken for epoch(SUBo) 54: 236.97 sec +<---------------------------------------|Epoch [54] END|---------------------------------------> + +Epoch: 55/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1734 - accuracy: 0.9438 - val_loss: 0.2456 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1551 - accuracy: 0.9512 - val_loss: 0.2227 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9468 - val_loss: 0.2150 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1365 - accuracy: 0.9600 - val_loss: 0.1964 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1341 - accuracy: 0.9595 - val_loss: 0.2038 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.2228 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 55: 286.51 sec +Time taken for epoch(SUBo) 55: 236.75 sec +<---------------------------------------|Epoch [55] END|---------------------------------------> + +Epoch: 56/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1372 - accuracy: 0.9575 - val_loss: 0.2215 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1534 - accuracy: 0.9541 - val_loss: 0.2516 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9629 - val_loss: 0.2329 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9673 - val_loss: 0.2124 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1028 - accuracy: 0.9727 - val_loss: 0.2299 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0982 - accuracy: 0.9736 - val_loss: 0.2280 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 56: 286.73 sec +Time taken for epoch(SUBo) 56: 237.12 sec +<---------------------------------------|Epoch [56] END|---------------------------------------> + +Epoch: 57/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 154ms/step - loss: 0.1279 - accuracy: 0.9604 - val_loss: 0.1954 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9590 - val_loss: 0.2062 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1403 - accuracy: 0.9580 - val_loss: 0.1679 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1308 - accuracy: 0.9570 - val_loss: 0.1776 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1117 - accuracy: 0.9648 - val_loss: 0.1890 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1019 - accuracy: 0.9717 - val_loss: 0.1922 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 57: 286.14 sec +Time taken for epoch(SUBo) 57: 235.92 sec +<---------------------------------------|Epoch [57] END|---------------------------------------> + +Epoch: 58/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1579 - accuracy: 0.9468 - val_loss: 0.1934 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1771 - accuracy: 0.9409 - val_loss: 0.1981 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1471 - accuracy: 0.9561 - val_loss: 0.2460 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1365 - accuracy: 0.9595 - val_loss: 0.1832 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9536 - val_loss: 0.1711 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1317 - accuracy: 0.9609 - val_loss: 0.1742 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 58: 287.08 sec +Time taken for epoch(SUBo) 58: 236.57 sec +<---------------------------------------|Epoch [58] END|---------------------------------------> + +Epoch: 59/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1481 - accuracy: 0.9551 - val_loss: 0.1874 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1438 - accuracy: 0.9546 - val_loss: 0.1799 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1512 - accuracy: 0.9575 - val_loss: 0.1774 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1369 - accuracy: 0.9595 - val_loss: 0.1793 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1269 - accuracy: 0.9663 - val_loss: 0.1713 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9688 - val_loss: 0.1879 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 59: 286.52 sec +Time taken for epoch(SUBo) 59: 237.12 sec +<---------------------------------------|Epoch [59] END|---------------------------------------> + +Epoch: 60/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9531 - val_loss: 0.1852 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1386 - accuracy: 0.9575 - val_loss: 0.1995 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9663 - val_loss: 0.2111 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1169 - accuracy: 0.9663 - val_loss: 0.2195 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1004 - accuracy: 0.9717 - val_loss: 0.2351 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9668 - val_loss: 0.2677 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 60: 287.80 sec +Time taken for epoch(SUBo) 60: 237.20 sec +<---------------------------------------|Epoch [60] END|---------------------------------------> + +Epoch: 61/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1434 - accuracy: 0.9551 - val_loss: 0.2024 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1346 - accuracy: 0.9604 - val_loss: 0.2110 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1218 - accuracy: 0.9644 - val_loss: 0.1917 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9629 - val_loss: 0.2180 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1204 - accuracy: 0.9639 - val_loss: 0.1932 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1012 - accuracy: 0.9683 - val_loss: 0.1964 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 61: 288.27 sec +Time taken for epoch(SUBo) 61: 237.72 sec +<---------------------------------------|Epoch [61] END|---------------------------------------> + +Epoch: 62/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1389 - accuracy: 0.9575 - val_loss: 0.2335 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1295 - accuracy: 0.9561 - val_loss: 0.2828 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1223 - accuracy: 0.9619 - val_loss: 0.2642 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1103 - accuracy: 0.9673 - val_loss: 0.2734 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.2583 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1019 - accuracy: 0.9707 - val_loss: 0.2563 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 62: 288.25 sec +Time taken for epoch(SUBo) 62: 237.52 sec +<---------------------------------------|Epoch [62] END|---------------------------------------> + +Epoch: 63/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1588 - accuracy: 0.9517 - val_loss: 0.2404 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1392 - accuracy: 0.9624 - val_loss: 0.1892 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.1851 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9634 - val_loss: 0.1914 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1298 - accuracy: 0.9619 - val_loss: 0.2004 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9673 - val_loss: 0.1989 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 63: 288.35 sec +Time taken for epoch(SUBo) 63: 237.48 sec +<---------------------------------------|Epoch [63] END|---------------------------------------> + +Epoch: 64/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1376 - accuracy: 0.9556 - val_loss: 0.1802 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1370 - accuracy: 0.9575 - val_loss: 0.2342 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1335 - accuracy: 0.9604 - val_loss: 0.1916 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1462 - accuracy: 0.9580 - val_loss: 0.1591 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1061 - accuracy: 0.9663 - val_loss: 0.2386 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9688 - val_loss: 0.2423 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 64: 288.62 sec +Time taken for epoch(SUBo) 64: 237.68 sec +<---------------------------------------|Epoch [64] END|---------------------------------------> + +Epoch: 65/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1365 - accuracy: 0.9556 - val_loss: 0.2579 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1324 - accuracy: 0.9595 - val_loss: 0.2196 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9619 - val_loss: 0.2640 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1136 - accuracy: 0.9663 - val_loss: 0.2262 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1052 - accuracy: 0.9692 - val_loss: 0.2272 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0993 - accuracy: 0.9697 - val_loss: 0.2402 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 65: 288.68 sec +Time taken for epoch(SUBo) 65: 238.01 sec +<---------------------------------------|Epoch [65] END|---------------------------------------> + +Epoch: 66/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9590 - val_loss: 0.2096 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1272 - accuracy: 0.9604 - val_loss: 0.2505 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1286 - accuracy: 0.9561 - val_loss: 0.2210 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9683 - val_loss: 0.1834 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1793 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1017 - accuracy: 0.9697 - val_loss: 0.2070 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 66: 288.12 sec +Time taken for epoch(SUBo) 66: 237.92 sec +<---------------------------------------|Epoch [66] END|---------------------------------------> + +Epoch: 67/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1517 - accuracy: 0.9565 - val_loss: 0.1927 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1471 - accuracy: 0.9502 - val_loss: 0.2064 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2286 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1262 - accuracy: 0.9619 - val_loss: 0.1877 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1157 - accuracy: 0.9639 - val_loss: 0.1992 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9658 - val_loss: 0.1889 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 67: 288.09 sec +Time taken for epoch(SUBo) 67: 237.40 sec +<---------------------------------------|Epoch [67] END|---------------------------------------> + +Epoch: 68/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1311 - accuracy: 0.9556 - val_loss: 0.1958 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1119 - accuracy: 0.9644 - val_loss: 0.2010 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1263 - accuracy: 0.9595 - val_loss: 0.1595 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9595 - val_loss: 0.1492 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1132 - accuracy: 0.9639 - val_loss: 0.1464 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1003 - accuracy: 0.9712 - val_loss: 0.1529 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 68: 288.54 sec +Time taken for epoch(SUBo) 68: 237.57 sec +<---------------------------------------|Epoch [68] END|---------------------------------------> + +Epoch: 69/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1554 - accuracy: 0.9546 - val_loss: 0.1697 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1428 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1287 - accuracy: 0.9629 - val_loss: 0.2158 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1152 - accuracy: 0.9634 - val_loss: 0.1788 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1029 - accuracy: 0.9697 - val_loss: 0.1732 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0991 - accuracy: 0.9722 - val_loss: 0.1837 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 69: 287.94 sec +Time taken for epoch(SUBo) 69: 237.46 sec +<---------------------------------------|Epoch [69] END|---------------------------------------> + +Epoch: 70/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1386 - accuracy: 0.9648 - val_loss: 0.1742 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.2681 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1782 - accuracy: 0.9482 - val_loss: 0.3058 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1468 - accuracy: 0.9526 - val_loss: 0.2156 - val_accuracy: 0.9327 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1217 - accuracy: 0.9634 - val_loss: 0.1891 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1098 - accuracy: 0.9668 - val_loss: 0.1983 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9567307829856873. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 70: 288.24 sec +Time taken for epoch(SUBo) 70: 237.80 sec +<---------------------------------------|Epoch [70] END|---------------------------------------> + +Epoch: 71/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1711 - accuracy: 0.9468 - val_loss: 0.1688 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1528 - accuracy: 0.9546 - val_loss: 0.1514 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9609 - val_loss: 0.1770 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9585 - val_loss: 0.1579 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1195 - accuracy: 0.9653 - val_loss: 0.1543 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1292 - accuracy: 0.9609 - val_loss: 0.1538 - val_accuracy: 0.9599 +Subset training done. +Improved model accuracy from 0.9567307829856873 to 0.9599359035491943. Saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 71: 289.74 sec +Time taken for epoch(SUBo) 71: 237.66 sec +<---------------------------------------|Epoch [71] END|---------------------------------------> + +Epoch: 72/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1505 - accuracy: 0.9521 - val_loss: 0.1529 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1589 - accuracy: 0.9512 - val_loss: 0.1426 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1484 - accuracy: 0.9546 - val_loss: 0.1592 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1210 - accuracy: 0.9658 - val_loss: 0.1791 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1154 - accuracy: 0.9673 - val_loss: 0.1634 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 72: 288.91 sec +Time taken for epoch(SUBo) 72: 237.07 sec +<---------------------------------------|Epoch [72] END|---------------------------------------> + +Epoch: 73/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1304 - accuracy: 0.9609 - val_loss: 0.1894 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1949 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1392 - accuracy: 0.9526 - val_loss: 0.2177 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.2006 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1074 - accuracy: 0.9746 - val_loss: 0.2530 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9692 - val_loss: 0.2516 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 73: 286.60 sec +Time taken for epoch(SUBo) 73: 236.85 sec +<---------------------------------------|Epoch [73] END|---------------------------------------> + +Epoch: 74/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1103 - accuracy: 0.9653 - val_loss: 0.2006 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1242 - accuracy: 0.9600 - val_loss: 0.2702 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1351 - accuracy: 0.9580 - val_loss: 0.2475 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9731 - val_loss: 0.2133 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2043 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 150ms/step - loss: 0.0768 - accuracy: 0.9780 - val_loss: 0.2014 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 74: 287.09 sec +Time taken for epoch(SUBo) 74: 237.29 sec +<---------------------------------------|Epoch [74] END|---------------------------------------> + +Epoch: 75/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1430 - accuracy: 0.9546 - val_loss: 0.2063 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1306 - accuracy: 0.9619 - val_loss: 0.1984 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1205 - accuracy: 0.9663 - val_loss: 0.1844 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1186 - accuracy: 0.9653 - val_loss: 0.1739 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0999 - accuracy: 0.9727 - val_loss: 0.1955 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0930 - accuracy: 0.9731 - val_loss: 0.1780 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 75: 287.36 sec +Time taken for epoch(SUBo) 75: 237.36 sec +<---------------------------------------|Epoch [75] END|---------------------------------------> + +Epoch: 76/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1332 - accuracy: 0.9561 - val_loss: 0.1757 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1358 - accuracy: 0.9590 - val_loss: 0.1649 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1475 - accuracy: 0.9546 - val_loss: 0.1689 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9570 - val_loss: 0.1557 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1127 - accuracy: 0.9619 - val_loss: 0.1633 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0955 - accuracy: 0.9717 - val_loss: 0.1716 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 76: 286.87 sec +Time taken for epoch(SUBo) 76: 237.21 sec +<---------------------------------------|Epoch [76] END|---------------------------------------> + +Epoch: 77/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1613 - accuracy: 0.9429 - val_loss: 0.1702 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1557 - accuracy: 0.9463 - val_loss: 0.1623 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1399 - accuracy: 0.9546 - val_loss: 0.2084 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1245 - accuracy: 0.9619 - val_loss: 0.2221 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1156 - accuracy: 0.9624 - val_loss: 0.2435 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9683 - val_loss: 0.2508 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 77: 286.93 sec +Time taken for epoch(SUBo) 77: 236.81 sec +<---------------------------------------|Epoch [77] END|---------------------------------------> + +Epoch: 78/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1258 - accuracy: 0.9609 - val_loss: 0.1880 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1473 - accuracy: 0.9507 - val_loss: 0.1763 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1170 - accuracy: 0.9658 - val_loss: 0.2302 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1490 - accuracy: 0.9551 - val_loss: 0.1573 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1236 - accuracy: 0.9585 - val_loss: 0.1819 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1150 - accuracy: 0.9639 - val_loss: 0.1925 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 78: 287.03 sec +Time taken for epoch(SUBo) 78: 237.13 sec +<---------------------------------------|Epoch [78] END|---------------------------------------> + +Epoch: 79/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1394 - accuracy: 0.9570 - val_loss: 0.1949 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1345 - accuracy: 0.9604 - val_loss: 0.2434 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.2313 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1145 - accuracy: 0.9648 - val_loss: 0.2336 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1077 - accuracy: 0.9707 - val_loss: 0.2261 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1020 - accuracy: 0.9688 - val_loss: 0.2249 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 79: 286.93 sec +Time taken for epoch(SUBo) 79: 237.31 sec +<---------------------------------------|Epoch [79] END|---------------------------------------> + +Epoch: 80/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1493 - accuracy: 0.9512 - val_loss: 0.2335 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1416 - accuracy: 0.9517 - val_loss: 0.2401 - val_accuracy: 0.9183 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2689 - accuracy: 0.9048 - val_loss: 0.4998 - val_accuracy: 0.7821 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.2924 - accuracy: 0.8955 - val_loss: 0.4549 - val_accuracy: 0.8782 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2427 - accuracy: 0.9136 - val_loss: 0.3899 - val_accuracy: 0.8830 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.2071 - accuracy: 0.9292 - val_loss: 0.3938 - val_accuracy: 0.8830 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 80: 287.37 sec +Time taken for epoch(SUBo) 80: 237.50 sec +<---------------------------------------|Epoch [80] END|---------------------------------------> + +Epoch: 81/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.2117 - accuracy: 0.9272 - val_loss: 0.3888 - val_accuracy: 0.8942 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.2039 - accuracy: 0.9326 - val_loss: 0.4718 - val_accuracy: 0.9038 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1797 - accuracy: 0.9424 - val_loss: 0.4449 - val_accuracy: 0.9087 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1627 - accuracy: 0.9512 - val_loss: 0.2830 - val_accuracy: 0.9151 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1495 - accuracy: 0.9565 - val_loss: 0.3565 - val_accuracy: 0.9167 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9541 - val_loss: 0.3372 - val_accuracy: 0.9199 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 81: 287.21 sec +Time taken for epoch(SUBo) 81: 237.47 sec +<---------------------------------------|Epoch [81] END|---------------------------------------> + +Epoch: 82/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1753 - accuracy: 0.9424 - val_loss: 0.3639 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 38s 150ms/step - loss: 0.1803 - accuracy: 0.9429 - val_loss: 0.3132 - val_accuracy: 0.9215 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1485 - accuracy: 0.9565 - val_loss: 0.2975 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1447 - accuracy: 0.9575 - val_loss: 0.3335 - val_accuracy: 0.9247 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1446 - accuracy: 0.9561 - val_loss: 0.2650 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9653 - val_loss: 0.2362 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 82: 286.96 sec +Time taken for epoch(SUBo) 82: 236.90 sec +<---------------------------------------|Epoch [82] END|---------------------------------------> + +Epoch: 83/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1623 - accuracy: 0.9492 - val_loss: 0.2152 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1599 - accuracy: 0.9502 - val_loss: 0.2598 - val_accuracy: 0.9231 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1508 - accuracy: 0.9609 - val_loss: 0.2304 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1310 - accuracy: 0.9517 - val_loss: 0.2164 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9624 - val_loss: 0.2169 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1250 - accuracy: 0.9595 - val_loss: 0.2147 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 83: 287.52 sec +Time taken for epoch(SUBo) 83: 237.43 sec +<---------------------------------------|Epoch [83] END|---------------------------------------> + +Epoch: 84/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9595 - val_loss: 0.2386 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.1926 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1437 - accuracy: 0.9526 - val_loss: 0.2082 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1389 - accuracy: 0.9556 - val_loss: 0.2051 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1211 - accuracy: 0.9634 - val_loss: 0.1852 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1104 - accuracy: 0.9702 - val_loss: 0.1848 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 84: 286.91 sec +Time taken for epoch(SUBo) 84: 237.24 sec +<---------------------------------------|Epoch [84] END|---------------------------------------> + +Epoch: 85/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1612 - accuracy: 0.9478 - val_loss: 0.2066 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1647 - accuracy: 0.9448 - val_loss: 0.1899 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1606 - accuracy: 0.9448 - val_loss: 0.1948 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1336 - accuracy: 0.9561 - val_loss: 0.1954 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1293 - accuracy: 0.9575 - val_loss: 0.1911 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.1925 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 85: 288.09 sec +Time taken for epoch(SUBo) 85: 237.57 sec +<---------------------------------------|Epoch [85] END|---------------------------------------> + +Epoch: 86/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1545 - accuracy: 0.9507 - val_loss: 0.1890 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1532 - accuracy: 0.9517 - val_loss: 0.2042 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1454 - accuracy: 0.9492 - val_loss: 0.1683 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1330 - accuracy: 0.9604 - val_loss: 0.1693 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1233 - accuracy: 0.9604 - val_loss: 0.1930 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1207 - accuracy: 0.9619 - val_loss: 0.1804 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 86: 288.17 sec +Time taken for epoch(SUBo) 86: 237.64 sec +<---------------------------------------|Epoch [86] END|---------------------------------------> + +Epoch: 87/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1573 - accuracy: 0.9536 - val_loss: 0.1667 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1656 - accuracy: 0.9478 - val_loss: 0.1621 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1384 - accuracy: 0.9595 - val_loss: 0.1620 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1258 - accuracy: 0.9585 - val_loss: 0.1718 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1227 - accuracy: 0.9595 - val_loss: 0.1562 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1226 - accuracy: 0.9653 - val_loss: 0.1679 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 87: 288.63 sec +Time taken for epoch(SUBo) 87: 237.58 sec +<---------------------------------------|Epoch [87] END|---------------------------------------> + +Epoch: 88/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1496 - accuracy: 0.9502 - val_loss: 0.1901 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1700 - accuracy: 0.9399 - val_loss: 0.1543 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1560 - accuracy: 0.9546 - val_loss: 0.1877 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.1802 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1187 - accuracy: 0.9609 - val_loss: 0.1640 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1221 - accuracy: 0.9629 - val_loss: 0.1898 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 88: 289.56 sec +Time taken for epoch(SUBo) 88: 238.18 sec +<---------------------------------------|Epoch [88] END|---------------------------------------> + +Epoch: 89/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1682 - accuracy: 0.9497 - val_loss: 0.1799 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1313 - accuracy: 0.9580 - val_loss: 0.2257 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9585 - val_loss: 0.2209 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1873 - accuracy: 0.9399 - val_loss: 0.1585 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1725 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1436 - accuracy: 0.9580 - val_loss: 0.1682 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 89: 288.75 sec +Time taken for epoch(SUBo) 89: 238.29 sec +<---------------------------------------|Epoch [89] END|---------------------------------------> + +Epoch: 90/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1505 - accuracy: 0.9502 - val_loss: 0.1977 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1613 - accuracy: 0.9478 - val_loss: 0.1510 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1232 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1183 - accuracy: 0.9658 - val_loss: 0.1810 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9717 - val_loss: 0.1728 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1102 - accuracy: 0.9658 - val_loss: 0.1794 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 90: 288.69 sec +Time taken for epoch(SUBo) 90: 237.88 sec +<---------------------------------------|Epoch [90] END|---------------------------------------> + +Epoch: 91/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1654 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1286 - accuracy: 0.9604 - val_loss: 0.2092 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1339 - accuracy: 0.9604 - val_loss: 0.1610 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1106 - accuracy: 0.9668 - val_loss: 0.1881 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9688 - val_loss: 0.2103 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0968 - accuracy: 0.9741 - val_loss: 0.2091 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 91: 290.04 sec +Time taken for epoch(SUBo) 91: 238.32 sec +<---------------------------------------|Epoch [91] END|---------------------------------------> + +Epoch: 92/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1806 - accuracy: 0.9453 - val_loss: 0.1973 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1625 - accuracy: 0.9502 - val_loss: 0.1934 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1476 - accuracy: 0.9517 - val_loss: 0.1993 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1311 - accuracy: 0.9551 - val_loss: 0.1942 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1282 - accuracy: 0.9580 - val_loss: 0.1883 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1260 - accuracy: 0.9619 - val_loss: 0.1955 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 92: 288.96 sec +Time taken for epoch(SUBo) 92: 237.66 sec +<---------------------------------------|Epoch [92] END|---------------------------------------> + +Epoch: 93/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1499 - accuracy: 0.9473 - val_loss: 0.1841 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1426 - accuracy: 0.9507 - val_loss: 0.2240 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1467 - accuracy: 0.9600 - val_loss: 0.1832 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1411 - accuracy: 0.9531 - val_loss: 0.4701 - val_accuracy: 0.8910 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1303 - accuracy: 0.9600 - val_loss: 0.3182 - val_accuracy: 0.9103 +Epoch 6/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.1197 - accuracy: 0.9692 - val_loss: 0.2972 - val_accuracy: 0.9151 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 93: 290.33 sec +Time taken for epoch(SUBo) 93: 239.01 sec +<---------------------------------------|Epoch [93] END|---------------------------------------> + +Epoch: 94/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1449 - accuracy: 0.9536 - val_loss: 0.2477 - val_accuracy: 0.9295 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1695 - accuracy: 0.9458 - val_loss: 0.1876 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1408 - accuracy: 0.9526 - val_loss: 0.2062 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1405 - accuracy: 0.9531 - val_loss: 0.1995 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1120 - accuracy: 0.9692 - val_loss: 0.2110 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1060 - accuracy: 0.9712 - val_loss: 0.2041 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 94: 289.36 sec +Time taken for epoch(SUBo) 94: 238.47 sec +<---------------------------------------|Epoch [94] END|---------------------------------------> + +Epoch: 95/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1489 - accuracy: 0.9580 - val_loss: 0.1769 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1445 - accuracy: 0.9512 - val_loss: 0.1728 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1269 - accuracy: 0.9565 - val_loss: 0.2260 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1205 - accuracy: 0.9624 - val_loss: 0.1696 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1278 - accuracy: 0.9624 - val_loss: 0.1737 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1040 - accuracy: 0.9707 - val_loss: 0.1714 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 95: 289.33 sec +Time taken for epoch(SUBo) 95: 238.40 sec +<---------------------------------------|Epoch [95] END|---------------------------------------> + +Epoch: 96/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1672 - accuracy: 0.9492 - val_loss: 0.1677 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1451 - accuracy: 0.9565 - val_loss: 0.1917 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1325 - accuracy: 0.9614 - val_loss: 0.2296 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.2639 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0987 - accuracy: 0.9717 - val_loss: 0.3081 - val_accuracy: 0.9215 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1016 - accuracy: 0.9653 - val_loss: 0.2600 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 96: 288.89 sec +Time taken for epoch(SUBo) 96: 237.88 sec +<---------------------------------------|Epoch [96] END|---------------------------------------> + +Epoch: 97/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1431 - accuracy: 0.9463 - val_loss: 0.2139 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1526 - accuracy: 0.9492 - val_loss: 0.2200 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1348 - accuracy: 0.9575 - val_loss: 0.2507 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1261 - accuracy: 0.9575 - val_loss: 0.2652 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1126 - accuracy: 0.9683 - val_loss: 0.2767 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1255 - accuracy: 0.9604 - val_loss: 0.2645 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 97: 288.48 sec +Time taken for epoch(SUBo) 97: 237.23 sec +<---------------------------------------|Epoch [97] END|---------------------------------------> + +Epoch: 98/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9556 - val_loss: 0.2275 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1329 - accuracy: 0.9614 - val_loss: 0.2393 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1515 - accuracy: 0.9556 - val_loss: 0.3716 - val_accuracy: 0.9135 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1402 - accuracy: 0.9595 - val_loss: 0.3404 - val_accuracy: 0.9087 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1193 - accuracy: 0.9712 - val_loss: 0.2649 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1155 - accuracy: 0.9648 - val_loss: 0.2462 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 98: 287.65 sec +Time taken for epoch(SUBo) 98: 237.26 sec +<---------------------------------------|Epoch [98] END|---------------------------------------> + +Epoch: 99/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.2086 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1320 - accuracy: 0.9580 - val_loss: 0.2175 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1388 - accuracy: 0.9556 - val_loss: 0.1846 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1222 - accuracy: 0.9658 - val_loss: 0.2280 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1001 - accuracy: 0.9692 - val_loss: 0.2335 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0935 - accuracy: 0.9741 - val_loss: 0.2289 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 99: 287.39 sec +Time taken for epoch(SUBo) 99: 237.52 sec +<---------------------------------------|Epoch [99] END|---------------------------------------> + +Epoch: 100/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1431 - accuracy: 0.9580 - val_loss: 0.2261 - val_accuracy: 0.9247 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1552 - accuracy: 0.9536 - val_loss: 0.1987 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1221 - accuracy: 0.9619 - val_loss: 0.2009 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1274 - accuracy: 0.9604 - val_loss: 0.2111 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1100 - accuracy: 0.9692 - val_loss: 0.2023 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0975 - accuracy: 0.9736 - val_loss: 0.1899 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 100: 287.11 sec +Time taken for epoch(SUBo) 100: 237.35 sec +<---------------------------------------|Epoch [100] END|---------------------------------------> + +Epoch: 101/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1400 - accuracy: 0.9541 - val_loss: 0.2182 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1364 - accuracy: 0.9629 - val_loss: 0.1850 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1349 - accuracy: 0.9600 - val_loss: 0.2381 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.1880 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1042 - accuracy: 0.9692 - val_loss: 0.2007 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0986 - accuracy: 0.9731 - val_loss: 0.2144 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 101: 287.74 sec +Time taken for epoch(SUBo) 101: 237.93 sec +<---------------------------------------|Epoch [101] END|---------------------------------------> + +Epoch: 102/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1327 - accuracy: 0.9570 - val_loss: 0.2415 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1164 - accuracy: 0.9653 - val_loss: 0.2319 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1270 - accuracy: 0.9658 - val_loss: 0.2692 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1342 - accuracy: 0.9629 - val_loss: 0.2067 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.1845 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1135 - accuracy: 0.9688 - val_loss: 0.2075 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 102: 288.00 sec +Time taken for epoch(SUBo) 102: 237.50 sec +<---------------------------------------|Epoch [102] END|---------------------------------------> + +Epoch: 103/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1454 - accuracy: 0.9531 - val_loss: 0.2672 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1464 - accuracy: 0.9556 - val_loss: 0.1568 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1430 - accuracy: 0.9614 - val_loss: 0.2431 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1267 - accuracy: 0.9595 - val_loss: 0.1676 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1114 - accuracy: 0.9648 - val_loss: 0.1947 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1131 - accuracy: 0.9688 - val_loss: 0.1926 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 103: 287.73 sec +Time taken for epoch(SUBo) 103: 237.64 sec +<---------------------------------------|Epoch [103] END|---------------------------------------> + +Epoch: 104/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1319 - accuracy: 0.9551 - val_loss: 0.2187 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1435 - accuracy: 0.9565 - val_loss: 0.2262 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1363 - accuracy: 0.9556 - val_loss: 0.1924 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1133 - accuracy: 0.9678 - val_loss: 0.2607 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1085 - accuracy: 0.9717 - val_loss: 0.2344 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1026 - accuracy: 0.9673 - val_loss: 0.2418 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 104: 286.90 sec +Time taken for epoch(SUBo) 104: 237.53 sec +<---------------------------------------|Epoch [104] END|---------------------------------------> + +Epoch: 105/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1383 - accuracy: 0.9580 - val_loss: 0.2079 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1252 - accuracy: 0.9614 - val_loss: 0.1844 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1239 - accuracy: 0.9600 - val_loss: 0.2032 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1005 - accuracy: 0.9722 - val_loss: 0.2134 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9688 - val_loss: 0.1937 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0898 - accuracy: 0.9741 - val_loss: 0.1968 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 105: 287.02 sec +Time taken for epoch(SUBo) 105: 237.52 sec +<---------------------------------------|Epoch [105] END|---------------------------------------> + +Epoch: 106/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1352 - accuracy: 0.9575 - val_loss: 0.1525 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1355 - accuracy: 0.9570 - val_loss: 0.1892 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1163 - accuracy: 0.9692 - val_loss: 0.1639 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1066 - accuracy: 0.9678 - val_loss: 0.1816 - val_accuracy: 0.9583 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0869 - accuracy: 0.9736 - val_loss: 0.1968 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0897 - accuracy: 0.9741 - val_loss: 0.2022 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9599359035491943. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 106: 287.48 sec +Time taken for epoch(SUBo) 106: 237.69 sec +<---------------------------------------|Epoch [106] END|---------------------------------------> + +Epoch: 107/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 155ms/step - loss: 0.1194 - accuracy: 0.9644 - val_loss: 0.1767 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1113 - accuracy: 0.9668 - val_loss: 0.1995 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1046 - accuracy: 0.9663 - val_loss: 0.1818 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0864 - accuracy: 0.9746 - val_loss: 0.1969 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0910 - accuracy: 0.9722 - val_loss: 0.1441 - val_accuracy: 0.9663 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1109 - accuracy: 0.9653 - val_loss: 0.1590 - val_accuracy: 0.9696 +Subset training done. +Improved model accuracy from 0.9599359035491943 to 0.9695512652397156. Saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 107: 289.43 sec +Time taken for epoch(SUBo) 107: 237.56 sec +<---------------------------------------|Epoch [107] END|---------------------------------------> + +Epoch: 108/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1730 - accuracy: 0.9492 - val_loss: 0.1516 - val_accuracy: 0.9679 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1736 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.1854 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1192 - accuracy: 0.9658 - val_loss: 0.2242 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1115 - accuracy: 0.9663 - val_loss: 0.1922 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0976 - accuracy: 0.9722 - val_loss: 0.1996 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 108: 288.48 sec +Time taken for epoch(SUBo) 108: 238.16 sec +<---------------------------------------|Epoch [108] END|---------------------------------------> + +Epoch: 109/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1546 - accuracy: 0.9526 - val_loss: 0.1503 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1529 - accuracy: 0.9551 - val_loss: 0.1752 - val_accuracy: 0.9631 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.1519 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1593 - accuracy: 0.9492 - val_loss: 0.1787 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1744 - accuracy: 0.9434 - val_loss: 0.1705 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1520 - accuracy: 0.9502 - val_loss: 0.1609 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 109: 287.98 sec +Time taken for epoch(SUBo) 109: 238.06 sec +<---------------------------------------|Epoch [109] END|---------------------------------------> + +Epoch: 110/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1470 - accuracy: 0.9482 - val_loss: 0.1651 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1690 - accuracy: 0.9443 - val_loss: 0.2425 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1394 - accuracy: 0.9561 - val_loss: 0.1863 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1128 - accuracy: 0.9619 - val_loss: 0.1728 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1037 - accuracy: 0.9653 - val_loss: 0.1770 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0962 - accuracy: 0.9712 - val_loss: 0.1774 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 110: 288.95 sec +Time taken for epoch(SUBo) 110: 238.41 sec +<---------------------------------------|Epoch [110] END|---------------------------------------> + +Epoch: 111/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1625 - accuracy: 0.9487 - val_loss: 0.1659 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1540 - accuracy: 0.9556 - val_loss: 0.1548 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1736 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1230 - accuracy: 0.9639 - val_loss: 0.2110 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1110 - accuracy: 0.9717 - val_loss: 0.1803 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1079 - accuracy: 0.9688 - val_loss: 0.1742 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 111: 288.76 sec +Time taken for epoch(SUBo) 111: 238.28 sec +<---------------------------------------|Epoch [111] END|---------------------------------------> + +Epoch: 112/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1423 - accuracy: 0.9561 - val_loss: 0.1898 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1493 - accuracy: 0.9473 - val_loss: 0.2439 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1295 - accuracy: 0.9614 - val_loss: 0.2080 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1483 - accuracy: 0.9604 - val_loss: 0.2009 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.2107 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2227 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 112: 288.69 sec +Time taken for epoch(SUBo) 112: 237.84 sec +<---------------------------------------|Epoch [112] END|---------------------------------------> + +Epoch: 113/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1289 - accuracy: 0.9604 - val_loss: 0.1870 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1315 - accuracy: 0.9619 - val_loss: 0.1862 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1271 - accuracy: 0.9604 - val_loss: 0.1778 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1002 - accuracy: 0.9707 - val_loss: 0.1887 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0981 - accuracy: 0.9717 - val_loss: 0.2135 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0856 - accuracy: 0.9741 - val_loss: 0.2159 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 113: 289.27 sec +Time taken for epoch(SUBo) 113: 237.88 sec +<---------------------------------------|Epoch [113] END|---------------------------------------> + +Epoch: 114/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 43s 155ms/step - loss: 0.1358 - accuracy: 0.9595 - val_loss: 0.1854 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1183 - accuracy: 0.9644 - val_loss: 0.2141 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2008 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1108 - accuracy: 0.9639 - val_loss: 0.1953 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1022 - accuracy: 0.9663 - val_loss: 0.1951 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0806 - accuracy: 0.9775 - val_loss: 0.1923 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 114: 288.83 sec +Time taken for epoch(SUBo) 114: 237.68 sec +<---------------------------------------|Epoch [114] END|---------------------------------------> + +Epoch: 115/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1186 - accuracy: 0.9600 - val_loss: 0.2549 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1196 - accuracy: 0.9604 - val_loss: 0.2198 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1253 - accuracy: 0.9590 - val_loss: 0.2396 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1043 - accuracy: 0.9736 - val_loss: 0.2314 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.0960 - accuracy: 0.9712 - val_loss: 0.2056 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.0915 - accuracy: 0.9722 - val_loss: 0.2126 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 115: 289.11 sec +Time taken for epoch(SUBo) 115: 238.53 sec +<---------------------------------------|Epoch [115] END|---------------------------------------> + +Epoch: 116/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2195 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1368 - accuracy: 0.9595 - val_loss: 0.1903 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 39s 151ms/step - loss: 0.1198 - accuracy: 0.9614 - val_loss: 0.2051 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1856 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1008 - accuracy: 0.9702 - val_loss: 0.1742 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1027 - accuracy: 0.9717 - val_loss: 0.1697 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 116: 289.60 sec +Time taken for epoch(SUBo) 116: 239.21 sec +<---------------------------------------|Epoch [116] END|---------------------------------------> + +Epoch: 117/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.1718 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9580 - val_loss: 0.2046 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0925 - accuracy: 0.9722 - val_loss: 0.2292 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0834 - accuracy: 0.9751 - val_loss: 0.2023 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9727 - val_loss: 0.2151 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1000 - accuracy: 0.9722 - val_loss: 0.2206 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 117: 294.65 sec +Time taken for epoch(SUBo) 117: 244.16 sec +<---------------------------------------|Epoch [117] END|---------------------------------------> + +Epoch: 118/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1199 - accuracy: 0.9644 - val_loss: 0.2294 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1139 - accuracy: 0.9663 - val_loss: 0.1655 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1037 - accuracy: 0.9707 - val_loss: 0.1589 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0889 - accuracy: 0.9741 - val_loss: 0.2250 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0840 - accuracy: 0.9785 - val_loss: 0.1895 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0828 - accuracy: 0.9727 - val_loss: 0.1852 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15118563175201416. Not saving model. +Time taken for epoch(FULL) 118: 295.73 sec +Time taken for epoch(SUBo) 118: 244.43 sec +<---------------------------------------|Epoch [118] END|---------------------------------------> + +Epoch: 119/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1416 - accuracy: 0.9585 - val_loss: 0.1226 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1682 - accuracy: 0.9434 - val_loss: 0.1301 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1486 - accuracy: 0.9497 - val_loss: 0.1562 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1247 - accuracy: 0.9604 - val_loss: 0.1408 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1257 - accuracy: 0.9648 - val_loss: 0.1476 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1120 - accuracy: 0.9629 - val_loss: 0.1468 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.15118563175201416 to 0.146798238158226. Saving model. +Time taken for epoch(FULL) 119: 296.83 sec +Time taken for epoch(SUBo) 119: 244.81 sec +<---------------------------------------|Epoch [119] END|---------------------------------------> + +Epoch: 120/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1305 - accuracy: 0.9570 - val_loss: 0.1442 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1428 - accuracy: 0.9551 - val_loss: 0.1382 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9653 - val_loss: 0.1388 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1095 - accuracy: 0.9692 - val_loss: 0.1446 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0795 - accuracy: 0.9790 - val_loss: 0.1430 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1469 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 120: 295.05 sec +Time taken for epoch(SUBo) 120: 244.35 sec +<---------------------------------------|Epoch [120] END|---------------------------------------> + +Epoch: 121/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1313 - accuracy: 0.9609 - val_loss: 0.1539 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1415 - accuracy: 0.9541 - val_loss: 0.1573 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1153 - accuracy: 0.9717 - val_loss: 0.1778 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1108 - accuracy: 0.9683 - val_loss: 0.1774 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9697 - val_loss: 0.1738 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9727 - val_loss: 0.1716 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 121: 294.56 sec +Time taken for epoch(SUBo) 121: 244.29 sec +<---------------------------------------|Epoch [121] END|---------------------------------------> + +Epoch: 122/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1261 - accuracy: 0.9619 - val_loss: 0.1905 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9634 - val_loss: 0.1801 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1278 - accuracy: 0.9580 - val_loss: 0.2058 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1094 - accuracy: 0.9663 - val_loss: 0.2683 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1103 - accuracy: 0.9648 - val_loss: 0.1943 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 122: 295.23 sec +Time taken for epoch(SUBo) 122: 244.50 sec +<---------------------------------------|Epoch [122] END|---------------------------------------> + +Epoch: 123/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1423 - accuracy: 0.9570 - val_loss: 0.1759 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9624 - val_loss: 0.2300 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1347 - accuracy: 0.9600 - val_loss: 0.2434 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1360 - accuracy: 0.9565 - val_loss: 0.2215 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1029 - accuracy: 0.9678 - val_loss: 0.2258 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1030 - accuracy: 0.9658 - val_loss: 0.1975 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 123: 294.95 sec +Time taken for epoch(SUBo) 123: 244.32 sec +<---------------------------------------|Epoch [123] END|---------------------------------------> + +Epoch: 124/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1253 - accuracy: 0.9614 - val_loss: 0.2786 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9600 - val_loss: 0.2731 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1414 - accuracy: 0.9575 - val_loss: 0.2149 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1280 - accuracy: 0.9609 - val_loss: 0.2693 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1312 - accuracy: 0.9619 - val_loss: 0.2356 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.2349 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 124: 294.95 sec +Time taken for epoch(SUBo) 124: 244.30 sec +<---------------------------------------|Epoch [124] END|---------------------------------------> + +Epoch: 125/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1388 - accuracy: 0.9570 - val_loss: 0.2241 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1322 - accuracy: 0.9595 - val_loss: 0.2067 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1604 - accuracy: 0.9448 - val_loss: 0.2070 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1206 - accuracy: 0.9629 - val_loss: 0.1951 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1370 - accuracy: 0.9556 - val_loss: 0.1795 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9614 - val_loss: 0.1803 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 125: 296.66 sec +Time taken for epoch(SUBo) 125: 245.25 sec +<---------------------------------------|Epoch [125] END|---------------------------------------> + +Epoch: 126/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9443 - val_loss: 0.1636 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1469 - accuracy: 0.9531 - val_loss: 0.1743 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9600 - val_loss: 0.2001 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1122 - accuracy: 0.9634 - val_loss: 0.2148 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9692 - val_loss: 0.1990 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0975 - accuracy: 0.9727 - val_loss: 0.1967 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 126: 296.05 sec +Time taken for epoch(SUBo) 126: 244.69 sec +<---------------------------------------|Epoch [126] END|---------------------------------------> + +Epoch: 127/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1350 - accuracy: 0.9590 - val_loss: 0.2002 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1241 - accuracy: 0.9604 - val_loss: 0.1730 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1136 - accuracy: 0.9658 - val_loss: 0.2452 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0970 - accuracy: 0.9756 - val_loss: 0.2381 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0872 - accuracy: 0.9707 - val_loss: 0.2602 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0813 - accuracy: 0.9761 - val_loss: 0.2530 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 127: 295.58 sec +Time taken for epoch(SUBo) 127: 244.41 sec +<---------------------------------------|Epoch [127] END|---------------------------------------> + +Epoch: 128/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1365 - accuracy: 0.9521 - val_loss: 0.1995 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1338 - accuracy: 0.9575 - val_loss: 0.1957 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1184 - accuracy: 0.9609 - val_loss: 0.1864 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1086 - accuracy: 0.9712 - val_loss: 0.2123 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1137 - accuracy: 0.9653 - val_loss: 0.1765 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1008 - accuracy: 0.9697 - val_loss: 0.1619 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 128: 303.71 sec +Time taken for epoch(SUBo) 128: 244.02 sec +<---------------------------------------|Epoch [128] END|---------------------------------------> + +Epoch: 129/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1492 - accuracy: 0.9492 - val_loss: 0.1890 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1478 - accuracy: 0.9565 - val_loss: 0.1770 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1285 - accuracy: 0.9609 - val_loss: 0.1963 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1331 - accuracy: 0.9590 - val_loss: 0.1629 - val_accuracy: 0.9599 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1027 - accuracy: 0.9722 - val_loss: 0.1720 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0962 - accuracy: 0.9722 - val_loss: 0.1728 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 129: 304.31 sec +Time taken for epoch(SUBo) 129: 243.77 sec +<---------------------------------------|Epoch [129] END|---------------------------------------> + +Epoch: 130/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1344 - accuracy: 0.9595 - val_loss: 0.1606 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1276 - accuracy: 0.9624 - val_loss: 0.1791 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.1730 - val_accuracy: 0.9615 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1088 - accuracy: 0.9683 - val_loss: 0.1984 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1004 - accuracy: 0.9668 - val_loss: 0.2138 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1041 - accuracy: 0.9683 - val_loss: 0.1963 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 130: 301.26 sec +Time taken for epoch(SUBo) 130: 244.07 sec +<---------------------------------------|Epoch [130] END|---------------------------------------> + +Epoch: 131/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1314 - accuracy: 0.9614 - val_loss: 0.1733 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1437 - accuracy: 0.9556 - val_loss: 0.1815 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9639 - val_loss: 0.1522 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1197 - accuracy: 0.9644 - val_loss: 0.1593 - val_accuracy: 0.9615 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1065 - accuracy: 0.9707 - val_loss: 0.1619 - val_accuracy: 0.9615 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0984 - accuracy: 0.9697 - val_loss: 0.1596 - val_accuracy: 0.9631 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 131: 300.73 sec +Time taken for epoch(SUBo) 131: 244.44 sec +<---------------------------------------|Epoch [131] END|---------------------------------------> + +Epoch: 132/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1359 - accuracy: 0.9590 - val_loss: 0.1611 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1136 - accuracy: 0.9644 - val_loss: 0.1692 - val_accuracy: 0.9615 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1270 - accuracy: 0.9629 - val_loss: 0.2881 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1380 - accuracy: 0.9609 - val_loss: 0.1959 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1193 - accuracy: 0.9658 - val_loss: 0.2176 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1125 - accuracy: 0.9648 - val_loss: 0.2147 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 132: 298.91 sec +Time taken for epoch(SUBo) 132: 243.60 sec +<---------------------------------------|Epoch [132] END|---------------------------------------> + +Epoch: 133/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1469 - accuracy: 0.9521 - val_loss: 0.2294 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1442 - accuracy: 0.9580 - val_loss: 0.2275 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1246 - accuracy: 0.9619 - val_loss: 0.2881 - val_accuracy: 0.9295 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1150 - accuracy: 0.9673 - val_loss: 0.2647 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1132 - accuracy: 0.9648 - val_loss: 0.2474 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0897 - accuracy: 0.9751 - val_loss: 0.2609 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 133: 296.74 sec +Time taken for epoch(SUBo) 133: 242.77 sec +<---------------------------------------|Epoch [133] END|---------------------------------------> + +Epoch: 134/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1280 - accuracy: 0.9604 - val_loss: 0.2374 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1308 - accuracy: 0.9590 - val_loss: 0.2543 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1377 - accuracy: 0.9565 - val_loss: 0.2752 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1032 - accuracy: 0.9736 - val_loss: 0.2675 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9663 - val_loss: 0.2584 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0954 - accuracy: 0.9756 - val_loss: 0.2853 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 134: 297.41 sec +Time taken for epoch(SUBo) 134: 243.12 sec +<---------------------------------------|Epoch [134] END|---------------------------------------> + +Epoch: 135/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1629 - accuracy: 0.9482 - val_loss: 0.2191 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1362 - accuracy: 0.9575 - val_loss: 0.2275 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1400 - accuracy: 0.9570 - val_loss: 0.1914 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1302 - accuracy: 0.9639 - val_loss: 0.1995 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9653 - val_loss: 0.2003 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1085 - accuracy: 0.9697 - val_loss: 0.2064 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 135: 298.46 sec +Time taken for epoch(SUBo) 135: 243.19 sec +<---------------------------------------|Epoch [135] END|---------------------------------------> + +Epoch: 136/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1415 - accuracy: 0.9561 - val_loss: 0.1941 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1323 - accuracy: 0.9648 - val_loss: 0.2252 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1230 - accuracy: 0.9614 - val_loss: 0.1982 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2166 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9678 - val_loss: 0.2508 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0991 - accuracy: 0.9707 - val_loss: 0.2181 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 136: 300.20 sec +Time taken for epoch(SUBo) 136: 243.49 sec +<---------------------------------------|Epoch [136] END|---------------------------------------> + +Epoch: 137/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1551 - accuracy: 0.9531 - val_loss: 0.2049 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1405 - accuracy: 0.9546 - val_loss: 0.2349 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1254 - accuracy: 0.9595 - val_loss: 0.1758 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1130 - accuracy: 0.9634 - val_loss: 0.2124 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0963 - accuracy: 0.9736 - val_loss: 0.1902 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.1870 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 137: 300.02 sec +Time taken for epoch(SUBo) 137: 243.90 sec +<---------------------------------------|Epoch [137] END|---------------------------------------> + +Epoch: 138/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1243 - accuracy: 0.9644 - val_loss: 0.1907 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1289 - accuracy: 0.9590 - val_loss: 0.1533 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1203 - accuracy: 0.9604 - val_loss: 0.1708 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1025 - accuracy: 0.9717 - val_loss: 0.1635 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0951 - accuracy: 0.9736 - val_loss: 0.1628 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0872 - accuracy: 0.9756 - val_loss: 0.1781 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 138: 298.57 sec +Time taken for epoch(SUBo) 138: 243.89 sec +<---------------------------------------|Epoch [138] END|---------------------------------------> + +Epoch: 139/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1322 - accuracy: 0.9629 - val_loss: 0.1652 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1569 - accuracy: 0.9458 - val_loss: 0.2143 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1260 - accuracy: 0.9609 - val_loss: 0.2487 - val_accuracy: 0.9231 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1343 - accuracy: 0.9585 - val_loss: 0.1756 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1018 - accuracy: 0.9678 - val_loss: 0.1879 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0864 - accuracy: 0.9751 - val_loss: 0.2002 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 139: 296.96 sec +Time taken for epoch(SUBo) 139: 243.53 sec +<---------------------------------------|Epoch [139] END|---------------------------------------> + +Epoch: 140/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1588 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9595 - val_loss: 0.1786 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1241 - accuracy: 0.9619 - val_loss: 0.1725 - val_accuracy: 0.9599 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1104 - accuracy: 0.9683 - val_loss: 0.1877 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1057 - accuracy: 0.9702 - val_loss: 0.1923 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1891 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 140: 298.07 sec +Time taken for epoch(SUBo) 140: 243.40 sec +<---------------------------------------|Epoch [140] END|---------------------------------------> + +Epoch: 141/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1314 - accuracy: 0.9541 - val_loss: 0.1613 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.1692 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1292 - accuracy: 0.9580 - val_loss: 0.1645 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1142 - accuracy: 0.9673 - val_loss: 0.1783 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0957 - accuracy: 0.9727 - val_loss: 0.1860 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1725 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 141: 298.52 sec +Time taken for epoch(SUBo) 141: 243.77 sec +<---------------------------------------|Epoch [141] END|---------------------------------------> + +Epoch: 142/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1406 - accuracy: 0.9565 - val_loss: 0.1811 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1378 - accuracy: 0.9536 - val_loss: 0.1458 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1216 - accuracy: 0.9614 - val_loss: 0.1723 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1112 - accuracy: 0.9683 - val_loss: 0.1895 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1075 - accuracy: 0.9707 - val_loss: 0.1709 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1590 - val_accuracy: 0.9599 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 142: 297.84 sec +Time taken for epoch(SUBo) 142: 243.24 sec +<---------------------------------------|Epoch [142] END|---------------------------------------> + +Epoch: 143/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 159ms/step - loss: 0.1446 - accuracy: 0.9512 - val_loss: 0.1575 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9600 - val_loss: 0.1438 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1499 - accuracy: 0.9556 - val_loss: 0.1531 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1312 - accuracy: 0.9575 - val_loss: 0.1520 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9629 - val_loss: 0.1651 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1007 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 143: 296.59 sec +Time taken for epoch(SUBo) 143: 243.29 sec +<---------------------------------------|Epoch [143] END|---------------------------------------> + +Epoch: 144/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 158ms/step - loss: 0.1502 - accuracy: 0.9531 - val_loss: 0.1520 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1484 - accuracy: 0.9536 - val_loss: 0.1554 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1475 - accuracy: 0.9575 - val_loss: 0.1452 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1296 - accuracy: 0.9624 - val_loss: 0.1943 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1104 - accuracy: 0.9648 - val_loss: 0.1803 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0984 - accuracy: 0.9736 - val_loss: 0.1858 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 144: 296.73 sec +Time taken for epoch(SUBo) 144: 242.88 sec +<---------------------------------------|Epoch [144] END|---------------------------------------> + +Epoch: 145/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1262 - accuracy: 0.9600 - val_loss: 0.1634 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1191 - accuracy: 0.9639 - val_loss: 0.1680 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1056 - accuracy: 0.9658 - val_loss: 0.1970 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1031 - accuracy: 0.9707 - val_loss: 0.2054 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0822 - accuracy: 0.9800 - val_loss: 0.2039 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0879 - accuracy: 0.9746 - val_loss: 0.2102 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 145: 298.13 sec +Time taken for epoch(SUBo) 145: 242.21 sec +<---------------------------------------|Epoch [145] END|---------------------------------------> + +Epoch: 146/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1362 - accuracy: 0.9570 - val_loss: 0.1822 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1300 - accuracy: 0.9595 - val_loss: 0.2085 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1156 - accuracy: 0.9629 - val_loss: 0.2197 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0958 - accuracy: 0.9761 - val_loss: 0.2403 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.2088 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0887 - accuracy: 0.9702 - val_loss: 0.2360 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 146: 301.03 sec +Time taken for epoch(SUBo) 146: 242.33 sec +<---------------------------------------|Epoch [146] END|---------------------------------------> + +Epoch: 147/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1234 - accuracy: 0.9619 - val_loss: 0.2010 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1173 - accuracy: 0.9614 - val_loss: 0.1836 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1030 - accuracy: 0.9717 - val_loss: 0.1736 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0980 - accuracy: 0.9707 - val_loss: 0.1931 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0948 - accuracy: 0.9722 - val_loss: 0.1875 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0902 - accuracy: 0.9741 - val_loss: 0.1813 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 147: 303.25 sec +Time taken for epoch(SUBo) 147: 242.94 sec +<---------------------------------------|Epoch [147] END|---------------------------------------> + +Epoch: 148/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1321 - accuracy: 0.9565 - val_loss: 0.2085 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1171 - accuracy: 0.9629 - val_loss: 0.1716 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9570 - val_loss: 0.1633 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1077 - accuracy: 0.9688 - val_loss: 0.1642 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1000 - accuracy: 0.9702 - val_loss: 0.1597 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0804 - accuracy: 0.9756 - val_loss: 0.1575 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 148: 301.98 sec +Time taken for epoch(SUBo) 148: 243.14 sec +<---------------------------------------|Epoch [148] END|---------------------------------------> + +Epoch: 149/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1178 - accuracy: 0.9634 - val_loss: 0.1412 - val_accuracy: 0.9615 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1271 - accuracy: 0.9580 - val_loss: 0.1553 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1074 - accuracy: 0.9658 - val_loss: 0.1972 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0920 - accuracy: 0.9741 - val_loss: 0.1781 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1054 - accuracy: 0.9692 - val_loss: 0.1791 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0850 - accuracy: 0.9761 - val_loss: 0.1786 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 149: 298.73 sec +Time taken for epoch(SUBo) 149: 242.85 sec +<---------------------------------------|Epoch [149] END|---------------------------------------> + +Epoch: 150/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1315 - accuracy: 0.9580 - val_loss: 0.1966 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1324 - accuracy: 0.9551 - val_loss: 0.2153 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1131 - accuracy: 0.9634 - val_loss: 0.2608 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1028 - accuracy: 0.9697 - val_loss: 0.2539 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0900 - accuracy: 0.9707 - val_loss: 0.2782 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1002 - accuracy: 0.9697 - val_loss: 0.2693 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 150: 300.25 sec +Time taken for epoch(SUBo) 150: 243.69 sec +<---------------------------------------|Epoch [150] END|---------------------------------------> + +Epoch: 151/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2125 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1103 - accuracy: 0.9712 - val_loss: 0.2087 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1040 - accuracy: 0.9653 - val_loss: 0.2110 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0983 - accuracy: 0.9727 - val_loss: 0.1971 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1968 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0845 - accuracy: 0.9751 - val_loss: 0.2230 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 151: 298.97 sec +Time taken for epoch(SUBo) 151: 242.93 sec +<---------------------------------------|Epoch [151] END|---------------------------------------> + +Epoch: 152/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1268 - accuracy: 0.9663 - val_loss: 0.2006 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9678 - val_loss: 0.1805 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1365 - accuracy: 0.9565 - val_loss: 0.1432 - val_accuracy: 0.9631 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1488 - accuracy: 0.9517 - val_loss: 0.1688 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1657 - accuracy: 0.9458 - val_loss: 0.1674 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1403 - accuracy: 0.9561 - val_loss: 0.1698 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 152: 302.68 sec +Time taken for epoch(SUBo) 152: 243.68 sec +<---------------------------------------|Epoch [152] END|---------------------------------------> + +Epoch: 153/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1499 - accuracy: 0.9507 - val_loss: 0.1872 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1414 - accuracy: 0.9580 - val_loss: 0.1947 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1562 - accuracy: 0.9463 - val_loss: 0.2135 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1247 - accuracy: 0.9629 - val_loss: 0.1884 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1041 - accuracy: 0.9712 - val_loss: 0.2042 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9712 - val_loss: 0.1936 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 153: 299.14 sec +Time taken for epoch(SUBo) 153: 243.89 sec +<---------------------------------------|Epoch [153] END|---------------------------------------> + +Epoch: 154/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1391 - accuracy: 0.9531 - val_loss: 0.1623 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1460 - accuracy: 0.9497 - val_loss: 0.2164 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1347 - accuracy: 0.9619 - val_loss: 0.4024 - val_accuracy: 0.8686 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2569 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1417 - accuracy: 0.9546 - val_loss: 0.2886 - val_accuracy: 0.9279 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9614 - val_loss: 0.2901 - val_accuracy: 0.9263 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 154: 303.43 sec +Time taken for epoch(SUBo) 154: 244.09 sec +<---------------------------------------|Epoch [154] END|---------------------------------------> + +Epoch: 155/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1674 - accuracy: 0.9424 - val_loss: 0.2398 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1466 - accuracy: 0.9556 - val_loss: 0.2424 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1350 - accuracy: 0.9565 - val_loss: 0.2398 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1153 - accuracy: 0.9639 - val_loss: 0.2173 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9692 - val_loss: 0.2637 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2615 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 155: 299.62 sec +Time taken for epoch(SUBo) 155: 243.67 sec +<---------------------------------------|Epoch [155] END|---------------------------------------> + +Epoch: 156/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1659 - accuracy: 0.9434 - val_loss: 0.2209 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1493 - accuracy: 0.9517 - val_loss: 0.2582 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1431 - accuracy: 0.9502 - val_loss: 0.2281 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9551 - val_loss: 0.2542 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1168 - accuracy: 0.9600 - val_loss: 0.1981 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1290 - accuracy: 0.9531 - val_loss: 0.2167 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 156: 301.27 sec +Time taken for epoch(SUBo) 156: 244.46 sec +<---------------------------------------|Epoch [156] END|---------------------------------------> + +Epoch: 157/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1338 - accuracy: 0.9565 - val_loss: 0.2626 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1420 - accuracy: 0.9473 - val_loss: 0.3502 - val_accuracy: 0.9215 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1291 - accuracy: 0.9585 - val_loss: 0.2344 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1022 - accuracy: 0.9683 - val_loss: 0.2722 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1164 - accuracy: 0.9648 - val_loss: 0.2915 - val_accuracy: 0.9215 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2660 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 157: 298.53 sec +Time taken for epoch(SUBo) 157: 243.70 sec +<---------------------------------------|Epoch [157] END|---------------------------------------> + +Epoch: 158/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1569 - accuracy: 0.9517 - val_loss: 0.2548 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1227 - accuracy: 0.9609 - val_loss: 0.3033 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1267 - accuracy: 0.9575 - val_loss: 0.2928 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1117 - accuracy: 0.9663 - val_loss: 0.2713 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0982 - accuracy: 0.9717 - val_loss: 0.2921 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0927 - accuracy: 0.9741 - val_loss: 0.2760 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 158: 305.20 sec +Time taken for epoch(SUBo) 158: 244.85 sec +<---------------------------------------|Epoch [158] END|---------------------------------------> + +Epoch: 159/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1135 - accuracy: 0.9668 - val_loss: 0.2714 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1001 - accuracy: 0.9663 - val_loss: 0.3513 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0937 - accuracy: 0.9712 - val_loss: 0.2725 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0861 - accuracy: 0.9780 - val_loss: 0.2921 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0836 - accuracy: 0.9751 - val_loss: 0.2788 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0809 - accuracy: 0.9780 - val_loss: 0.2651 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 159: 306.51 sec +Time taken for epoch(SUBo) 159: 245.04 sec +<---------------------------------------|Epoch [159] END|---------------------------------------> + +Epoch: 160/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1241 - accuracy: 0.9609 - val_loss: 0.2724 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1337 - accuracy: 0.9570 - val_loss: 0.2510 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1102 - accuracy: 0.9653 - val_loss: 0.2081 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1101 - accuracy: 0.9702 - val_loss: 0.1942 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0956 - accuracy: 0.9688 - val_loss: 0.2166 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0885 - accuracy: 0.9727 - val_loss: 0.2052 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 160: 305.10 sec +Time taken for epoch(SUBo) 160: 245.83 sec +<---------------------------------------|Epoch [160] END|---------------------------------------> + +Epoch: 161/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1290 - accuracy: 0.9614 - val_loss: 0.1891 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1327 - accuracy: 0.9575 - val_loss: 0.1965 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2083 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1031 - accuracy: 0.9678 - val_loss: 0.2418 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1070 - accuracy: 0.9678 - val_loss: 0.2420 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0859 - accuracy: 0.9761 - val_loss: 0.2691 - val_accuracy: 0.9247 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 161: 299.90 sec +Time taken for epoch(SUBo) 161: 244.85 sec +<---------------------------------------|Epoch [161] END|---------------------------------------> + +Epoch: 162/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1228 - accuracy: 0.9629 - val_loss: 0.2065 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1223 - accuracy: 0.9604 - val_loss: 0.1999 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1606 - accuracy: 0.9517 - val_loss: 0.2025 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1366 - accuracy: 0.9575 - val_loss: 0.2026 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1233 - accuracy: 0.9619 - val_loss: 0.2040 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1096 - accuracy: 0.9673 - val_loss: 0.2063 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 162: 299.56 sec +Time taken for epoch(SUBo) 162: 244.33 sec +<---------------------------------------|Epoch [162] END|---------------------------------------> + +Epoch: 163/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1399 - accuracy: 0.9565 - val_loss: 0.2292 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1215 - accuracy: 0.9585 - val_loss: 0.2450 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1078 - accuracy: 0.9648 - val_loss: 0.2188 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1154 - accuracy: 0.9648 - val_loss: 0.2537 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1237 - accuracy: 0.9619 - val_loss: 0.2278 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9634 - val_loss: 0.2206 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 163: 297.49 sec +Time taken for epoch(SUBo) 163: 243.39 sec +<---------------------------------------|Epoch [163] END|---------------------------------------> + +Epoch: 164/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1580 - accuracy: 0.9507 - val_loss: 0.2399 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9570 - val_loss: 0.2307 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1342 - accuracy: 0.9604 - val_loss: 0.1897 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.2260 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9668 - val_loss: 0.2024 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0980 - accuracy: 0.9673 - val_loss: 0.2013 - val_accuracy: 0.9551 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 164: 300.36 sec +Time taken for epoch(SUBo) 164: 244.25 sec +<---------------------------------------|Epoch [164] END|---------------------------------------> + +Epoch: 165/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1589 - accuracy: 0.9497 - val_loss: 0.1661 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9575 - val_loss: 0.2048 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1393 - accuracy: 0.9600 - val_loss: 0.1941 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.2079 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1092 - accuracy: 0.9688 - val_loss: 0.2288 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9761 - val_loss: 0.2080 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 165: 307.38 sec +Time taken for epoch(SUBo) 165: 245.49 sec +<---------------------------------------|Epoch [165] END|---------------------------------------> + +Epoch: 166/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1276 - accuracy: 0.9585 - val_loss: 0.2018 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1326 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1107 - accuracy: 0.9673 - val_loss: 0.1818 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1782 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0880 - accuracy: 0.9731 - val_loss: 0.1845 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0775 - accuracy: 0.9756 - val_loss: 0.1787 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 166: 306.37 sec +Time taken for epoch(SUBo) 166: 244.99 sec +<---------------------------------------|Epoch [166] END|---------------------------------------> + +Epoch: 167/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 162ms/step - loss: 0.1360 - accuracy: 0.9585 - val_loss: 0.1928 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.1949 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1286 - accuracy: 0.9600 - val_loss: 0.2223 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1548 - accuracy: 0.9487 - val_loss: 0.3237 - val_accuracy: 0.9199 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1733 - accuracy: 0.9395 - val_loss: 0.2911 - val_accuracy: 0.9135 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9565 - val_loss: 0.2720 - val_accuracy: 0.9231 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 167: 302.14 sec +Time taken for epoch(SUBo) 167: 244.91 sec +<---------------------------------------|Epoch [167] END|---------------------------------------> + +Epoch: 168/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1783 - accuracy: 0.9365 - val_loss: 0.3662 - val_accuracy: 0.9006 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1679 - accuracy: 0.9419 - val_loss: 0.2450 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1442 - accuracy: 0.9512 - val_loss: 0.2916 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1321 - accuracy: 0.9575 - val_loss: 0.3255 - val_accuracy: 0.9231 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1195 - accuracy: 0.9624 - val_loss: 0.3551 - val_accuracy: 0.9199 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1084 - accuracy: 0.9668 - val_loss: 0.3794 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 168: 299.21 sec +Time taken for epoch(SUBo) 168: 243.84 sec +<---------------------------------------|Epoch [168] END|---------------------------------------> + +Epoch: 169/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1427 - accuracy: 0.9624 - val_loss: 0.2396 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1854 - accuracy: 0.9336 - val_loss: 0.2213 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1539 - accuracy: 0.9458 - val_loss: 0.2068 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1354 - accuracy: 0.9585 - val_loss: 0.3011 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1135 - accuracy: 0.9629 - val_loss: 0.2591 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1127 - accuracy: 0.9629 - val_loss: 0.2691 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 169: 300.15 sec +Time taken for epoch(SUBo) 169: 243.82 sec +<---------------------------------------|Epoch [169] END|---------------------------------------> + +Epoch: 170/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1595 - accuracy: 0.9438 - val_loss: 0.2370 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1465 - accuracy: 0.9492 - val_loss: 0.1867 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1322 - accuracy: 0.9565 - val_loss: 0.2246 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1295 - accuracy: 0.9609 - val_loss: 0.2039 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1179 - accuracy: 0.9644 - val_loss: 0.1999 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1043 - accuracy: 0.9688 - val_loss: 0.2048 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 170: 299.63 sec +Time taken for epoch(SUBo) 170: 242.91 sec +<---------------------------------------|Epoch [170] END|---------------------------------------> + +Epoch: 171/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1650 - accuracy: 0.9541 - val_loss: 0.1615 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1401 - accuracy: 0.9565 - val_loss: 0.1734 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1397 - accuracy: 0.9570 - val_loss: 0.1680 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1035 - accuracy: 0.9741 - val_loss: 0.1722 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1021 - accuracy: 0.9668 - val_loss: 0.1847 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1145 - accuracy: 0.9629 - val_loss: 0.1761 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 171: 304.06 sec +Time taken for epoch(SUBo) 171: 243.87 sec +<---------------------------------------|Epoch [171] END|---------------------------------------> + +Epoch: 172/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1201 - accuracy: 0.9629 - val_loss: 0.1739 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1188 - accuracy: 0.9614 - val_loss: 0.1925 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1138 - accuracy: 0.9673 - val_loss: 0.2372 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1000 - accuracy: 0.9697 - val_loss: 0.1883 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0899 - accuracy: 0.9731 - val_loss: 0.2044 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0740 - accuracy: 0.9790 - val_loss: 0.2011 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 172: 304.33 sec +Time taken for epoch(SUBo) 172: 243.65 sec +<---------------------------------------|Epoch [172] END|---------------------------------------> + +Epoch: 173/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1373 - accuracy: 0.9541 - val_loss: 0.1948 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1472 - accuracy: 0.9502 - val_loss: 0.2673 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1669 - accuracy: 0.9453 - val_loss: 0.1954 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.1729 - val_accuracy: 0.9519 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1263 - accuracy: 0.9629 - val_loss: 0.2251 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1095 - accuracy: 0.9658 - val_loss: 0.2223 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 173: 302.38 sec +Time taken for epoch(SUBo) 173: 244.10 sec +<---------------------------------------|Epoch [173] END|---------------------------------------> + +Epoch: 174/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1421 - accuracy: 0.9580 - val_loss: 0.2098 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1407 - accuracy: 0.9561 - val_loss: 0.2066 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1279 - accuracy: 0.9609 - val_loss: 0.2408 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1170 - accuracy: 0.9629 - val_loss: 0.2116 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.2266 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0989 - accuracy: 0.9722 - val_loss: 0.2566 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 174: 298.38 sec +Time taken for epoch(SUBo) 174: 242.96 sec +<---------------------------------------|Epoch [174] END|---------------------------------------> + +Epoch: 175/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1366 - accuracy: 0.9546 - val_loss: 0.2196 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.1153 - accuracy: 0.9619 - val_loss: 0.2363 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1186 - accuracy: 0.9624 - val_loss: 0.2094 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1060 - accuracy: 0.9683 - val_loss: 0.2792 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0901 - accuracy: 0.9736 - val_loss: 0.2793 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0818 - accuracy: 0.9751 - val_loss: 0.3102 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 175: 298.34 sec +Time taken for epoch(SUBo) 175: 243.27 sec +<---------------------------------------|Epoch [175] END|---------------------------------------> + +Epoch: 176/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1217 - accuracy: 0.9561 - val_loss: 0.3390 - val_accuracy: 0.8894 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1363 - accuracy: 0.9600 - val_loss: 0.3365 - val_accuracy: 0.9151 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1219 - accuracy: 0.9580 - val_loss: 0.2768 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.1262 - accuracy: 0.9629 - val_loss: 0.2921 - val_accuracy: 0.9135 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0952 - accuracy: 0.9717 - val_loss: 0.3173 - val_accuracy: 0.9151 +Epoch 6/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0972 - accuracy: 0.9731 - val_loss: 0.3247 - val_accuracy: 0.9135 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 176: 300.75 sec +Time taken for epoch(SUBo) 176: 244.46 sec +<---------------------------------------|Epoch [176] END|---------------------------------------> + +Epoch: 177/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1301 - accuracy: 0.9600 - val_loss: 0.2746 - val_accuracy: 0.9215 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1191 - accuracy: 0.9658 - val_loss: 0.2657 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1160 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0987 - accuracy: 0.9722 - val_loss: 0.2429 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0863 - accuracy: 0.9756 - val_loss: 0.2320 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0852 - accuracy: 0.9771 - val_loss: 0.2548 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 177: 307.13 sec +Time taken for epoch(SUBo) 177: 245.28 sec +<---------------------------------------|Epoch [177] END|---------------------------------------> + +Epoch: 178/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1285 - accuracy: 0.9634 - val_loss: 0.1938 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1361 - accuracy: 0.9551 - val_loss: 0.2198 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1310 - accuracy: 0.9614 - val_loss: 0.2257 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1178 - accuracy: 0.9658 - val_loss: 0.1883 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1097 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9697 - val_loss: 0.2949 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 178: 307.31 sec +Time taken for epoch(SUBo) 178: 246.17 sec +<---------------------------------------|Epoch [178] END|---------------------------------------> + +Epoch: 179/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1366 - accuracy: 0.9551 - val_loss: 0.2232 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1883 - accuracy: 0.9370 - val_loss: 0.2155 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1590 - accuracy: 0.9492 - val_loss: 0.2392 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1456 - accuracy: 0.9517 - val_loss: 0.2673 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2418 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1098 - accuracy: 0.9658 - val_loss: 0.2398 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 179: 304.66 sec +Time taken for epoch(SUBo) 179: 246.00 sec +<---------------------------------------|Epoch [179] END|---------------------------------------> + +Epoch: 180/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1470 - accuracy: 0.9546 - val_loss: 0.2427 - val_accuracy: 0.9231 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1592 - accuracy: 0.9521 - val_loss: 0.3052 - val_accuracy: 0.9103 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1297 - accuracy: 0.9629 - val_loss: 0.2849 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1300 - accuracy: 0.9551 - val_loss: 0.2115 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1155 - accuracy: 0.9644 - val_loss: 0.2489 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1184 - accuracy: 0.9648 - val_loss: 0.2458 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 180: 303.24 sec +Time taken for epoch(SUBo) 180: 245.01 sec +<---------------------------------------|Epoch [180] END|---------------------------------------> + +Epoch: 181/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1431 - accuracy: 0.9556 - val_loss: 0.2670 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1354 - accuracy: 0.9580 - val_loss: 0.3152 - val_accuracy: 0.9071 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1250 - accuracy: 0.9604 - val_loss: 0.2952 - val_accuracy: 0.9054 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.3917 - val_accuracy: 0.8958 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0896 - accuracy: 0.9756 - val_loss: 0.3502 - val_accuracy: 0.8990 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0898 - accuracy: 0.9707 - val_loss: 0.3361 - val_accuracy: 0.9071 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 181: 302.34 sec +Time taken for epoch(SUBo) 181: 244.62 sec +<---------------------------------------|Epoch [181] END|---------------------------------------> + +Epoch: 182/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1245 - accuracy: 0.9604 - val_loss: 0.2772 - val_accuracy: 0.9247 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1336 - accuracy: 0.9600 - val_loss: 0.2250 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1114 - accuracy: 0.9644 - val_loss: 0.3103 - val_accuracy: 0.9135 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1016 - accuracy: 0.9731 - val_loss: 0.3044 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0906 - accuracy: 0.9702 - val_loss: 0.3051 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0863 - accuracy: 0.9731 - val_loss: 0.3318 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 182: 304.09 sec +Time taken for epoch(SUBo) 182: 245.03 sec +<---------------------------------------|Epoch [182] END|---------------------------------------> + +Epoch: 183/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1180 - accuracy: 0.9609 - val_loss: 0.3431 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1122 - accuracy: 0.9678 - val_loss: 0.2777 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1235 - accuracy: 0.9634 - val_loss: 0.1881 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0921 - accuracy: 0.9717 - val_loss: 0.2754 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9712 - val_loss: 0.3383 - val_accuracy: 0.9103 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0866 - accuracy: 0.9751 - val_loss: 0.3123 - val_accuracy: 0.9215 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 183: 304.60 sec +Time taken for epoch(SUBo) 183: 244.97 sec +<---------------------------------------|Epoch [183] END|---------------------------------------> + +Epoch: 184/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1436 - accuracy: 0.9565 - val_loss: 0.2403 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1356 - accuracy: 0.9575 - val_loss: 0.2531 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1325 - accuracy: 0.9531 - val_loss: 0.3488 - val_accuracy: 0.9215 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.2155 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1100 - accuracy: 0.9658 - val_loss: 0.2753 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1108 - accuracy: 0.9644 - val_loss: 0.2761 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 184: 304.30 sec +Time taken for epoch(SUBo) 184: 244.89 sec +<---------------------------------------|Epoch [184] END|---------------------------------------> + +Epoch: 185/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 160ms/step - loss: 0.1250 - accuracy: 0.9619 - val_loss: 0.2633 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1248 - accuracy: 0.9604 - val_loss: 0.2972 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1252 - accuracy: 0.9639 - val_loss: 0.2754 - val_accuracy: 0.9263 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1152 - accuracy: 0.9683 - val_loss: 0.2419 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.2478 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0871 - accuracy: 0.9736 - val_loss: 0.2475 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 185: 306.52 sec +Time taken for epoch(SUBo) 185: 245.42 sec +<---------------------------------------|Epoch [185] END|---------------------------------------> + +Epoch: 186/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1323 - accuracy: 0.9585 - val_loss: 0.2456 - val_accuracy: 0.9295 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1374 - accuracy: 0.9639 - val_loss: 0.2509 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1351 - accuracy: 0.9639 - val_loss: 0.2669 - val_accuracy: 0.9311 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1114 - accuracy: 0.9639 - val_loss: 0.2947 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0944 - accuracy: 0.9766 - val_loss: 0.2886 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0906 - accuracy: 0.9736 - val_loss: 0.2739 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 186: 307.26 sec +Time taken for epoch(SUBo) 186: 246.41 sec +<---------------------------------------|Epoch [186] END|---------------------------------------> + +Epoch: 187/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1226 - accuracy: 0.9644 - val_loss: 0.2625 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1695 - accuracy: 0.9453 - val_loss: 1.2514 - val_accuracy: 0.7115 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1965 - accuracy: 0.9336 - val_loss: 0.5935 - val_accuracy: 0.8429 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1654 - accuracy: 0.9458 - val_loss: 0.4132 - val_accuracy: 0.9054 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1389 - accuracy: 0.9551 - val_loss: 0.4170 - val_accuracy: 0.9038 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1316 - accuracy: 0.9595 - val_loss: 0.4311 - val_accuracy: 0.9022 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 187: 297.48 sec +Time taken for epoch(SUBo) 187: 244.18 sec +<---------------------------------------|Epoch [187] END|---------------------------------------> + +Epoch: 188/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1581 - accuracy: 0.9458 - val_loss: 0.3557 - val_accuracy: 0.9087 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1425 - accuracy: 0.9561 - val_loss: 0.3358 - val_accuracy: 0.9199 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1316 - accuracy: 0.9551 - val_loss: 0.3622 - val_accuracy: 0.9231 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1155 - accuracy: 0.9634 - val_loss: 0.3811 - val_accuracy: 0.9119 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1323 - accuracy: 0.9546 - val_loss: 0.3472 - val_accuracy: 0.9167 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1224 - accuracy: 0.9644 - val_loss: 0.3330 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 188: 299.49 sec +Time taken for epoch(SUBo) 188: 244.91 sec +<---------------------------------------|Epoch [188] END|---------------------------------------> + +Epoch: 189/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1436 - accuracy: 0.9517 - val_loss: 0.2752 - val_accuracy: 0.9279 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1421 - accuracy: 0.9531 - val_loss: 0.2516 - val_accuracy: 0.9263 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1263 - accuracy: 0.9600 - val_loss: 0.2514 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1058 - accuracy: 0.9663 - val_loss: 0.2660 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1131 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9311 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1111 - accuracy: 0.9663 - val_loss: 0.2356 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 189: 301.75 sec +Time taken for epoch(SUBo) 189: 245.44 sec +<---------------------------------------|Epoch [189] END|---------------------------------------> + +Epoch: 190/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1480 - accuracy: 0.9570 - val_loss: 0.1996 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1882 - accuracy: 0.9380 - val_loss: 0.2167 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1597 - accuracy: 0.9512 - val_loss: 0.2156 - val_accuracy: 0.9247 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1344 - accuracy: 0.9590 - val_loss: 0.2198 - val_accuracy: 0.9295 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1345 - accuracy: 0.9609 - val_loss: 0.2668 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1128 - accuracy: 0.9644 - val_loss: 0.2396 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 190: 300.24 sec +Time taken for epoch(SUBo) 190: 245.01 sec +<---------------------------------------|Epoch [190] END|---------------------------------------> + +Epoch: 191/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1471 - accuracy: 0.9570 - val_loss: 0.2358 - val_accuracy: 0.9279 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1378 - accuracy: 0.9551 - val_loss: 0.2055 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1446 - accuracy: 0.9546 - val_loss: 0.1978 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1355 - accuracy: 0.9595 - val_loss: 0.1849 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1076 - accuracy: 0.9727 - val_loss: 0.2088 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1103 - accuracy: 0.9663 - val_loss: 0.1988 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 191: 301.30 sec +Time taken for epoch(SUBo) 191: 245.49 sec +<---------------------------------------|Epoch [191] END|---------------------------------------> + +Epoch: 192/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 161ms/step - loss: 0.1421 - accuracy: 0.9575 - val_loss: 0.2050 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1355 - accuracy: 0.9541 - val_loss: 0.3539 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1398 - accuracy: 0.9551 - val_loss: 0.2728 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1200 - accuracy: 0.9653 - val_loss: 0.2649 - val_accuracy: 0.9103 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9604 - val_loss: 0.2364 - val_accuracy: 0.9247 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1229 - accuracy: 0.9580 - val_loss: 0.2355 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 192: 300.20 sec +Time taken for epoch(SUBo) 192: 245.83 sec +<---------------------------------------|Epoch [192] END|---------------------------------------> + +Epoch: 193/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1544 - accuracy: 0.9478 - val_loss: 0.2400 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1478 - accuracy: 0.9507 - val_loss: 0.2931 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.2789 - val_accuracy: 0.9327 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1375 - accuracy: 0.9585 - val_loss: 0.2220 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1067 - accuracy: 0.9712 - val_loss: 0.2248 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0902 - accuracy: 0.9751 - val_loss: 0.2198 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 193: 298.24 sec +Time taken for epoch(SUBo) 193: 245.22 sec +<---------------------------------------|Epoch [193] END|---------------------------------------> + +Epoch: 194/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1352 - accuracy: 0.9634 - val_loss: 0.2151 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1429 - accuracy: 0.9595 - val_loss: 0.2100 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1182 - accuracy: 0.9653 - val_loss: 0.2180 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1083 - accuracy: 0.9683 - val_loss: 0.2342 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1105 - accuracy: 0.9683 - val_loss: 0.2624 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0829 - accuracy: 0.9741 - val_loss: 0.2530 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 194: 299.52 sec +Time taken for epoch(SUBo) 194: 245.69 sec +<---------------------------------------|Epoch [194] END|---------------------------------------> + +Epoch: 195/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 159ms/step - loss: 0.1158 - accuracy: 0.9673 - val_loss: 0.2753 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.1058 - accuracy: 0.9648 - val_loss: 0.2734 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1013 - accuracy: 0.9673 - val_loss: 0.2366 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0861 - accuracy: 0.9756 - val_loss: 0.2831 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0798 - accuracy: 0.9775 - val_loss: 0.2666 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0687 - accuracy: 0.9824 - val_loss: 0.3035 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 195: 299.74 sec +Time taken for epoch(SUBo) 195: 244.18 sec +<---------------------------------------|Epoch [195] END|---------------------------------------> + +Epoch: 196/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1189 - accuracy: 0.9604 - val_loss: 0.2907 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1172 - accuracy: 0.9658 - val_loss: 0.2743 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1052 - accuracy: 0.9663 - val_loss: 0.2412 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1047 - accuracy: 0.9653 - val_loss: 0.4034 - val_accuracy: 0.9006 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1205 - accuracy: 0.9580 - val_loss: 0.3797 - val_accuracy: 0.9199 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1042 - accuracy: 0.9678 - val_loss: 0.3400 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 196: 300.20 sec +Time taken for epoch(SUBo) 196: 246.37 sec +<---------------------------------------|Epoch [196] END|---------------------------------------> + +Epoch: 197/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1318 - accuracy: 0.9595 - val_loss: 0.2433 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1237 - accuracy: 0.9624 - val_loss: 0.2380 - val_accuracy: 0.9311 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1288 - accuracy: 0.9600 - val_loss: 0.2326 - val_accuracy: 0.9279 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2398 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0943 - accuracy: 0.9751 - val_loss: 0.2242 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9736 - val_loss: 0.2357 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 197: 297.68 sec +Time taken for epoch(SUBo) 197: 246.24 sec +<---------------------------------------|Epoch [197] END|---------------------------------------> + +Epoch: 198/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1174 - accuracy: 0.9658 - val_loss: 0.2696 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9575 - val_loss: 0.2424 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0977 - accuracy: 0.9707 - val_loss: 0.2852 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0980 - accuracy: 0.9688 - val_loss: 0.2780 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0881 - accuracy: 0.9736 - val_loss: 0.2471 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0809 - accuracy: 0.9751 - val_loss: 0.2606 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 198: 297.77 sec +Time taken for epoch(SUBo) 198: 246.78 sec +<---------------------------------------|Epoch [198] END|---------------------------------------> + +Epoch: 199/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1334 - accuracy: 0.9609 - val_loss: 0.2220 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1240 - accuracy: 0.9604 - val_loss: 0.2392 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1112 - accuracy: 0.9658 - val_loss: 0.2233 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1175 - accuracy: 0.9673 - val_loss: 0.2212 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1032 - accuracy: 0.9678 - val_loss: 0.2742 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1011 - accuracy: 0.9663 - val_loss: 0.2787 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 199: 298.50 sec +Time taken for epoch(SUBo) 199: 246.76 sec +<---------------------------------------|Epoch [199] END|---------------------------------------> + +Epoch: 200/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1285 - accuracy: 0.9580 - val_loss: 0.3062 - val_accuracy: 0.9103 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1324 - accuracy: 0.9570 - val_loss: 0.2178 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1266 - accuracy: 0.9624 - val_loss: 0.2289 - val_accuracy: 0.9327 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1193 - accuracy: 0.9565 - val_loss: 0.2471 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1040 - accuracy: 0.9673 - val_loss: 0.2422 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0873 - accuracy: 0.9741 - val_loss: 0.2505 - val_accuracy: 0.9311 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 200: 298.67 sec +Time taken for epoch(SUBo) 200: 246.74 sec +<---------------------------------------|Epoch [200] END|---------------------------------------> + +Epoch: 201/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1427 - accuracy: 0.9551 - val_loss: 0.2224 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1369 - accuracy: 0.9541 - val_loss: 0.2401 - val_accuracy: 0.9295 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1309 - accuracy: 0.9595 - val_loss: 0.2131 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1004 - accuracy: 0.9683 - val_loss: 0.2495 - val_accuracy: 0.9311 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0969 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0972 - accuracy: 0.9697 - val_loss: 0.2479 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 201: 297.63 sec +Time taken for epoch(SUBo) 201: 245.98 sec +<---------------------------------------|Epoch [201] END|---------------------------------------> + +Epoch: 202/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1129 - accuracy: 0.9663 - val_loss: 0.2707 - val_accuracy: 0.9327 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1298 - accuracy: 0.9600 - val_loss: 0.2119 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1173 - accuracy: 0.9644 - val_loss: 0.2111 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1074 - accuracy: 0.9712 - val_loss: 0.1881 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0924 - accuracy: 0.9702 - val_loss: 0.2089 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9805 - val_loss: 0.2168 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 202: 298.33 sec +Time taken for epoch(SUBo) 202: 246.64 sec +<---------------------------------------|Epoch [202] END|---------------------------------------> + +Epoch: 203/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1369 - accuracy: 0.9561 - val_loss: 0.2180 - val_accuracy: 0.9343 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1303 - accuracy: 0.9541 - val_loss: 0.2391 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1245 - accuracy: 0.9634 - val_loss: 0.2390 - val_accuracy: 0.9359 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1135 - accuracy: 0.9648 - val_loss: 0.2664 - val_accuracy: 0.9279 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0981 - accuracy: 0.9727 - val_loss: 0.2374 - val_accuracy: 0.9359 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0972 - accuracy: 0.9722 - val_loss: 0.2165 - val_accuracy: 0.9375 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 203: 296.86 sec +Time taken for epoch(SUBo) 203: 245.14 sec +<---------------------------------------|Epoch [203] END|---------------------------------------> + +Epoch: 204/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1145 - accuracy: 0.9663 - val_loss: 0.2079 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1075 - accuracy: 0.9648 - val_loss: 0.2058 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0978 - accuracy: 0.9673 - val_loss: 0.2125 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1015 - accuracy: 0.9722 - val_loss: 0.2370 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0780 - accuracy: 0.9775 - val_loss: 0.2245 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0684 - accuracy: 0.9814 - val_loss: 0.2192 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 204: 298.03 sec +Time taken for epoch(SUBo) 204: 246.30 sec +<---------------------------------------|Epoch [204] END|---------------------------------------> + +Epoch: 205/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1153 - accuracy: 0.9614 - val_loss: 0.2277 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1168 - accuracy: 0.9629 - val_loss: 0.2214 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1209 - accuracy: 0.9629 - val_loss: 0.1874 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1025 - accuracy: 0.9692 - val_loss: 0.2265 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0891 - accuracy: 0.9766 - val_loss: 0.1875 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0753 - accuracy: 0.9805 - val_loss: 0.2138 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 205: 297.87 sec +Time taken for epoch(SUBo) 205: 245.90 sec +<---------------------------------------|Epoch [205] END|---------------------------------------> + +Epoch: 206/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1070 - accuracy: 0.9697 - val_loss: 0.2057 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1039 - accuracy: 0.9673 - val_loss: 0.2215 - val_accuracy: 0.9391 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0855 - accuracy: 0.9741 - val_loss: 0.2183 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.3037 - val_accuracy: 0.9359 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9766 - val_loss: 0.2560 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0760 - accuracy: 0.9766 - val_loss: 0.2418 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 206: 297.94 sec +Time taken for epoch(SUBo) 206: 246.21 sec +<---------------------------------------|Epoch [206] END|---------------------------------------> + +Epoch: 207/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1259 - accuracy: 0.9658 - val_loss: 0.2366 - val_accuracy: 0.9359 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1204 - accuracy: 0.9644 - val_loss: 0.2283 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1144 - accuracy: 0.9624 - val_loss: 0.1889 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0992 - accuracy: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9407 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0875 - accuracy: 0.9775 - val_loss: 0.2601 - val_accuracy: 0.9343 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0808 - accuracy: 0.9800 - val_loss: 0.2478 - val_accuracy: 0.9343 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 207: 298.66 sec +Time taken for epoch(SUBo) 207: 246.51 sec +<---------------------------------------|Epoch [207] END|---------------------------------------> + +Epoch: 208/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1141 - accuracy: 0.9648 - val_loss: 0.2134 - val_accuracy: 0.9407 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1072 - accuracy: 0.9663 - val_loss: 0.1996 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0942 - accuracy: 0.9697 - val_loss: 0.1941 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0885 - accuracy: 0.9741 - val_loss: 0.2165 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0837 - accuracy: 0.9741 - val_loss: 0.2150 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0726 - accuracy: 0.9829 - val_loss: 0.2024 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 208: 298.91 sec +Time taken for epoch(SUBo) 208: 246.69 sec +<---------------------------------------|Epoch [208] END|---------------------------------------> + +Epoch: 209/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1141 - accuracy: 0.9639 - val_loss: 0.2234 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1162 - accuracy: 0.9629 - val_loss: 0.2288 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1251 - accuracy: 0.9624 - val_loss: 0.2119 - val_accuracy: 0.9407 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.2092 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1016 - accuracy: 0.9678 - val_loss: 0.2370 - val_accuracy: 0.9327 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0761 - accuracy: 0.9771 - val_loss: 0.2383 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 209: 296.92 sec +Time taken for epoch(SUBo) 209: 245.42 sec +<---------------------------------------|Epoch [209] END|---------------------------------------> + +Epoch: 210/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1219 - accuracy: 0.9619 - val_loss: 0.2331 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1128 - accuracy: 0.9624 - val_loss: 0.2102 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1038 - accuracy: 0.9658 - val_loss: 0.1857 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0935 - accuracy: 0.9727 - val_loss: 0.2113 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1070 - accuracy: 0.9668 - val_loss: 0.2461 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0851 - accuracy: 0.9766 - val_loss: 0.2336 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 210: 295.74 sec +Time taken for epoch(SUBo) 210: 245.46 sec +<---------------------------------------|Epoch [210] END|---------------------------------------> + +Epoch: 211/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1203 - accuracy: 0.9658 - val_loss: 0.1951 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1033 - accuracy: 0.9673 - val_loss: 0.1898 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0882 - accuracy: 0.9771 - val_loss: 0.1876 - val_accuracy: 0.9423 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0830 - accuracy: 0.9751 - val_loss: 0.1828 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0600 - accuracy: 0.9829 - val_loss: 0.2026 - val_accuracy: 0.9423 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0587 - accuracy: 0.9854 - val_loss: 0.1957 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 211: 295.87 sec +Time taken for epoch(SUBo) 211: 245.59 sec +<---------------------------------------|Epoch [211] END|---------------------------------------> + +Epoch: 212/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9746 - val_loss: 0.1699 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1037 - accuracy: 0.9673 - val_loss: 0.2054 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0907 - accuracy: 0.9731 - val_loss: 0.2072 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0802 - accuracy: 0.9771 - val_loss: 0.1906 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0749 - accuracy: 0.9814 - val_loss: 0.1856 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0661 - accuracy: 0.9824 - val_loss: 0.1860 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 212: 295.86 sec +Time taken for epoch(SUBo) 212: 245.69 sec +<---------------------------------------|Epoch [212] END|---------------------------------------> + +Epoch: 213/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1047 - accuracy: 0.9688 - val_loss: 0.1803 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0977 - accuracy: 0.9746 - val_loss: 0.1586 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9722 - val_loss: 0.1882 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1002 - accuracy: 0.9756 - val_loss: 0.2034 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0865 - accuracy: 0.9766 - val_loss: 0.2175 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0730 - accuracy: 0.9790 - val_loss: 0.2228 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 213: 295.87 sec +Time taken for epoch(SUBo) 213: 245.31 sec +<---------------------------------------|Epoch [213] END|---------------------------------------> + +Epoch: 214/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1225 - accuracy: 0.9619 - val_loss: 0.1941 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1188 - accuracy: 0.9658 - val_loss: 0.1750 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1020 - accuracy: 0.9644 - val_loss: 0.2022 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0990 - accuracy: 0.9668 - val_loss: 0.1984 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0992 - accuracy: 0.9722 - val_loss: 0.2096 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0697 - accuracy: 0.9814 - val_loss: 0.2177 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 214: 295.73 sec +Time taken for epoch(SUBo) 214: 245.40 sec +<---------------------------------------|Epoch [214] END|---------------------------------------> + +Epoch: 215/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0995 - accuracy: 0.9717 - val_loss: 0.2052 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1099 - accuracy: 0.9688 - val_loss: 0.2122 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0904 - accuracy: 0.9712 - val_loss: 0.2057 - val_accuracy: 0.9455 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0789 - accuracy: 0.9756 - val_loss: 0.2348 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0699 - accuracy: 0.9834 - val_loss: 0.2055 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.2412 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 215: 296.11 sec +Time taken for epoch(SUBo) 215: 245.32 sec +<---------------------------------------|Epoch [215] END|---------------------------------------> + +Epoch: 216/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1354 - accuracy: 0.9619 - val_loss: 1.9127 - val_accuracy: 0.6250 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.6524 - accuracy: 0.6860 - val_loss: 0.5187 - val_accuracy: 0.8253 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.4618 - accuracy: 0.8057 - val_loss: 0.4150 - val_accuracy: 0.9103 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3662 - accuracy: 0.8638 - val_loss: 0.2908 - val_accuracy: 0.9263 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.3131 - accuracy: 0.8921 - val_loss: 0.3339 - val_accuracy: 0.9263 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2602 - accuracy: 0.9121 - val_loss: 0.3118 - val_accuracy: 0.9279 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 216: 294.77 sec +Time taken for epoch(SUBo) 216: 244.51 sec +<---------------------------------------|Epoch [216] END|---------------------------------------> + +Epoch: 217/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.3051 - accuracy: 0.8945 - val_loss: 0.2281 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2581 - accuracy: 0.9053 - val_loss: 0.2585 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.2153 - accuracy: 0.9385 - val_loss: 0.1958 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1935 - accuracy: 0.9463 - val_loss: 0.1896 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1759 - accuracy: 0.9492 - val_loss: 0.2038 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1616 - accuracy: 0.9502 - val_loss: 0.2104 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 217: 295.94 sec +Time taken for epoch(SUBo) 217: 245.61 sec +<---------------------------------------|Epoch [217] END|---------------------------------------> + +Epoch: 218/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.2018 - accuracy: 0.9331 - val_loss: 0.2546 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1969 - accuracy: 0.9355 - val_loss: 0.2012 - val_accuracy: 0.9471 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1747 - accuracy: 0.9453 - val_loss: 0.1932 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1722 - accuracy: 0.9507 - val_loss: 0.2019 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1437 - accuracy: 0.9536 - val_loss: 0.2124 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1302 - accuracy: 0.9609 - val_loss: 0.2347 - val_accuracy: 0.9391 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 218: 295.43 sec +Time taken for epoch(SUBo) 218: 245.14 sec +<---------------------------------------|Epoch [218] END|---------------------------------------> + +Epoch: 219/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1756 - accuracy: 0.9478 - val_loss: 0.1971 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1803 - accuracy: 0.9414 - val_loss: 0.1779 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1618 - accuracy: 0.9424 - val_loss: 0.2014 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1546 - accuracy: 0.9600 - val_loss: 0.2209 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1289 - accuracy: 0.9639 - val_loss: 0.2224 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.2182 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 219: 293.27 sec +Time taken for epoch(SUBo) 219: 243.09 sec +<---------------------------------------|Epoch [219] END|---------------------------------------> + +Epoch: 220/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 156ms/step - loss: 0.1384 - accuracy: 0.9580 - val_loss: 0.1899 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1636 - accuracy: 0.9497 - val_loss: 0.1965 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1510 - accuracy: 0.9561 - val_loss: 0.1807 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1192 - accuracy: 0.9629 - val_loss: 0.2034 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 39s 152ms/step - loss: 0.1268 - accuracy: 0.9585 - val_loss: 0.1812 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.1890 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 220: 289.19 sec +Time taken for epoch(SUBo) 220: 239.59 sec +<---------------------------------------|Epoch [220] END|---------------------------------------> + +Epoch: 221/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1293 - accuracy: 0.9604 - val_loss: 0.2001 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1258 - accuracy: 0.9629 - val_loss: 0.2138 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1242 - accuracy: 0.9629 - val_loss: 0.2242 - val_accuracy: 0.9471 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1100 - accuracy: 0.9712 - val_loss: 0.2425 - val_accuracy: 0.9391 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1082 - accuracy: 0.9712 - val_loss: 0.2177 - val_accuracy: 0.9455 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0903 - accuracy: 0.9751 - val_loss: 0.2145 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 221: 303.15 sec +Time taken for epoch(SUBo) 221: 246.93 sec +<---------------------------------------|Epoch [221] END|---------------------------------------> + +Epoch: 222/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2076 - val_accuracy: 0.9375 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1585 - accuracy: 0.9556 - val_loss: 0.2135 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1446 - accuracy: 0.9575 - val_loss: 0.2137 - val_accuracy: 0.9375 +Epoch 4/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1215 - accuracy: 0.9663 - val_loss: 0.2196 - val_accuracy: 0.9343 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1310 - accuracy: 0.9609 - val_loss: 0.2567 - val_accuracy: 0.9295 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1038 - accuracy: 0.9727 - val_loss: 0.2416 - val_accuracy: 0.9327 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 222: 299.03 sec +Time taken for epoch(SUBo) 222: 248.17 sec +<---------------------------------------|Epoch [222] END|---------------------------------------> + +Epoch: 223/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 165ms/step - loss: 0.1276 - accuracy: 0.9619 - val_loss: 0.2650 - val_accuracy: 0.9311 +Epoch 2/6 +256/256 [==============================] - 42s 165ms/step - loss: 0.1193 - accuracy: 0.9570 - val_loss: 0.1668 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1061 - accuracy: 0.9688 - val_loss: 0.1817 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.2031 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 43s 166ms/step - loss: 0.0876 - accuracy: 0.9751 - val_loss: 0.1877 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.0826 - accuracy: 0.9766 - val_loss: 0.1862 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 223: 312.69 sec +Time taken for epoch(SUBo) 223: 256.34 sec +<---------------------------------------|Epoch [223] END|---------------------------------------> + +Epoch: 224/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1238 - accuracy: 0.9668 - val_loss: 0.1797 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1198 - accuracy: 0.9624 - val_loss: 0.1924 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1028 - accuracy: 0.9712 - val_loss: 0.2374 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1065 - accuracy: 0.9722 - val_loss: 0.2279 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0899 - accuracy: 0.9771 - val_loss: 0.1902 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0824 - accuracy: 0.9795 - val_loss: 0.1907 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 224: 301.18 sec +Time taken for epoch(SUBo) 224: 248.32 sec +<---------------------------------------|Epoch [224] END|---------------------------------------> + +Epoch: 225/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1296 - accuracy: 0.9609 - val_loss: 0.1972 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1254 - accuracy: 0.9619 - val_loss: 0.1699 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1233 - accuracy: 0.9624 - val_loss: 0.2114 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1953 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0820 - accuracy: 0.9780 - val_loss: 0.2077 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0815 - accuracy: 0.9814 - val_loss: 0.2196 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 225: 302.66 sec +Time taken for epoch(SUBo) 225: 248.69 sec +<---------------------------------------|Epoch [225] END|---------------------------------------> + +Epoch: 226/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1353 - accuracy: 0.9604 - val_loss: 0.2359 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.1373 - accuracy: 0.9561 - val_loss: 0.2577 - val_accuracy: 0.9359 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1259 - accuracy: 0.9648 - val_loss: 0.2211 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1084 - accuracy: 0.9707 - val_loss: 0.1719 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1007 - accuracy: 0.9712 - val_loss: 0.1720 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0895 - accuracy: 0.9751 - val_loss: 0.1756 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 226: 313.16 sec +Time taken for epoch(SUBo) 226: 251.41 sec +<---------------------------------------|Epoch [226] END|---------------------------------------> + +Epoch: 227/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1123 - accuracy: 0.9639 - val_loss: 0.1721 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1115 - accuracy: 0.9653 - val_loss: 0.2263 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1077 - accuracy: 0.9639 - val_loss: 0.1975 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0943 - accuracy: 0.9717 - val_loss: 0.2010 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0949 - accuracy: 0.9736 - val_loss: 0.1780 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0812 - accuracy: 0.9771 - val_loss: 0.1900 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 227: 306.72 sec +Time taken for epoch(SUBo) 227: 248.78 sec +<---------------------------------------|Epoch [227] END|---------------------------------------> + +Epoch: 228/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1398 - accuracy: 0.9546 - val_loss: 0.1847 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1483 - accuracy: 0.9551 - val_loss: 0.1827 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1162 - accuracy: 0.9678 - val_loss: 0.2110 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1037 - accuracy: 0.9639 - val_loss: 0.1890 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0836 - accuracy: 0.9775 - val_loss: 0.1704 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.0876 - accuracy: 0.9746 - val_loss: 0.1758 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 228: 307.11 sec +Time taken for epoch(SUBo) 228: 249.60 sec +<---------------------------------------|Epoch [228] END|---------------------------------------> + +Epoch: 229/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 162ms/step - loss: 0.1046 - accuracy: 0.9688 - val_loss: 0.1633 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1031 - accuracy: 0.9702 - val_loss: 0.1893 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1045 - accuracy: 0.9717 - val_loss: 0.1849 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0950 - accuracy: 0.9780 - val_loss: 0.1626 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0764 - accuracy: 0.9800 - val_loss: 0.1711 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0747 - accuracy: 0.9795 - val_loss: 0.1604 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 229: 302.46 sec +Time taken for epoch(SUBo) 229: 249.29 sec +<---------------------------------------|Epoch [229] END|---------------------------------------> + +Epoch: 230/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1203 - accuracy: 0.9648 - val_loss: 0.1849 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.1080 - accuracy: 0.9639 - val_loss: 0.1861 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0951 - accuracy: 0.9697 - val_loss: 0.2135 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.1004 - accuracy: 0.9712 - val_loss: 0.2054 - val_accuracy: 0.9439 +Epoch 5/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0709 - accuracy: 0.9771 - val_loss: 0.2113 - val_accuracy: 0.9439 +Epoch 6/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0771 - accuracy: 0.9766 - val_loss: 0.2083 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 230: 299.92 sec +Time taken for epoch(SUBo) 230: 248.71 sec +<---------------------------------------|Epoch [230] END|---------------------------------------> + +Epoch: 231/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 167ms/step - loss: 0.0909 - accuracy: 0.9707 - val_loss: 0.1829 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0896 - accuracy: 0.9746 - val_loss: 0.1859 - val_accuracy: 0.9423 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0809 - accuracy: 0.9766 - val_loss: 0.1953 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0791 - accuracy: 0.9780 - val_loss: 0.1783 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0703 - accuracy: 0.9800 - val_loss: 0.1679 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0497 - accuracy: 0.9863 - val_loss: 0.1703 - val_accuracy: 0.9455 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 231: 304.17 sec +Time taken for epoch(SUBo) 231: 253.03 sec +<---------------------------------------|Epoch [231] END|---------------------------------------> + +Epoch: 232/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 165ms/step - loss: 0.1107 - accuracy: 0.9683 - val_loss: 0.1718 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.1060 - accuracy: 0.9697 - val_loss: 0.1952 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0898 - accuracy: 0.9746 - val_loss: 0.1595 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0998 - accuracy: 0.9722 - val_loss: 0.1685 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.0773 - accuracy: 0.9795 - val_loss: 0.2042 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0897 - accuracy: 0.9780 - val_loss: 0.1887 - val_accuracy: 0.9407 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 232: 312.43 sec +Time taken for epoch(SUBo) 232: 254.46 sec +<---------------------------------------|Epoch [232] END|---------------------------------------> + +Epoch: 233/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 48s 167ms/step - loss: 0.1260 - accuracy: 0.9575 - val_loss: 0.1891 - val_accuracy: 0.9391 +Epoch 2/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.1052 - accuracy: 0.9688 - val_loss: 0.1659 - val_accuracy: 0.9455 +Epoch 3/6 +256/256 [==============================] - 42s 164ms/step - loss: 0.1140 - accuracy: 0.9688 - val_loss: 0.1445 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0954 - accuracy: 0.9717 - val_loss: 0.1710 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.0933 - accuracy: 0.9761 - val_loss: 0.1612 - val_accuracy: 0.9519 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0744 - accuracy: 0.9814 - val_loss: 0.1741 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 233: 313.69 sec +Time taken for epoch(SUBo) 233: 256.06 sec +<---------------------------------------|Epoch [233] END|---------------------------------------> + +Epoch: 234/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 167ms/step - loss: 0.1023 - accuracy: 0.9683 - val_loss: 0.1438 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 42s 162ms/step - loss: 0.0962 - accuracy: 0.9707 - val_loss: 0.2408 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 42s 162ms/step - loss: 0.0875 - accuracy: 0.9736 - val_loss: 0.1795 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 42s 163ms/step - loss: 0.0846 - accuracy: 0.9722 - val_loss: 0.1669 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 162ms/step - loss: 0.0591 - accuracy: 0.9844 - val_loss: 0.1704 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0565 - accuracy: 0.9873 - val_loss: 0.1818 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 234: 311.13 sec +Time taken for epoch(SUBo) 234: 255.01 sec +<---------------------------------------|Epoch [234] END|---------------------------------------> + +Epoch: 235/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1210 - accuracy: 0.9629 - val_loss: 0.1778 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.1125 - accuracy: 0.9663 - val_loss: 0.1453 - val_accuracy: 0.9519 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.1608 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0843 - accuracy: 0.9775 - val_loss: 0.1615 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0782 - accuracy: 0.9771 - val_loss: 0.1832 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0672 - accuracy: 0.9800 - val_loss: 0.1808 - val_accuracy: 0.9439 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 235: 300.21 sec +Time taken for epoch(SUBo) 235: 251.11 sec +<---------------------------------------|Epoch [235] END|---------------------------------------> + +Epoch: 236/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 164ms/step - loss: 0.1133 - accuracy: 0.9639 - val_loss: 0.1626 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0993 - accuracy: 0.9648 - val_loss: 0.1585 - val_accuracy: 0.9583 +Epoch 3/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0924 - accuracy: 0.9717 - val_loss: 0.1581 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0813 - accuracy: 0.9780 - val_loss: 0.1336 - val_accuracy: 0.9583 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0724 - accuracy: 0.9790 - val_loss: 0.1694 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0585 - accuracy: 0.9839 - val_loss: 0.1735 - val_accuracy: 0.9503 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 236: 302.45 sec +Time taken for epoch(SUBo) 236: 251.67 sec +<---------------------------------------|Epoch [236] END|---------------------------------------> + +Epoch: 237/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1015 - accuracy: 0.9663 - val_loss: 0.1594 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0919 - accuracy: 0.9736 - val_loss: 0.1593 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0886 - accuracy: 0.9746 - val_loss: 0.1714 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0809 - accuracy: 0.9795 - val_loss: 0.1978 - val_accuracy: 0.9503 +Epoch 5/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0690 - accuracy: 0.9829 - val_loss: 0.2800 - val_accuracy: 0.9375 +Epoch 6/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0600 - accuracy: 0.9873 - val_loss: 0.2560 - val_accuracy: 0.9359 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 237: 301.88 sec +Time taken for epoch(SUBo) 237: 251.53 sec +<---------------------------------------|Epoch [237] END|---------------------------------------> + +Epoch: 238/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 47s 166ms/step - loss: 0.1051 - accuracy: 0.9663 - val_loss: 0.2133 - val_accuracy: 0.9423 +Epoch 2/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0934 - accuracy: 0.9717 - val_loss: 0.2560 - val_accuracy: 0.9375 +Epoch 3/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0785 - accuracy: 0.9790 - val_loss: 0.2045 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 41s 161ms/step - loss: 0.0702 - accuracy: 0.9790 - val_loss: 0.2433 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 160ms/step - loss: 0.0706 - accuracy: 0.9800 - val_loss: 0.1769 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0689 - accuracy: 0.9819 - val_loss: 0.1796 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 238: 307.10 sec +Time taken for epoch(SUBo) 238: 252.62 sec +<---------------------------------------|Epoch [238] END|---------------------------------------> + +Epoch: 239/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 46s 163ms/step - loss: 0.1147 - accuracy: 0.9673 - val_loss: 0.1823 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0958 - accuracy: 0.9751 - val_loss: 0.2081 - val_accuracy: 0.9407 +Epoch 3/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0865 - accuracy: 0.9775 - val_loss: 0.2058 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0716 - accuracy: 0.9795 - val_loss: 0.2068 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2146 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 41s 158ms/step - loss: 0.0562 - accuracy: 0.9834 - val_loss: 0.2186 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 239: 303.13 sec +Time taken for epoch(SUBo) 239: 249.48 sec +<---------------------------------------|Epoch [239] END|---------------------------------------> + +Epoch: 240/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.1219 - accuracy: 0.9595 - val_loss: 0.1957 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1010 - accuracy: 0.9717 - val_loss: 0.2189 - val_accuracy: 0.9327 +Epoch 3/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0829 - accuracy: 0.9756 - val_loss: 0.2015 - val_accuracy: 0.9439 +Epoch 4/6 +256/256 [==============================] - 41s 159ms/step - loss: 0.0715 - accuracy: 0.9780 - val_loss: 0.2191 - val_accuracy: 0.9487 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0614 - accuracy: 0.9839 - val_loss: 0.2335 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0522 - accuracy: 0.9858 - val_loss: 0.2491 - val_accuracy: 0.9295 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 240: 296.96 sec +Time taken for epoch(SUBo) 240: 247.49 sec +<---------------------------------------|Epoch [240] END|---------------------------------------> + +Epoch: 241/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0874 - accuracy: 0.9731 - val_loss: 0.2011 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0942 - accuracy: 0.9731 - val_loss: 0.1900 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0867 - accuracy: 0.9731 - val_loss: 0.2119 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0814 - accuracy: 0.9727 - val_loss: 0.2344 - val_accuracy: 0.9455 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0619 - accuracy: 0.9834 - val_loss: 0.2379 - val_accuracy: 0.9487 +Epoch 6/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0528 - accuracy: 0.9868 - val_loss: 0.2390 - val_accuracy: 0.9423 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 241: 301.50 sec +Time taken for epoch(SUBo) 241: 244.85 sec +<---------------------------------------|Epoch [241] END|---------------------------------------> + +Epoch: 242/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 157ms/step - loss: 0.1068 - accuracy: 0.9692 - val_loss: 0.2088 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.0962 - accuracy: 0.9692 - val_loss: 0.2827 - val_accuracy: 0.9343 +Epoch 3/6 +256/256 [==============================] - 39s 153ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.2028 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0831 - accuracy: 0.9761 - val_loss: 0.2217 - val_accuracy: 0.9551 +Epoch 5/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0908 - accuracy: 0.9775 - val_loss: 0.2048 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0678 - accuracy: 0.9814 - val_loss: 0.1931 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 242: 289.32 sec +Time taken for epoch(SUBo) 242: 241.25 sec +<---------------------------------------|Epoch [242] END|---------------------------------------> + +Epoch: 243/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 44s 158ms/step - loss: 0.1125 - accuracy: 0.9692 - val_loss: 0.1588 - val_accuracy: 0.9487 +Epoch 2/6 +256/256 [==============================] - 40s 154ms/step - loss: 0.0962 - accuracy: 0.9668 - val_loss: 0.1660 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0947 - accuracy: 0.9717 - val_loss: 0.2053 - val_accuracy: 0.9343 +Epoch 4/6 +256/256 [==============================] - 39s 154ms/step - loss: 0.0780 - accuracy: 0.9756 - val_loss: 0.1659 - val_accuracy: 0.9471 +Epoch 5/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0762 - accuracy: 0.9805 - val_loss: 0.1947 - val_accuracy: 0.9407 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0544 - accuracy: 0.9844 - val_loss: 0.1827 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 243: 289.77 sec +Time taken for epoch(SUBo) 243: 242.82 sec +<---------------------------------------|Epoch [243] END|---------------------------------------> + +Epoch: 244/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9717 - val_loss: 0.1976 - val_accuracy: 0.9439 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0864 - accuracy: 0.9775 - val_loss: 0.2101 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1914 - val_accuracy: 0.9487 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2286 - val_accuracy: 0.9375 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0735 - accuracy: 0.9819 - val_loss: 0.2039 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0471 - accuracy: 0.9897 - val_loss: 0.2055 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 244: 292.32 sec +Time taken for epoch(SUBo) 244: 245.77 sec +<---------------------------------------|Epoch [244] END|---------------------------------------> + +Epoch: 245/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1215 - accuracy: 0.9648 - val_loss: 0.1895 - val_accuracy: 0.9455 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.1283 - accuracy: 0.9629 - val_loss: 0.1734 - val_accuracy: 0.9439 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0933 - accuracy: 0.9731 - val_loss: 0.1550 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0845 - accuracy: 0.9746 - val_loss: 0.1631 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0857 - accuracy: 0.9731 - val_loss: 0.1576 - val_accuracy: 0.9583 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0706 - accuracy: 0.9824 - val_loss: 0.1603 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 245: 293.35 sec +Time taken for epoch(SUBo) 245: 246.05 sec +<---------------------------------------|Epoch [245] END|---------------------------------------> + +Epoch: 246/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0914 - accuracy: 0.9771 - val_loss: 0.1657 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1083 - accuracy: 0.9697 - val_loss: 0.1844 - val_accuracy: 0.9503 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0831 - accuracy: 0.9756 - val_loss: 0.1675 - val_accuracy: 0.9567 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0649 - accuracy: 0.9800 - val_loss: 0.1947 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0551 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0458 - accuracy: 0.9897 - val_loss: 0.1977 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 246: 292.03 sec +Time taken for epoch(SUBo) 246: 245.80 sec +<---------------------------------------|Epoch [246] END|---------------------------------------> + +Epoch: 247/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 162ms/step - loss: 0.0973 - accuracy: 0.9727 - val_loss: 0.1630 - val_accuracy: 0.9503 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0989 - accuracy: 0.9702 - val_loss: 0.1590 - val_accuracy: 0.9551 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0671 - accuracy: 0.9800 - val_loss: 0.1650 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0731 - accuracy: 0.9805 - val_loss: 0.1396 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9854 - val_loss: 0.1649 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0560 - accuracy: 0.9883 - val_loss: 0.1677 - val_accuracy: 0.9535 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 247: 294.16 sec +Time taken for epoch(SUBo) 247: 247.01 sec +<---------------------------------------|Epoch [247] END|---------------------------------------> + +Epoch: 248/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0900 - accuracy: 0.9756 - val_loss: 0.1515 - val_accuracy: 0.9551 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0812 - accuracy: 0.9761 - val_loss: 0.1617 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.1895 - val_accuracy: 0.9519 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0630 - accuracy: 0.9858 - val_loss: 0.1660 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0626 - accuracy: 0.9834 - val_loss: 0.1958 - val_accuracy: 0.9535 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0587 - accuracy: 0.9849 - val_loss: 0.1824 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 248: 293.13 sec +Time taken for epoch(SUBo) 248: 246.15 sec +<---------------------------------------|Epoch [248] END|---------------------------------------> + +Epoch: 249/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.1567 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.1036 - accuracy: 0.9717 - val_loss: 0.1435 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0755 - accuracy: 0.9780 - val_loss: 0.1969 - val_accuracy: 0.9503 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0742 - accuracy: 0.9775 - val_loss: 0.1623 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1840 - val_accuracy: 0.9551 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0557 - accuracy: 0.9844 - val_loss: 0.1914 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 249: 293.33 sec +Time taken for epoch(SUBo) 249: 245.80 sec +<---------------------------------------|Epoch [249] END|---------------------------------------> + +Epoch: 250/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.1097 - accuracy: 0.9658 - val_loss: 0.1761 - val_accuracy: 0.9519 +Epoch 2/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0999 - accuracy: 0.9697 - val_loss: 0.1736 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 155ms/step - loss: 0.0943 - accuracy: 0.9673 - val_loss: 0.1766 - val_accuracy: 0.9535 +Epoch 4/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0878 - accuracy: 0.9746 - val_loss: 0.1743 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0821 - accuracy: 0.9727 - val_loss: 0.1941 - val_accuracy: 0.9503 +Epoch 6/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0683 - accuracy: 0.9800 - val_loss: 0.1990 - val_accuracy: 0.9487 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 250: 292.13 sec +Time taken for epoch(SUBo) 250: 244.59 sec +<---------------------------------------|Epoch [250] END|---------------------------------------> + +Epoch: 251/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 160ms/step - loss: 0.0972 - accuracy: 0.9707 - val_loss: 0.1764 - val_accuracy: 0.9471 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0835 - accuracy: 0.9736 - val_loss: 0.1675 - val_accuracy: 0.9567 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0819 - accuracy: 0.9785 - val_loss: 0.1513 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0704 - accuracy: 0.9800 - val_loss: 0.1564 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0581 - accuracy: 0.9839 - val_loss: 0.1602 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0579 - accuracy: 0.9849 - val_loss: 0.1547 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.146798238158226. Not saving model. +Time taken for epoch(FULL) 251: 292.96 sec +Time taken for epoch(SUBo) 251: 246.01 sec +<---------------------------------------|Epoch [251] END|---------------------------------------> + +Epoch: 252/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0999 - accuracy: 0.9707 - val_loss: 0.1387 - val_accuracy: 0.9567 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0826 - accuracy: 0.9756 - val_loss: 0.1897 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0722 - accuracy: 0.9775 - val_loss: 0.1514 - val_accuracy: 0.9615 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0767 - accuracy: 0.9780 - val_loss: 0.1432 - val_accuracy: 0.9599 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0720 - accuracy: 0.9814 - val_loss: 0.1414 - val_accuracy: 0.9599 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0650 - accuracy: 0.9795 - val_loss: 0.1418 - val_accuracy: 0.9583 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.146798238158226 to 0.14178654551506042. Saving model. +Time taken for epoch(FULL) 252: 295.30 sec +Time taken for epoch(SUBo) 252: 246.41 sec +<---------------------------------------|Epoch [252] END|---------------------------------------> + +Epoch: 253/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1538 - val_accuracy: 0.9599 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0866 - accuracy: 0.9761 - val_loss: 0.1447 - val_accuracy: 0.9599 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0777 - accuracy: 0.9800 - val_loss: 0.1519 - val_accuracy: 0.9583 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0644 - accuracy: 0.9829 - val_loss: 0.1863 - val_accuracy: 0.9423 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0585 - accuracy: 0.9868 - val_loss: 0.1939 - val_accuracy: 0.9471 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0511 - accuracy: 0.9878 - val_loss: 0.1766 - val_accuracy: 0.9471 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 253: 293.56 sec +Time taken for epoch(SUBo) 253: 246.59 sec +<---------------------------------------|Epoch [253] END|---------------------------------------> + +Epoch: 254/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.1089 - accuracy: 0.9673 - val_loss: 0.1512 - val_accuracy: 0.9583 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0968 - accuracy: 0.9653 - val_loss: 0.1482 - val_accuracy: 0.9535 +Epoch 3/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0950 - accuracy: 0.9658 - val_loss: 0.1955 - val_accuracy: 0.9391 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0852 - accuracy: 0.9756 - val_loss: 0.1505 - val_accuracy: 0.9567 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0796 - accuracy: 0.9795 - val_loss: 0.1484 - val_accuracy: 0.9567 +Epoch 6/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0683 - accuracy: 0.9810 - val_loss: 0.1534 - val_accuracy: 0.9567 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 254: 293.79 sec +Time taken for epoch(SUBo) 254: 246.40 sec +<---------------------------------------|Epoch [254] END|---------------------------------------> + +Epoch: 255/256 | [Fine tuning] +Shuffling data... +Taking a subset of [2048]... +Augmenting data... +Setting model OneCycleLr::maxlr to [0.001500]... +Setting model subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +256/256 [==============================] - 45s 161ms/step - loss: 0.0860 - accuracy: 0.9746 - val_loss: 0.1747 - val_accuracy: 0.9535 +Epoch 2/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0919 - accuracy: 0.9727 - val_loss: 0.1806 - val_accuracy: 0.9487 +Epoch 3/6 +256/256 [==============================] - 40s 156ms/step - loss: 0.0816 - accuracy: 0.9756 - val_loss: 0.1677 - val_accuracy: 0.9551 +Epoch 4/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0612 - accuracy: 0.9834 - val_loss: 0.1808 - val_accuracy: 0.9535 +Epoch 5/6 +256/256 [==============================] - 40s 157ms/step - loss: 0.0564 - accuracy: 0.9844 - val_loss: 0.2127 - val_accuracy: 0.9391 +Epoch 6/6 +256/256 [==============================] - 40s 158ms/step - loss: 0.0513 - accuracy: 0.9883 - val_loss: 0.1953 - val_accuracy: 0.9519 +Subset training done. +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.14178654551506042. Not saving model. +Time taken for epoch(FULL) 255: 293.85 sec +Time taken for epoch(SUBo) 255: 246.35 sec +<---------------------------------------|Epoch [255] END|---------------------------------------> +Training done. + diff --git a/backup/V5/Model_T&T.ipynb b/backup/V5/Model_T&T.ipynb index 5f316c3..cc718c9 100644 --- a/backup/V5/Model_T&T.ipynb +++ b/backup/V5/Model_T&T.ipynb @@ -1,13505 +1,13505 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# keras/TF model\n", - "
\n",
-    " Copyright (c) 2023 Aydin Hamedi\n",
-    " \n",
-    " This software is released under the MIT License.\n",
-    " https://opensource.org/licenses/MIT\n",
-    "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pre Conf" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-25T12:17:58.501889500Z", - "start_time": "2023-12-25T12:17:58.486457700Z" - }, - "notebookRunGroups": { - "groupValue": "21" - } - }, - "outputs": [], - "source": [ - "CPU_only = False # True to Force TF to use the cpu" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pylibs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import time\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", - "if CPU_only:\n", - " os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n", - "import cv2\n", - "import glob \n", - "import keras\n", - "import pprint\n", - "import random\n", - "import shutil\n", - "import gzip\n", - "import glob\n", - "import pickle\n", - "import datetime\n", - "import subprocess\n", - "import gpu_control\n", - "import numpy as np\n", - "import pandas as pd\n", - "from tqdm import tqdm\n", - "import seaborn as sns\n", - "from hyperas import optim\n", - "# import tensorflow_addons as tfa\n", - "from keras_adabound import AdaBound\n", - "from importlib import reload\n", - "from keras.losses import categorical_crossentropy\n", - "import tensorflow as tf\n", - "from keras.models import Model\n", - "from scipy.ndimage import zoom\n", - "import matplotlib.pyplot as plt\n", - "from model_profiler import model_profiler\n", - "from keras_gradient_noise import add_gradient_noise\n", - "from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax\n", - "# from tensorflow_addons.optimizers import Yogi\n", - "from adabelief_tf import AdaBeliefOptimizer\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from imblearn.over_sampling import SMOTE\n", - "from keras.regularizers import l2\n", - "from keras.models import load_model\n", - "from matplotlib import pyplot as plt\n", - "from PIL import Image, ImageDraw, ImageFont\n", - "from keras import Sequential\n", - "from random import randint, choice, shuffle\n", - "from keras.callbacks import EarlyStopping\n", - "from keras.callbacks import TensorBoard\n", - "from keras.utils import to_categorical\n", - "from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler\n", - "from sklearn.model_selection import train_test_split\n", - "from keras.preprocessing.image import ImageDataGenerator\n", - "from keras.layers import Conv2D,\\\n", - " MaxPooling2D,\\\n", - " Flatten,\\\n", - " Dense,\\\n", - " Dropout,\\\n", - " BatchNormalization,\\\n", - " SeparableConv2D,\\\n", - " Input, Concatenate,\\\n", - " GlobalAveragePooling2D,\\\n", - " CuDNNLSTM, concatenate,\\\n", - " Reshape, Multiply\n", - "# Utils\n", - "from Utils.one_cycle import OneCycleLr\n", - "from Utils.lr_find import LrFinder\n", - "from Utils.print_color_V2_NEW import print_Color_V2\n", - "from Utils.print_color_V1_OLD import print_Color\n", - "from Utils.Other import *\n", - "# Other\n", - "tf.get_logger().setLevel('ERROR')\n", - "physical_devices = tf.config.list_physical_devices('GPU')\n", - "for gpu_instance in physical_devices:\n", - " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conf\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data processing conf" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [], - "source": [ - "# Directory paths# Directory paths for training, test and validation image data\n", - "train_dir = 'Database\\\\Train\\\\Data\\\\train'\n", - "test_dir = 'Database\\\\Train\\\\Data\\\\test'\n", - "validation_dir = 'Database\\\\Train\\\\Data\\\\val'\n", - "img_res = [224, 224, 3]\n", - "# img_res = [324, 324, 3]\n", - "# img_res = [224, 224, 3]\n", - "# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!)\n", - "interpolation_order_IFG = 2\n", - "categorical_IMP = True\n", - "Make_EV_DATA = False\n", - "R_fill_mode = True\n", - "add_img_grain = True\n", - "Save_TS = True\n", - "Use_SMOTE = False # (⚠️Beta⚠️)\n", - "ADBD = 1\n", - "OP_HDC = False\n", - "SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1\n", - "LNTS = 0\n", - "Debug_OUT = False\n", - "adjust_brightness_Mode = True\n", - "RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️)\n", - "scale_data_NP_M = False # (⚠️deprecated⚠️)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "SAVE_TYPE = 'H5'\n", - "Use_mixed_float16 = False\n", - "#Other\n", - "if Use_mixed_float16:\n", - " tf.keras.mixed_precision.set_global_policy('mixed_float16')\n", - "else:\n", - " tf.keras.mixed_precision.set_global_policy('float32')\n", - " \n", - "print(tf.keras.mixed_precision.global_policy())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## data processing \n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mUsing Def IDG...\u001b[0m\n", - "Found 8818 images belonging to 2 classes.\n", - "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", - "\u001b[0;33mMaking categorical data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m1\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "> Generating ADB[1/1]...\n", - "> β”œβ”€β”€β”€Applying adaptive histogram equalization...\n", - "> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = 0.8\n", - "> └───Adding the Generated ADB...\n", - "\u001b[0;33mNormalizing image data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m64.36% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m35.64% NORMAL\u001b[0m\n", - "\u001b[0;33mSetting LNTS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m17636\u001b[0m\n", - "\u001b[0;33mshuffling data...\u001b[0m\n", - "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2023_m12_d25-h18_m09_s57\u001b[0m\n", - "\u001b[0;32mDone.\u001b[0m\n" - ] - } - ], - "source": [ - "#Z_SCORE_normalize\n", - "def Z_SCORE_normalize(arr):\n", - " arr = arr.astype('float32')\n", - " mean = np.mean(arr)\n", - " std_dev = np.std(arr)\n", - " arr = (arr - mean) / std_dev\n", - " return arr\n", - "#normalize_TO_RANGE\n", - "def normalize_TO_RANGE(arr, min_val, max_val):\n", - " arr = arr.astype('float32')\n", - " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", - " arr = arr * (max_val - min_val) + min_val\n", - " return arr\n", - "#scale_data\n", - "def scale_data_NP(data):\n", - " if scale_data_NP_M:\n", - " data = data.astype('float32')\n", - " data = (data - 127.5) / 127.5\n", - " return data\n", - " else:\n", - " return data / 255\n", - "#add_image_grain\n", - "def add_image_grain(image, intensity = 0.01):\n", - " # Generate random noise array\n", - " noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8)\n", - "\n", - " # Scale the noise array\n", - " scaled_noise = (noise * intensity).astype(np.float32)\n", - " # Add the noise to the image\n", - " noisy_image = cv2.add(image, scaled_noise)\n", - "\n", - " return noisy_image\n", - "#apply_clahe_rgb_array\n", - "def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)):\n", - " # Create a CLAHE object\n", - " clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n", - " \n", - " # Iterate over each image in the array\n", - " for i in range(len(images)):\n", - " # Split the image into color channels\n", - " b, g, r = cv2.split(images[i])\n", - " \n", - " # Convert the channels to the appropriate format\n", - " b = cv2.convertScaleAbs(b)\n", - " g = cv2.convertScaleAbs(g)\n", - " r = cv2.convertScaleAbs(r)\n", - " \n", - " # Apply adaptive histogram equalization to each channel\n", - " equalized_b = clahe.apply(b)\n", - " equalized_g = clahe.apply(g)\n", - " equalized_r = clahe.apply(r)\n", - "\n", - " # Merge the equalized channels back into an image\n", - " equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r))\n", - "\n", - " # Replace the original image with the equalized image in the array\n", - " images[i] = equalized_image\n", - "\n", - " return images\n", - "#noise_func\n", - "def noise_func(image):\n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.05, 0.05)\n", - " intensityL1 = random.uniform(-0.04, 0.04)\n", - " else:\n", - " intensityL2 = random.uniform(-0.06, 0.06)\n", - " intensityL1 = random.uniform(-0.04, 0.04)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 64)\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image.shape[0], block_size_L2):\n", - " for j in range(0, image.shape[1], block_size_L2):\n", - " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image = new_image \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image.shape[0], block_size_L1):\n", - " for j in range(0, image.shape[1], block_size_L1):\n", - " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026\n", - " new_image = add_image_grain(new_image, intensity=intensity)\n", - " return new_image\n", - "#shuffle_data\n", - "def shuffle_data(x, y):\n", - " indices = np.arange(x.shape[0])\n", - " np.random.shuffle(indices)\n", - " x = x[indices]\n", - " y = y[indices]\n", - " return x, y\n", - "#save_images_to_dir\n", - "def save_images_to_dir(images, labels, dir_path):\n", - " # create the directory if it doesn't exist\n", - " if not os.path.exists(dir_path):\n", - " os.makedirs(dir_path)\n", - " # iterate over the images and labels\n", - " for i, (image, label) in enumerate(zip(images, labels)):\n", - " # get the class label\n", - " class_label = np.argmax(label)\n", - " # create the file path\n", - " file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png')\n", - " # save the image to the file path\n", - " plt.imsave(file_path, image.squeeze())\n", - " # compress the directory\n", - " shutil.make_archive(dir_path, 'gztar', dir_path)\n", - " # remove the original directory\n", - " shutil.rmtree(dir_path)\n", - "#Debug_img_Save\n", - "def Debug_img_Save(img, id = 'DEF'): \n", - " SITD = np.random.choice(img.shape[0], size=400, replace=False)\n", - " S_dir = f'Samples\\\\Debug\\\\{id}\\\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", - " save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir)\n", - "# Create an ImageDataGenerator for the training set\n", - "if OP_HDC:\n", - " print_Color('Using OP_HDC IDG...', ['yellow'])\n", - " train_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.24, \n", - " shear_range=0.22,\n", - " width_shift_range=0.21,\n", - " brightness_range=(0.86, 1.1),\n", - " height_shift_range=0.21,\n", - " channel_shift_range=100,\n", - " featurewise_center=False,\n", - " featurewise_std_normalization=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " fill_mode='nearest', # constant\n", - " preprocessing_function=noise_func\n", - " )\n", - "else:\n", - " print_Color('Using Def IDG...', ['yellow'])\n", - " train_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.26, \n", - " shear_range=0.25,\n", - " width_shift_range=0.25,\n", - " brightness_range=(0.78, 1.1),\n", - " height_shift_range=0.25,\n", - " channel_shift_range=100,\n", - " featurewise_center=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " featurewise_std_normalization=False,\n", - " fill_mode='nearest', # constant\n", - " preprocessing_function=noise_func\n", - " )\n", - "train_datagen_SM = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " vertical_flip=False,\n", - " rotation_range=20,\n", - " zoom_range=0.07, \n", - " shear_range=0.07,\n", - " width_shift_range=0.07,\n", - " brightness_range=(0.99, 1.01),\n", - " height_shift_range=0.07,\n", - " channel_shift_range=0,\n", - " featurewise_center=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " featurewise_std_normalization=False\n", - ")\n", - "# Create an iterator for the training set\n", - "train_generator_SM = train_datagen_SM.flow_from_directory(\n", - " train_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]),\n", - " class_mode='binary')\n", - "# Create an ImageDataGenerator for the validation set (OP)\n", - "if Make_EV_DATA:\n", - " val_datagen = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " zoom_range = 0.01, \n", - " width_shift_range=0.01, \n", - " interpolation_order=interpolation_order_IFG,\n", - " height_shift_range=0.01)\n", - "\n", - " # Create an iterator for the validation set\n", - " val_generator = val_datagen.flow_from_directory(\n", - " validation_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]),\n", - " class_mode='binary',\n", - " color_mode='rgb')\n", - "\n", - " # Create an ImageDataGenerator for the test set\n", - " test_datagen = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " zoom_range = 0.01, \n", - " width_shift_range=0.01, \n", - " interpolation_order=interpolation_order_IFG,\n", - " height_shift_range=0.01)\n", - "\n", - " # Create an iterator for the test set\n", - " test_generator = test_datagen.flow_from_directory(\n", - " test_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]),\n", - " class_mode='binary',\n", - " color_mode='rgb')\n", - "# Load all images and labels into memory\n", - "print_Color('Loading all images and labels into memory...', ['yellow'])\n", - "x_train, y_train = next(iter(train_generator_SM))\n", - "if Make_EV_DATA:\n", - " x_val, y_val = next(iter(val_generator))\n", - " x_test, y_test = next(iter(test_generator))\n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG\n", - "# fit parameters from data\n", - "# train_datagen.fit(x_train)\n", - "#to_categorical (TEMP)\n", - "if categorical_IMP:\n", - " print_Color('Making categorical data...', ['yellow'])\n", - " y_train = to_categorical(y_train, num_classes=2)\n", - " if Make_EV_DATA:\n", - " y_val = to_categorical(y_val, num_classes=2)\n", - " y_test = to_categorical(y_test, num_classes=2)\n", - "# Use_SMOTE\n", - "if Use_SMOTE:\n", - " print_Color('SMOTE...', ['yellow'])\n", - " # Convert y_train from one-hot encoding to label encoding\n", - " y_train_label_encoded = np.argmax(y_train, axis=1)\n", - "\n", - " # Print the original label distribution\n", - " unique, counts = np.unique(y_train_label_encoded, return_counts=True)\n", - " print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True)\n", - "\n", - " # Use SMOTE to oversample the minority class\n", - " smote = SMOTE(random_state=42)\n", - " x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded)\n", - "\n", - " # Print the resampled label distribution\n", - " unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True)\n", - " print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True)\n", - "\n", - " # Reshape x_train_res back to the original x_train shape\n", - " x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3])\n", - "\n", - " # Convert y_train_res from label encoding back to one-hot encoding\n", - " y_train_res = to_categorical(y_train_res_label_encoded)\n", - "\n", - " # Calculate the ratio of two labels after resampling\n", - " pneumonia_count = np.sum(y_train_res[:, 1])\n", - " total_count = y_train_res.shape[0]\n", - " label_ratio_res = pneumonia_count / total_count\n", - " label_ratio_percentage_res = label_ratio_res * 100\n", - "\n", - " # Replace the original data with the resampled data\n", - " x_train = x_train_res\n", - " y_train = y_train_res\n", - "\n", - " # Delete the resampled data to free up memory\n", - " del x_train_res, y_train_res_label_encoded, y_train_res\n", - "# Generating augmented data\n", - "print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...',\n", - " ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'],\n", - " advanced_mode=True)\n", - "if ADBD > 0:\n", - " for i in range(ADBD):\n", - " # ADB_clip_limit Scheduler>>>\n", - " if i == 0:\n", - " ADB_clip_limit = 0.8\n", - " else:\n", - " #V1>>>\n", - " CL_SLM = 2.4\n", - " ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05)\n", - " # Try it in win graphing calculator copy and paste:\n", - " # β”Œ-------------┬--┬---------------┐\n", - " # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚\n", - " # β””-------------β”΄--β”΄---------------β”˜\n", - " #V2>>>\n", - " # CL_SLM_2 = 1.4\n", - " # CL_SLM_Start_2 = 2\n", - " # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) \n", - " # Try it in win graphing calculator copy and paste:\n", - " # β”Œ-----------------┬--┬-------------------┐\n", - " # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚\n", - " # β””-----------------β”΄--β”΄-------------------β”˜\n", - " print(f'> Generating ADB[{i+1}/{ADBD}]...')\n", - " # prepare an iterators to scale images\n", - " train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train))\n", - "\n", - " # get augmented data\n", - " x_train_augmented, y_train_augmented = train_iterator.next()\n", - " print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...')\n", - " print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}')\n", - " x_train_augmented = np.clip(x_train_augmented, 0, 255) \n", - " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG\n", - " #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", - " x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss\n", - " print(f'> └───Adding the Generated ADB...')\n", - " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG\n", - " # append augmented data to original data\n", - " x_train = np.concatenate([x_train, x_train_augmented])\n", - " y_train = np.concatenate([y_train, y_train_augmented])\n", - " #free up memory\n", - " del y_train_augmented\n", - " del x_train_augmented\n", - "# normalizing \n", - "print_Color('Normalizing image data...', ['yellow'])\n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG\n", - "x_train = np.clip(x_train, 0, 255)\n", - "if RANGE_NOM:\n", - " x_train = scale_data_NP(x_train)\n", - "y_train = np.array(y_train) \n", - "if Make_EV_DATA:\n", - " x_test = np.clip(x_test, 0, 255) \n", - " x_val = np.clip(x_val, 0, 255) \n", - " if RANGE_NOM:\n", - " x_val = scale_data_NP(x_val)\n", - " y_val = np.array(y_val) \n", - " if RANGE_NOM: \n", - " x_test = scale_data_NP(x_test)\n", - " y_test = np.array(y_test) \n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG\n", - "# Check the data type of image data\n", - "print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True)\n", - "# Check the range of image data\n", - "print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", - "# Calculate the ratio of two labels\n", - "if categorical_IMP:\n", - " label_sums = np.sum(y_train, axis=0)\n", - " label_ratio = label_sums / (np.sum(y_train) + 1e-10)\n", - " label_ratio_percentage = label_ratio * 100\n", - " print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL',\n", - " ['normal', 'red', 'magenta', 'green'], advanced_mode=True) \n", - "print_Color('Setting LNTS...', ['yellow'])\n", - "# Get the total number of samples in the arrays\n", - "num_samples = x_train.shape[0]\n", - "print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", - "if LNTS != 0:\n", - " print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True)\n", - " print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True)\n", - " # Generate random indices to select LNTS samples\n", - " indices = np.random.choice(num_samples, size=LNTS, replace=False)\n", - " # Select the samples using the generated indices\n", - " x_selected = x_train[indices]\n", - " y_selected = y_train[indices]\n", - " x_train = x_selected\n", - " y_train = y_selected\n", - " #free up memory\n", - " del x_selected\n", - " del y_selected\n", - " del indices\n", - " #Debug\n", - " num_samples = x_train.shape[0]\n", - " print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", - "# Shuffle the training data\n", - "print_Color('shuffling data...', ['yellow'])\n", - "x_train, y_train = shuffle_data(x_train, y_train)\n", - "#save_images_to_dir \n", - "if Save_TS:\n", - " print_Color('Saving TS...', ['yellow'])\n", - " SITD = np.random.choice(num_samples, size=400, replace=False)\n", - " S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True)\n", - " if RANGE_NOM:\n", - " if scale_data_NP_M:\n", - " save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir)\n", - " else:\n", - " save_images_to_dir(x_train[SITD], y_train[SITD], S_dir)\n", - " else:\n", - " save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir)\n", - "print_Color('Done.', ['green'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save EV Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.save(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy', x_val)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy', y_val)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy', x_test)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy', y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load EV Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "notebookRunGroups": { - "groupValue": "1" - } - }, - "outputs": [], - "source": [ - "x_val = np.load(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy')\n", - "y_val = np.load(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy')\n", - "x_test = np.load(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy')\n", - "y_test = np.load(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Analyzation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import seaborn as sns\n", - "from scipy.stats import zscore\n", - "\n", - "# Select a subset of your data\n", - "subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels\n", - "subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values\n", - "indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False)\n", - "indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False)\n", - "subset_pixels = x_train[indices_pixels]\n", - "subset_mean = x_train[indices_mean]\n", - "\n", - "# Reshape the data for calculating Z-scores\n", - "reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1])\n", - "reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1])\n", - "\n", - "# Calculate the mean intensity\n", - "mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1)\n", - "mean_intensity_mean = reshaped_data_mean.mean(axis=-1)\n", - "\n", - "# Stack the mean intensity with the reshaped data\n", - "data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)])\n", - "data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)])\n", - "\n", - "# Calculate Z-scores\n", - "z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))\n", - "z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0))\n", - "\n", - "# Identify outliers\n", - "outliers_pixels = np.where(z_scores_pixels > 3)\n", - "outliers_mean = np.where(z_scores_mean > 3)\n", - "\n", - "# Create a 3D scatter plot for RGB channels\n", - "fig = plt.figure(figsize=(10, 20))\n", - "\n", - "# Plot for individual pixels\n", - "ax = fig.add_subplot(211, projection='3d')\n", - "ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1)\n", - "ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red')\n", - "ax.set_title('Z-Score Scatter Plot for Individual Pixels')\n", - "ax.set_xlabel('Red')\n", - "ax.set_ylabel('Green')\n", - "ax.set_zlabel('Blue')\n", - "\n", - "# Plot for mean RGB values\n", - "ax = fig.add_subplot(212, projection='3d')\n", - "ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1)\n", - "ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red')\n", - "ax.set_title('Z-Score Scatter Plot for Mean RGB Values')\n", - "ax.set_xlabel('Red')\n", - "ax.set_ylabel('Green')\n", - "ax.set_zlabel('Blue')\n", - "\n", - "# Density plot of the mean intensity\n", - "plt.figure(figsize=(10, 5))\n", - "sns.kdeplot(data=z_scores_pixels[:, -1], fill=True)\n", - "plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels')\n", - "plt.xlabel('Z-Score')\n", - "\n", - "sns.kdeplot(data=z_scores_mean[:, -1], fill=True)\n", - "plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values')\n", - "plt.xlabel('Z-Score')\n", - "\n", - "# Display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1\n", - "```\n", - "recommended: ⚠️\n", - "statuses: Ready\n", - "Working: βœ…\n", - "Max fine tuned acc: β‰…95.1\n", - "Max fine tuned acc TLRev2: N/A\n", - "type: transfer learning>>>(EfficientNetB7)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.applications import EfficientNetB7\n", - "\n", - "EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax')\n", - "# define new model\n", - "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.summary()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.1\n", - "```\n", - "recommended: ❌\n", - "statuses: S.Ready (can improve)\n", - "Working: ❌\n", - "Max fine tuned acc: β‰…93.2\n", - "Max fine tuned acc TLRev2: N/A\n", - "type: transfer learning>>>(ConvNeXtLarge)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.applications import ConvNeXtLarge\n", - "\n", - "ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False)\n", - "# define new model\n", - "model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.summary()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "source": [ - "### Rev1.2\n", - "```\n", - "recommended: βœ…\n", - "statuses: Ready\n", - "Working: βœ…\n", - "Max fine tuned acc: 95.3\n", - "Max fine tuned acc TLRev2: 96.96\n", - "type: transfer learning>>>(EfficientNetB7::CCL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from efficientnet.keras import EfficientNetB7 as KENB7\n", - "# FUNC\n", - "def Eff_B7_NS(freeze_layers):\n", - " base_model = KENB7(input_shape=(\n", - " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) /\n", - " len(base_model.layers)) * 100\n", - " print(\n", - " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(\n", - " inputs=base_model.input, outputs=predictions)\n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " # OPT/compile\n", - " opt = SGD(momentum=0.9, nesterov=False)\n", - " # opt = Nadam()\n", - " # opt = Adamax()\n", - " # opt = RMSprop(momentum=0.9)\n", - " # opt = Adagrad()\n", - " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B7_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.3\n", - "```\n", - "recommended: ❌\n", - "statuses: Test\n", - "Working: βœ…\n", - "Max fine tuned acc: ⚠️\n", - "Max fine tuned acc TLRev2: ⚠️\n", - "type: transfer learning>>>(EfficientNetB7|Xception::CCL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from efficientnet.keras import EfficientNetB7 as KENB7\n", - "from keras.applications.xception import Xception\n", - "\n", - "#FUNC\n", - "def Combo_Model(freeze_layers1, freeze_layers2):\n", - " # Define a common input\n", - " common_input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", - "\n", - " # Base model 1\n", - " base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " # base_model1.load_weights('models\\Ready\\Other\\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True)\n", - " base_model1_out = base_model1(common_input)\n", - " \n", - " # Base model 2\n", - " base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False)\n", - " # base_model1.load_weights('models\\Ready\\Other\\Xception_PRET.h5', by_name=True, skip_mismatch=True)\n", - " base_model2_out = base_model2(common_input)\n", - "\n", - " print('Total base_model1 layers: ', len(base_model1.layers))\n", - " print('Total base_model2 layers: ', len(base_model2.layers))\n", - " \n", - " # Freeze the specified number of layers in both models\n", - " for layer in base_model1.layers[:freeze_layers1]:\n", - " layer.trainable = False\n", - " for layer in base_model2.layers[:freeze_layers2]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest in both models\n", - " for layer in base_model1.layers[freeze_layers1:]:\n", - " layer.trainable = True\n", - " for layer in base_model2.layers[freeze_layers2:]:\n", - " layer.trainable = True\n", - "\n", - " # Combine the output of the two base models\n", - " combined = concatenate([GlobalAveragePooling2D()(base_model1_out), GlobalAveragePooling2D()(base_model2_out)])\n", - "\n", - " # adding CDL\n", - " Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined)\n", - " Dropout_L1 = Dropout(0.4)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " combo_model = Model(inputs=common_input, outputs=predictions) \n", - " print('Total model layers: ', len(combo_model.layers))\n", - " \n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return combo_model\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers_1 = 0\n", - "freeze_layers_2 = 0\n", - "model = Combo_Model(freeze_layers_1, freeze_layers_2)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.4\n", - "```\n", - "recommended: ⚠️\n", - "statuses: Test\n", - "Working: βœ…\n", - "Max fine tuned acc: ⚠️\n", - "Max fine tuned acc TLRev2: β‰…95.64\n", - "type: transfer learning>>>(EfficientNetV2XL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras_efficientnet_v2 import EfficientNetV2XL\n", - "\n", - "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n", - "# define new model\n", - "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", - "\n", - "# compile model\n", - "# opt = SGD(momentum=0.9)\n", - "opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n", - "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "freeze_layers = 0\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from efficientnet.keras import EfficientNetL2 as KENBL2\n", - "#FUNC\n", - "def Eff_B7_NS(freeze_layers):\n", - " base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n", - " weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n", - " include_top=False,\n", - " drop_connect_rate=0)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n", - " print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", - " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) \n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return model_EfficientNetB7_NS\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B7_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta2" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aydin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer VarianceScaling is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-s-imagenet.h5\n", - "87846816/87846816 [==============================] - 430s 5us/step\n", - ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-s-imagenet.h5\n", - "Model: \"model_2\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 24 648 ['input_3[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 24 96 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_swish (Activation) (None, 112, 112, 24 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 24 5184 ['stem_swish[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 24 96 ['stack_0_block0_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block0_fu_swish (Activ (None, 112, 112, 24 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add (Add) (None, 112, 112, 24 0 ['stem_swish[0][0]', Y \n", - " ) 'stack_0_block0_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 24 5184 ['add[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 24 96 ['stack_0_block1_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block1_fu_swish (Activ (None, 112, 112, 24 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_1 (Add) (None, 112, 112, 24 0 ['add[0][0]', Y \n", - " ) 'stack_0_block1_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_1_block0_sortcut_conv (C (None, 56, 56, 96) 20736 ['add_1[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 96) 384 ['stack_1_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block0_sortcut_swish ( (None, 56, 56, 96) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 48) 4608 ['stack_1_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_1_block1_sortcut_conv (C (None, 56, 56, 192) 82944 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block1_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_2 (Add) (None, 56, 56, 48) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_1_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block2_sortcut_conv (C (None, 56, 56, 192) 82944 ['add_2[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block2_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_3 (Add) (None, 56, 56, 48) 0 ['add_2[0][0]', Y \n", - " 'stack_1_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block3_sortcut_conv (C (None, 56, 56, 192) 82944 ['add_3[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block3_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_4 (Add) (None, 56, 56, 48) 0 ['add_3[0][0]', Y \n", - " 'stack_1_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block0_sortcut_conv (C (None, 28, 28, 192) 82944 ['add_4[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 192) 768 ['stack_2_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block0_sortcut_swish ( (None, 28, 28, 192) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 64) 12288 ['stack_2_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_2_block1_sortcut_conv (C (None, 28, 28, 256) 147456 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block1_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_5 (Add) (None, 28, 28, 64) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_2_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block2_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_5[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block2_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_6 (Add) (None, 28, 28, 64) 0 ['add_5[0][0]', Y \n", - " 'stack_2_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block3_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_6[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block3_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_7 (Add) (None, 28, 28, 64) 0 ['add_6[0][0]', Y \n", - " 'stack_2_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block0_sortcut_conv (C (None, 28, 28, 256) 16384 ['add_7[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_3_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 256) 2304 ['stack_3_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 256) 1024 ['stack_3_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 256) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 256) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " a) [0]'] \n", - " \n", - " stack_3_block0_se_1_conv (Conv (None, 1, 1, 16) 4112 ['tf.math.reduce_mean[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation (Activation) (None, 1, 1, 16) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block0_se_2_conv (Conv (None, 1, 1, 256) 4352 ['activation[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_1 (Activation) (None, 1, 1, 256) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply (Multiply) (None, 14, 14, 256) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_1[0][0]'] \n", - " \n", - " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 128) 32768 ['multiply[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_3_block1_sortcut_conv (C (None, 14, 14, 512) 65536 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block1_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block1_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_1[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_2 (Activation) (None, 1, 1, 32) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block1_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_3 (Activation) (None, 1, 1, 512) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_1 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_3[0][0]'] \n", - " \n", - " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_1[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_8 (Add) (None, 14, 14, 128) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_3_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block2_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_8[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block2_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block2_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_4 (Activation) (None, 1, 1, 32) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block2_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_5 (Activation) (None, 1, 1, 512) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_2 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_5[0][0]'] \n", - " \n", - " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_2[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_9 (Add) (None, 14, 14, 128) 0 ['add_8[0][0]', Y \n", - " 'stack_3_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block3_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_9[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block3_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block3_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_3[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_6 (Activation) (None, 1, 1, 32) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block3_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_7 (Activation) (None, 1, 1, 512) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_3 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_7[0][0]'] \n", - " \n", - " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_3[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_10 (Add) (None, 14, 14, 128) 0 ['add_9[0][0]', Y \n", - " 'stack_3_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block4_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_10[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block4_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block4_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_8 (Activation) (None, 1, 1, 32) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block4_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_9 (Activation) (None, 1, 1, 512) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_4 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_9[0][0]'] \n", - " \n", - " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_4[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_11 (Add) (None, 14, 14, 128) 0 ['add_10[0][0]', Y \n", - " 'stack_3_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block5_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_11[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block5_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block5_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_5[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_10 (Activation) (None, 1, 1, 32) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block5_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_10[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_11 (Activation) (None, 1, 1, 512) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_5 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_11[0][0]'] \n", - " \n", - " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_5[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_12 (Add) (None, 14, 14, 128) 0 ['add_11[0][0]', Y \n", - " 'stack_3_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block0_sortcut_conv (C (None, 14, 14, 768) 98304 ['add_12[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_4_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block0_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_4_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_4_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_4_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_4_block0_se_1_conv (Conv (None, 1, 1, 32) 24608 ['tf.math.reduce_mean_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_12 (Activation) (None, 1, 1, 32) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block0_se_2_conv (Conv (None, 1, 1, 768) 25344 ['activation_12[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_13[0][0]'] \n", - " \n", - " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 160) 122880 ['multiply_6[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_4_block1_sortcut_conv (C (None, 14, 14, 960) 153600 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block1_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_4_block1_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_7[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_14 (Activation) (None, 1, 1, 40) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block1_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_14[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_15 (Activation) (None, 1, 1, 960) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_7 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_15[0][0]'] \n", - " \n", - " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_7[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_13 (Add) (None, 14, 14, 160) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_4_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block2_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_13[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block2_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_4_block2_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_16 (Activation) (None, 1, 1, 40) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block2_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_16[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_17 (Activation) (None, 1, 1, 960) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_8 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_17[0][0]'] \n", - " \n", - " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_8[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_14 (Add) (None, 14, 14, 160) 0 ['add_13[0][0]', Y \n", - " 'stack_4_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block3_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_14[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block3_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_4_block3_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_9[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_18 (Activation) (None, 1, 1, 40) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block3_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_18[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_19 (Activation) (None, 1, 1, 960) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_9 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_19[0][0]'] \n", - " \n", - " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_9[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_15 (Add) (None, 14, 14, 160) 0 ['add_14[0][0]', Y \n", - " 'stack_4_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block4_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_15[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block4_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block4_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_10[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_20 (Activation) (None, 1, 1, 40) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block4_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_20[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_21 (Activation) (None, 1, 1, 960) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_10 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_21[0][0]'] \n", - " \n", - " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_10[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_16 (Add) (None, 14, 14, 160) 0 ['add_15[0][0]', Y \n", - " 'stack_4_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block5_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_16[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block5_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block5_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_11[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_22 (Activation) (None, 1, 1, 40) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block5_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_22[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_23 (Activation) (None, 1, 1, 960) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_11 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_23[0][0]'] \n", - " \n", - " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_11[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_17 (Add) (None, 14, 14, 160) 0 ['add_16[0][0]', Y \n", - " 'stack_4_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block6_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_17[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block6_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block6_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_12[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_24 (Activation) (None, 1, 1, 40) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block6_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_24[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_25 (Activation) (None, 1, 1, 960) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_12 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_25[0][0]'] \n", - " \n", - " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_12[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_18 (Add) (None, 14, 14, 160) 0 ['add_17[0][0]', Y \n", - " 'stack_4_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block7_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_18[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block7_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block7_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_13[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_26 (Activation) (None, 1, 1, 40) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block7_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_26[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_27 (Activation) (None, 1, 1, 960) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_13 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_27[0][0]'] \n", - " \n", - " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_13[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_19 (Add) (None, 14, 14, 160) 0 ['add_18[0][0]', Y \n", - " 'stack_4_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block8_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_19[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_4_block8_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block8_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_14[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_28 (Activation) (None, 1, 1, 40) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block8_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_28[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_29 (Activation) (None, 1, 1, 960) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_14 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_29[0][0]'] \n", - " \n", - " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_14[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_20 (Add) (None, 14, 14, 160) 0 ['add_19[0][0]', Y \n", - " 'stack_4_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block0_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_20[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_5_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block0_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_5_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 960) 8640 ['stack_5_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 960) 3840 ['stack_5_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 960) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 960) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block0_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_15[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_30 (Activation) (None, 1, 1, 40) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block0_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_30[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_31 (Activation) (None, 1, 1, 960) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_15 (Multiply) (None, 7, 7, 960) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_31[0][0]'] \n", - " \n", - " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 256) 245760 ['multiply_15[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_5_block1_sortcut_conv (C (None, 7, 7, 1536) 393216 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block1_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_16[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_32 (Activation) (None, 1, 1, 64) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_32[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_33 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_16 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_33[0][0]'] \n", - " \n", - " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_16[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_21 (Add) (None, 7, 7, 256) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_5_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block2_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_21[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block2_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_17 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_35[0][0]'] \n", - " \n", - " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_17[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_22 (Add) (None, 7, 7, 256) 0 ['add_21[0][0]', Y \n", - " 'stack_5_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block3_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_22[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block3_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_18 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_37[0][0]'] \n", - " \n", - " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_18[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_23 (Add) (None, 7, 7, 256) 0 ['add_22[0][0]', Y \n", - " 'stack_5_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block4_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_23[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block4_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_19 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_39[0][0]'] \n", - " \n", - " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_19[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_24 (Add) (None, 7, 7, 256) 0 ['add_23[0][0]', Y \n", - " 'stack_5_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block5_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_24[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block5_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_20 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_41[0][0]'] \n", - " \n", - " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_25 (Add) (None, 7, 7, 256) 0 ['add_24[0][0]', Y \n", - " 'stack_5_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block6_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_25[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block6_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_21 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_43[0][0]'] \n", - " \n", - " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_21[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_26 (Add) (None, 7, 7, 256) 0 ['add_25[0][0]', Y \n", - " 'stack_5_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block7_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_26[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block7_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_22 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_45[0][0]'] \n", - " \n", - " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_27 (Add) (None, 7, 7, 256) 0 ['add_26[0][0]', Y \n", - " 'stack_5_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block8_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_27[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block8_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_23 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_47[0][0]'] \n", - " \n", - " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_23[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_28 (Add) (None, 7, 7, 256) 0 ['add_27[0][0]', Y \n", - " 'stack_5_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block9_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_28[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block9_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_24 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_49[0][0]'] \n", - " \n", - " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_29 (Add) (None, 7, 7, 256) 0 ['add_28[0][0]', Y \n", - " 'stack_5_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block10_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_29[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block10_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block10_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_5_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_25 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_51[0][0]'] \n", - " \n", - " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_25[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_30 (Add) (None, 7, 7, 256) 0 ['add_29[0][0]', Y \n", - " 'stack_5_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block11_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_30[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block11_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block11_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_5_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_26 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_53[0][0]'] \n", - " \n", - " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_26[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_31 (Add) (None, 7, 7, 256) 0 ['add_30[0][0]', Y \n", - " 'stack_5_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block12_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_31[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block12_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block12_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_5_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_27 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_55[0][0]'] \n", - " \n", - " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_27[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_32 (Add) (None, 7, 7, 256) 0 ['add_31[0][0]', Y \n", - " 'stack_5_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block13_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_32[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block13_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block13_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_5_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_28 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_57[0][0]'] \n", - " \n", - " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_28[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_33 (Add) (None, 7, 7, 256) 0 ['add_32[0][0]', Y \n", - " 'stack_5_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block14_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_33[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block14_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block14_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_5_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_29 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_59[0][0]'] \n", - " \n", - " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_29[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_34 (Add) (None, 7, 7, 256) 0 ['add_33[0][0]', Y \n", - " 'stack_5_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " post_conv (Conv2D) (None, 7, 7, 1280) 327680 ['add_34[0][0]'] Y \n", - " \n", - " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", - " \n", - " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", - " \n", - " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", - " 2D) \n", - " \n", - " dropout_2 (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", - " \n", - " predictions (Dense) (None, 2) 2562 ['dropout_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 20,333,922\n", - "Trainable params: 20,180,050\n", - "Non-trainable params: 153,872\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from keras_efficientnet_v2 import EfficientNetV2S\n", - "\n", - "EfficientNet_M = EfficientNetV2S(input_shape=(img_res[0], img_res[1], img_res[2]), num_classes=2, dropout=0.5)\n", - "# define new model\n", - "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "freeze_layers = 0\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.applications import ConvNeXtXLarge\n", - "from keras.layers import Lambda\n", - "#FUNC\n", - "def Eff_B7_NS():\n", - " # Add a Lambda layer at the beginning to scale the input\n", - " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", - " x = Lambda(lambda image: image * 255)(input)\n", - " \n", - " base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x)\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model)\n", - " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) \n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "model = Eff_B7_NS()\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LR FINDER" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "#CONF/Other\n", - "LRF_OPT = SGD(momentum=0.9)\n", - "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", - "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", - "# Instantiate LrFinder\n", - "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", - "\n", - "# Start range_test\n", - "lr_find.range_test(LRF_dataset)\n", - "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model vis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dot_img_file = 'model_1.png'\n", - "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the full model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 N \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] N \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] N \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] N \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] N \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] N \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] N \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 63,786,960\n", - "Non-trainable params: 1,954,626\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "import efficientnet.tfkeras\n", - "# Configuration\n", - "PRMC = False\n", - "freeze_from_opposite = False\n", - "Extra_EXT = '_T'\n", - "freeze_layers = 0 \n", - "randomly_frozen_layers = 0 \n", - "freeze_last_seven = True \n", - "# CEC_opt = Adagrad()\n", - "# CEC_opt = Yogi()\n", - "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", - "# CEC_opt = Adam()\n", - "# Main\n", - "try:\n", - " if SAVE_TYPE == 'TF':\n", - " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", - " else:\n", - " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", - "except (ImportError, IOError) as e:\n", - " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", - "else:\n", - " print('\\033[92mLoading model done.')\n", - " if not PRMC:\n", - " print('Compiling the AI model...\\033[0m')\n", - " \n", - " for layer in model.layers:\n", - " layer.trainable = True\n", - " \n", - " # Select random layers to freeze\n", - " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", - " \n", - " for i, layer in enumerate(model.layers):\n", - " if i in frozen_layer_indices:\n", - " layer.trainable = False\n", - " else:\n", - " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", - " layer.trainable = False\n", - " elif (not freeze_from_opposite) and i < freeze_layers:\n", - " layer.trainable = False\n", - " else:\n", - " layer.trainable = True\n", - " \n", - " for layer in model.layers[-7:]:\n", - " layer.trainable = not freeze_last_seven\n", - " \n", - " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - " model.summary(show_trainable=True, expand_nested=True)\n", - " print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading model weights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.load_weights('PAI_model_weights.h5')\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reset FC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for layer in model.layers[-7:]:\n", - " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", - " weight_initializer = layer.kernel_initializer\n", - " bias_initializer = layer.bias_initializer\n", - "\n", - " old_weights, old_biases = layer.get_weights()\n", - "\n", - " layer.set_weights([\n", - " weight_initializer(shape=old_weights.shape),\n", - " bias_initializer(shape=len(old_biases))\n", - " ])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rev2 (THE BEST)\n", - "```\n", - "Working: βœ…\n", - "Other:\n", - " + Tensorboard works.\n", - " + Perverts overfitting.\n", - " + Lower memory usage.\n", - " - Slow training.\n", - " + Achieving higher acc.\n", - " - Some models dont work.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the model...\n", - "\u001b[0;33m\n", - "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2023_m12_d26-h05_m19_s58]\u001b[0m\u001b[0;36m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0;33mSetup Verbose END.\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - "128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260\n", - "Epoch 2/6\n", - "128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740\n", - "Epoch 3/6\n", - "128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926\n", - "Epoch 4/6\n", - "128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173\n", - "Epoch 5/6\n", - "128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894\n", - "Epoch 6/6\n", - "128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-006-0.8974.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8974\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m2.4019\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.8974359035491943\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m2.4019267559051514\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m676.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m405.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029\n", - "Epoch 8/12\n", - "128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295\n", - "Epoch 9/12\n", - "128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926\n", - "Epoch 10/12\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702\n", - "Epoch 11/12\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071\n", - "Epoch 12/12\n", - "128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9407.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5406\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.8974359035491943 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9407051205635071\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m2.4019267559051514 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5405705571174622\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423\n", - "Epoch 14/18\n", - "128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247\n", - "Epoch 15/18\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958\n", - "Epoch 16/18\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135\n", - "Epoch 17/18\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247\n", - "Epoch 18/18\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-013-0.9423.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4656\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9407051205635071 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.942307710647583\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5405705571174622 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4656426012516022\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263\n", - "Epoch 20/24\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343\n", - "Epoch 21/24\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199\n", - "Epoch 22/24\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263\n", - "Epoch 23/24\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327\n", - "Epoch 24/24\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-020-0.9343.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4089\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.942307710647583. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.4656426012516022 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.40894174575805664\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054\n", - "Epoch 26/30\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407\n", - "Epoch 27/30\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455\n", - "Epoch 28/30\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439\n", - "Epoch 29/30\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519\n", - "Epoch 30/30\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-029-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2338\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.942307710647583 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9519230723381042\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.40894174575805664 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.23381969332695007\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471\n", - "Epoch 32/36\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439\n", - "Epoch 33/36\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487\n", - "Epoch 34/36\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311\n", - "Epoch 35/36\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391\n", - "Epoch 36/36\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2253\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.23381969332695007 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2253303825855255\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439\n", - "Epoch 38/42\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455\n", - "Epoch 39/42\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215\n", - "Epoch 40/42\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471\n", - "Epoch 41/42\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439\n", - "Epoch 42/42\n", - "128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-042-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2070\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2253303825855255 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.20697814226150513\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503\n", - "Epoch 44/48\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439\n", - "Epoch 45/48\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215\n", - "Epoch 46/48\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375\n", - "Epoch 47/48\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439\n", - "Epoch 48/48\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.20697814226150513. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487\n", - "Epoch 50/54\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535\n", - "Epoch 51/54\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054\n", - "Epoch 52/54\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471\n", - "Epoch 53/54\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519\n", - "Epoch 54/54\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-050-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1662\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9519230723381042 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9535256624221802\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.20697814226150513 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.16622641682624817\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439\n", - "Epoch 56/60\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519\n", - "Epoch 57/60\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343\n", - "Epoch 58/60\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535\n", - "Epoch 59/60\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487\n", - "Epoch 60/60\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-058-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2059\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624221802. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487\n", - "Epoch 62/66\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471\n", - "Epoch 63/66\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487\n", - "Epoch 64/66\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391\n", - "Epoch 65/66\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487\n", - "Epoch 66/66\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2938\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624221802. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535\n", - "Epoch 68/72\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471\n", - "Epoch 69/72\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567\n", - "Epoch 70/72\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567\n", - "Epoch 71/72\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631\n", - "Epoch 72/72\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-071-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2115\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9535256624221802 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9631410241127014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599\n", - "Epoch 74/78\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567\n", - "Epoch 75/78\n", - "128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551\n", - "Epoch 76/78\n", - "128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439\n", - "Epoch 77/78\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647\n", - "Epoch 78/78\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-077-0.9647.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1545\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9631410241127014 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9647436141967773\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.16622641682624817 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1544923484325409\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359\n", - "Epoch 80/84\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535\n", - "Epoch 81/84\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359\n", - "Epoch 82/84\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599\n", - "Epoch 83/84\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567\n", - "Epoch 84/84\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-082-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1780\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487\n", - "Epoch 86/90\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327\n", - "Epoch 87/90\n", - "128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926\n", - "Epoch 88/90\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327\n", - "Epoch 89/90\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535\n", - "Epoch 90/90\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9615.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2233\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599\n", - "Epoch 92/96\n", - "128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990\n", - "Epoch 93/96\n", - "128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583\n", - "Epoch 94/96\n", - "128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519\n", - "Epoch 95/96\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311\n", - "Epoch 96/96\n", - "128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9599.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1648\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m319.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m249.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311\n", - "Epoch 98/102\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135\n", - "Epoch 99/102\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022\n", - "Epoch 100/102\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519\n", - "Epoch 101/102\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471\n", - "Epoch 102/102\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-100-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1764\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423\n", - "Epoch 104/108\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423\n", - "Epoch 105/108\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391\n", - "Epoch 106/108\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535\n", - "Epoch 107/108\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423\n", - "Epoch 108/108\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-106-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2677\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439\n", - "Epoch 110/114\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343\n", - "Epoch 111/114\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439\n", - "Epoch 112/114\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471\n", - "Epoch 113/114\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535\n", - "Epoch 114/114\n", - "128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-113-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2055\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119\n", - "Epoch 116/120\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638\n", - "Epoch 117/120\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439\n", - "Epoch 118/120\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407\n", - "Epoch 119/120\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022\n", - "Epoch 120/120\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-117-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3367\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567\n", - "Epoch 122/126\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487\n", - "Epoch 123/126\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503\n", - "Epoch 124/126\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535\n", - "Epoch 125/126\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583\n", - "Epoch 126/126\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-125-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2373\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942\n", - "Epoch 128/132\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455\n", - "Epoch 129/132\n", - "128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439\n", - "Epoch 130/132\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535\n", - "Epoch 131/132\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455\n", - "Epoch 132/132\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-130-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2035\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423\n", - "Epoch 134/138\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519\n", - "Epoch 135/138\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487\n", - "Epoch 136/138\n", - "128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503\n", - "Epoch 137/138\n", - "128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519\n", - "Epoch 138/138\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-134-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1983\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439\n", - "Epoch 140/144\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567\n", - "Epoch 141/144\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551\n", - "Epoch 142/144\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487\n", - "Epoch 143/144\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535\n", - "Epoch 144/144\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-140-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503\n", - "Epoch 146/150\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375\n", - "Epoch 147/150\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750\n", - "Epoch 148/150\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423\n", - "Epoch 149/150\n", - "128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487\n", - "Epoch 150/150\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2961\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487\n", - "Epoch 152/156\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311\n", - "Epoch 153/156\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455\n", - "Epoch 154/156\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519\n", - "Epoch 155/156\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551\n", - "Epoch 156/156\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2054\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519\n", - "Epoch 158/162\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535\n", - "Epoch 159/162\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974\n", - "Epoch 160/162\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487\n", - "Epoch 161/162\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551\n", - "Epoch 162/162\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2812\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439\n", - "Epoch 164/168\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535\n", - "Epoch 165/168\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471\n", - "Epoch 166/168\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295\n", - "Epoch 167/168\n", - "128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391\n", - "Epoch 168/168\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3527\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247\n", - "Epoch 170/174\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343\n", - "Epoch 171/174\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487\n", - "Epoch 172/174\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135\n", - "Epoch 173/174\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439\n", - "Epoch 174/174\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3280\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567\n", - "Epoch 176/180\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215\n", - "Epoch 177/180\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551\n", - "Epoch 178/180\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519\n", - "Epoch 179/180\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567\n", - "Epoch 180/180\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2430\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231\n", - "Epoch 182/186\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279\n", - "Epoch 183/186\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535\n", - "Epoch 184/186\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375\n", - "Epoch 185/186\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135\n", - "Epoch 186/186\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9022\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5451\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295\n", - "Epoch 188/192\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567\n", - "Epoch 189/192\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471\n", - "Epoch 190/192\n", - "128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455\n", - "Epoch 191/192\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519\n", - "Epoch 192/192\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2548\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m338.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583\n", - "Epoch 194/198\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391\n", - "Epoch 195/198\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663\n", - "Epoch 196/198\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583\n", - "Epoch 197/198\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583\n", - "Epoch 198/198\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-195-0.9663.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9663\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1816\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9647436141967773 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9663461446762085\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m328.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 199/204\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551\n", - "Epoch 200/204\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599\n", - "Epoch 201/204\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647\n", - "Epoch 202/204\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567\n", - "Epoch 203/204\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599\n", - "Epoch 204/204\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2118\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 205/210\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551\n", - "Epoch 206/210\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503\n", - "Epoch 207/210\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391\n", - "Epoch 208/210\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551\n", - "Epoch 209/210\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503\n", - "Epoch 210/210\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-205-0.9551.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1544\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1544923484325409 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.15437141060829163\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m329.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 211/216\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551\n", - "Epoch 212/216\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263\n", - "Epoch 213/216\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750\n", - "Epoch 214/216\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615\n", - "Epoch 215/216\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327\n", - "Epoch 216/216\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3144\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m328.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 217/222\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199\n", - "Epoch 218/222\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391\n", - "Epoch 219/222\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535\n", - "Epoch 220/222\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199\n", - "Epoch 221/222\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247\n", - "Epoch 222/222\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4185\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 223/228\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519\n", - "Epoch 224/228\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119\n", - "Epoch 225/228\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455\n", - "Epoch 226/228\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295\n", - "Epoch 227/228\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599\n", - "Epoch 228/228\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2645\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 229/234\n", - "128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942\n", - "Epoch 230/234\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231\n", - "Epoch 231/234\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471\n", - "Epoch 232/234\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551\n", - "Epoch 233/234\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022\n", - "Epoch 234/234\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3982\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 235/240\n", - "128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183\n", - "Epoch 236/240\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583\n", - "Epoch 237/240\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551\n", - "Epoch 238/240\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439\n", - "Epoch 239/240\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599\n", - "Epoch 240/240\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2006\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 241/246\n", - "128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487\n", - "Epoch 242/246\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535\n", - "Epoch 243/246\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631\n", - "Epoch 244/246\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615\n", - "Epoch 245/246\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599\n", - "Epoch 246/246\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2011\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 247/252\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599\n", - "Epoch 248/252\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535\n", - "Epoch 249/252\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535\n", - "Epoch 250/252\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615\n", - "Epoch 251/252\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583\n", - "Epoch 252/252\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2136\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 253/258\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583\n", - "Epoch 254/258\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631\n", - "Epoch 255/258\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599\n", - "Epoch 256/258\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599\n", - "Epoch 257/258\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599\n", - "Epoch 258/258\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2042\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 259/264\n", - "128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599\n", - "Epoch 260/264\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439\n", - "Epoch 261/264\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343\n", - "Epoch 262/264\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439\n", - "Epoch 263/264\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343\n", - "Epoch 264/264\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3485\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 265/270\n", - "128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535\n", - "Epoch 266/270\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471\n", - "Epoch 267/270\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455\n", - "Epoch 268/270\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599\n", - "Epoch 269/270\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535\n", - "Epoch 270/270\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2324\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 271/276\n", - "128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407\n", - "Epoch 272/276\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199\n", - "Epoch 273/276\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631\n", - "Epoch 274/276\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519\n", - "Epoch 275/276\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487\n", - "Epoch 276/276\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3108\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 277/282\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519\n", - "Epoch 278/282\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215\n", - "Epoch 279/282\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894\n", - "Epoch 280/282\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471\n", - "Epoch 281/282\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407\n", - "Epoch 282/282\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3641\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 283/288\n", - "128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638\n", - "Epoch 284/288\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183\n", - "Epoch 285/288\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327\n", - "Epoch 286/288\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478\n", - "Epoch 287/288\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862\n", - "Epoch 288/288\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8958\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7104\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m330.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 289/294\n", - "128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167\n", - "Epoch 290/294\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247\n", - "Epoch 291/294\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103\n", - "Epoch 292/294\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279\n", - "Epoch 293/294\n", - "128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215\n", - "Epoch 294/294\n", - "128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5959\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m330.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 295/300\n", - "128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151\n", - "Epoch 296/300\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494\n", - "Epoch 297/300\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279\n", - "Epoch 298/300\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327\n", - "Epoch 299/300\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311\n", - "Epoch 300/300\n", - "128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4308\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 301/306\n", - "128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846\n", - "Epoch 302/306\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990\n", - "Epoch 303/306\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311\n", - "Epoch 304/306\n", - "128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151\n", - "Epoch 305/306\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990\n", - "Epoch 306/306\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8974\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7052\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m362.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 307/312\n", - "128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183\n", - "Epoch 308/312\n", - "128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022\n", - "Epoch 309/312\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974\n", - "Epoch 310/312\n", - "128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279\n", - "Epoch 311/312\n", - "128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135\n", - "Epoch 312/312\n", - "128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5680\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 313/318\n", - "128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247\n", - "Epoch 314/318\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766\n", - "Epoch 315/318\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375\n", - "Epoch 316/318\n", - "128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375\n", - "Epoch 317/318\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231\n", - "Epoch 318/318\n", - "128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4632\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 319/324\n", - "128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263\n", - "Epoch 320/324\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439\n", - "Epoch 321/324\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455\n", - "Epoch 322/324\n", - "128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375\n", - "Epoch 323/324\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423\n", - "Epoch 324/324\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5101\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m287.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00908\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 325/330\n", - "128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263\n", - "Epoch 326/330\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455\n", - "Epoch 327/330\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391\n", - "Epoch 328/330\n", - "128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359\n", - "Epoch 329/330\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375\n", - "Epoch 330/330\n", - "128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00902\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 331/336\n", - "128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535\n", - "Epoch 332/336\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519\n", - "Epoch 333/336\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583\n", - "Epoch 334/336\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439\n", - "Epoch 335/336\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311\n", - "Epoch 336/336\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3479\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00896\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 337/342\n", - "128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327\n", - "Epoch 338/342\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167\n", - "Epoch 339/342\n", - "128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247\n", - "Epoch 340/342\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183\n", - "Epoch 341/342\n", - "128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263\n", - "Epoch 342/342\n", - "128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4083\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m285.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0089\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 343/348\n", - "128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343\n", - "Epoch 344/348\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279\n", - "Epoch 345/348\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359\n", - "Epoch 346/348\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343\n", - "Epoch 347/348\n", - "128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375\n", - "Epoch 348/348\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3776\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m268.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00884\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 349/354\n", - "128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439\n", - "Epoch 350/354\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135\n", - "Epoch 351/354\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327\n", - "Epoch 352/354\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599\n", - "Epoch 353/354\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375\n", - "Epoch 354/354\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4066\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00878\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 355/360\n", - "128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455\n", - "Epoch 356/360\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119\n", - "Epoch 357/360\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071\n", - "Epoch 358/360\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022\n", - "Epoch 359/360\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295\n", - "Epoch 360/360\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4142\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00872\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 361/366\n", - "128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311\n", - "Epoch 362/366\n", - "128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471\n", - "Epoch 363/366\n", - "128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135\n", - "Epoch 364/366\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263\n", - "Epoch 365/366\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151\n", - "Epoch 366/366\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7276\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00866\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 367/372\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391\n", - "Epoch 368/372\n", - "128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407\n", - "Epoch 369/372\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551\n", - "Epoch 370/372\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407\n", - "Epoch 371/372\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407\n", - "Epoch 372/372\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2727\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m266.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0086\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 373/378\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279\n", - "Epoch 374/378\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535\n", - "Epoch 375/378\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311\n", - "Epoch 376/378\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343\n", - "Epoch 377/378\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295\n", - "Epoch 378/378\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4211\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m334.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00854\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 379/384\n", - "128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006\n", - "Epoch 380/384\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263\n", - "Epoch 381/384\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183\n", - "Epoch 382/384\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359\n", - "Epoch 383/384\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519\n", - "Epoch 384/384\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3692\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00848\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 385/390\n", - "128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423\n", - "Epoch 386/390\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359\n", - "Epoch 387/390\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679\n", - "Epoch 388/390\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503\n", - "Epoch 389/390\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423\n", - "Epoch 390/390\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-387-0.9679.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1823\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9663461446762085 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9679487347602844\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00842\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 391/396\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647\n", - "Epoch 392/396\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519\n", - "Epoch 393/396\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359\n", - "Epoch 394/396\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423\n", - "Epoch 395/396\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615\n", - "Epoch 396/396\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2462\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00836\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 397/402\n", - "128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583\n", - "Epoch 398/402\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583\n", - "Epoch 399/402\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215\n", - "Epoch 400/402\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647\n", - "Epoch 401/402\n", - "128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599\n", - "Epoch 402/402\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2736\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m348.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m267.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0083\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 403/408\n", - "128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503\n", - "Epoch 404/408\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295\n", - "Epoch 405/408\n", - "128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423\n", - "Epoch 406/408\n", - "128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423\n", - "Epoch 407/408\n", - "128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423\n", - "Epoch 408/408\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2983\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00824\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 409/414\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487\n", - "Epoch 410/414\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343\n", - "Epoch 411/414\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551\n", - "Epoch 412/414\n", - "128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567\n", - "Epoch 413/414\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407\n", - "Epoch 414/414\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3477\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00818\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 415/420\n", - "128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455\n", - "Epoch 416/420\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407\n", - "Epoch 417/420\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455\n", - "Epoch 418/420\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263\n", - "Epoch 419/420\n", - "128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295\n", - "Epoch 420/420\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4541\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00812\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 421/426\n", - "128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615\n", - "Epoch 422/426\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567\n", - "Epoch 423/426\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199\n", - "Epoch 424/426\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631\n", - "Epoch 425/426\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599\n", - "Epoch 426/426\n", - "128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2110\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00806\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 427/432\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567\n", - "Epoch 428/432\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535\n", - "Epoch 429/432\n", - "128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631\n", - "Epoch 430/432\n", - "128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583\n", - "Epoch 431/432\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599\n", - "Epoch 432/432\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-429-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1528\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.15437141060829163 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.15280155837535858\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m265.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.008\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 433/438\n", - "128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375\n", - "Epoch 434/438\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712\n", - "Epoch 435/438\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343\n", - "Epoch 436/438\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567\n", - "Epoch 437/438\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535\n", - "Epoch 438/438\n", - "128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-434-0.9712.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9696\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1676\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9679487347602844 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9695512652397156\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00794\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 439/444\n", - "128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696\n", - "Epoch 440/444\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663\n", - "Epoch 441/444\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615\n", - "Epoch 442/444\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455\n", - "Epoch 443/444\n", - "128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663\n", - "Epoch 444/444\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3061\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00788\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 445/450\n", - "128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647\n", - "Epoch 446/450\n", - "128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647\n", - "Epoch 447/450\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631\n", - "Epoch 448/450\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407\n", - "Epoch 449/450\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551\n", - "Epoch 450/450\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-445-0.9647.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2149\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00782\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 451/456\n", - "128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599\n", - "Epoch 452/456\n", - "128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567\n", - "Epoch 453/456\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551\n", - "Epoch 454/456\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679\n", - "Epoch 455/456\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679\n", - "Epoch 456/456\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-454-0.9679.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1664\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00776\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 457/462\n", - "128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551\n", - "Epoch 458/462\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696\n", - "Epoch 459/462\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663\n", - "Epoch 460/462\n", - "128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583\n", - "Epoch 461/462\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519\n", - "Epoch 462/462\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-458-0.9696.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9696\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1549\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m394.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0077\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 463/468\n", - "128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647\n", - "Epoch 464/468\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631\n", - "Epoch 465/468\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551\n", - "Epoch 466/468\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503\n", - "Epoch 467/468\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679\n", - "Epoch 468/468\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-467-0.9679.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1365\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.15280155837535858 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.13646124303340912\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m398.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00764\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 469/474\n", - "128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535\n", - "Epoch 470/474\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327\n", - "Epoch 471/474\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359\n", - "Epoch 472/474\n", - "128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535\n", - "Epoch 473/474\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599\n", - "Epoch 474/474\n", - "128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2348\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m395.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00758\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 475/480\n", - "128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519\n", - "Epoch 476/480\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423\n", - "Epoch 477/480\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247\n", - "Epoch 478/480\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599\n", - "Epoch 479/480\n", - "128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647\n", - "Epoch 480/480\n", - "128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2441\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m399.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00752\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 481/486\n", - "128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551\n", - "Epoch 482/486\n", - "128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503\n", - "Epoch 483/486\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423\n", - "Epoch 484/486\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311\n", - "Epoch 485/486\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375\n", - "Epoch 486/486\n", - "128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m360.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00746\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 487/492\n", - "128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343\n", - "Epoch 488/492\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375\n", - "Epoch 489/492\n", - "128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071\n", - "Epoch 490/492\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135\n", - "Epoch 491/492\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503\n", - "Epoch 492/492\n", - "128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2877\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m338.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0074\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 493/498\n", - "128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503\n", - "Epoch 494/498\n", - "128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359\n", - "Epoch 495/498\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551\n", - "Epoch 496/498\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343\n", - "Epoch 497/498\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391\n", - "Epoch 498/498\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3008\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00734\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 499/504\n", - "128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247\n", - "Epoch 500/504\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103\n", - "Epoch 501/504\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263\n", - "Epoch 502/504\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343\n", - "Epoch 503/504\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359\n", - "Epoch 504/504\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3369\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m401.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00728\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 505/510\n", - "128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343\n", - "Epoch 506/510\n", - "128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487\n", - "Epoch 507/510\n", - "128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535\n", - "Epoch 508/510\n", - "128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631\n", - "Epoch 509/510\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519\n", - "Epoch 510/510\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2355\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m378.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00722\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 511/516\n", - "128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455\n", - "Epoch 512/516\n", - "128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567\n", - "Epoch 513/516\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279\n", - "Epoch 514/516\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423\n", - "Epoch 515/516\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567\n", - "Epoch 516/516\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2163\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m348.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00716\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 517/522\n", - "128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327\n", - "Epoch 518/522\n", - "128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375\n", - "Epoch 519/522\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327\n", - "Epoch 520/522\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215\n", - "Epoch 521/522\n", - "128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535\n", - "Epoch 522/522\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m265.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0071\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 523/528\n", - "128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471\n", - "Epoch 524/528\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503\n", - "Epoch 525/528\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471\n", - "Epoch 526/528\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439\n", - "Epoch 527/528\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455\n", - "Epoch 528/528\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3755\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00704\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 529/534\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487\n", - "Epoch 530/534\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615\n", - "Epoch 531/534\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599\n", - "Epoch 532/534\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631\n", - "Epoch 533/534\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615\n", - "Epoch 534/534\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1451\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [89] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m90\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 534)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00698\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 535/540\n", - "128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615\n", - "Epoch 536/540\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551\n", - "Epoch 537/540\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471\n", - "Epoch 538/540\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519\n", - "Epoch 539/540\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567\n", - "Epoch 540/540\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2261\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m376.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [90] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m91\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 540)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00692\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 541/546\n", - "128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263\n", - "Epoch 542/546\n", - "128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487\n", - "Epoch 543/546\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439\n", - "Epoch 544/546\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423\n", - "Epoch 545/546\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471\n", - "Epoch 546/546\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2810\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [91] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m92\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 546)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00686\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 547/552\n", - "128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439\n", - "Epoch 548/552\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471\n", - "Epoch 549/552\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215\n", - "Epoch 550/552\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391\n", - "Epoch 551/552\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503\n", - "Epoch 552/552\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2734\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [92] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m93\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 552)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0068\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 553/558\n", - "128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407\n", - "Epoch 554/558\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103\n", - "Epoch 555/558\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343\n", - "Epoch 556/558\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327\n", - "Epoch 557/558\n", - "128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295\n", - "Epoch 558/558\n", - "128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4314\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [93] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m94\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 558)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00674\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 559/564\n", - "128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231\n", - "Epoch 560/564\n", - "128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519\n", - "Epoch 561/564\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455\n", - "Epoch 562/564\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503\n", - "Epoch 563/564\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359\n", - "Epoch 564/564\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2974\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [94] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m95\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 564)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00668\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 565/570\n", - "128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455\n", - "Epoch 566/570\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279\n", - "Epoch 567/570\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183\n", - "Epoch 568/570\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503\n", - "Epoch 569/570\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391\n", - "Epoch 570/570\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4469\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [95] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m96\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 570)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00662\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 571/576\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247\n", - "Epoch 572/576\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958\n", - "Epoch 573/576\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455\n", - "Epoch 574/576\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343\n", - "Epoch 575/576\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391\n", - "Epoch 576/576\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3027\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m360.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [96] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m97\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 576)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00656\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 577/582\n", - "128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295\n", - "Epoch 578/582\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375\n", - "Epoch 579/582\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535\n", - "Epoch 580/582\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487\n", - "Epoch 581/582\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359\n", - "Epoch 582/582\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3618\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [97] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m98\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 582)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0065\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 583/588\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295\n", - "Epoch 584/588\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423\n", - "Epoch 585/588\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455\n", - "Epoch 586/588\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535\n", - "Epoch 587/588\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503\n", - "Epoch 588/588\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3557\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [98] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m99\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 588)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00644\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 589/594\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423\n", - "Epoch 590/594\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423\n", - "Epoch 591/594\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375\n", - "Epoch 592/594\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455\n", - "Epoch 593/594\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471\n", - "Epoch 594/594\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3592\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [99] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m100\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 594)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00638\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 595/600\n", - "128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407\n", - "Epoch 596/600\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359\n", - "Epoch 597/600\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311\n", - "Epoch 598/600\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439\n", - "Epoch 599/600\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455\n", - "Epoch 600/600\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3042\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [100] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m101\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 600)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00632\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 601/606\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503\n", - "Epoch 602/606\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311\n", - "Epoch 603/606\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487\n", - "Epoch 604/606\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471\n", - "Epoch 605/606\n", - "128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583\n", - "Epoch 606/606\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1757\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [101] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m102\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 606)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00626\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 607/612\n", - "128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471\n", - "Epoch 608/612\n", - "128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423\n", - "Epoch 609/612\n", - "128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247\n", - "Epoch 610/612\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103\n", - "Epoch 611/612\n", - "128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215\n", - "Epoch 612/612\n", - "128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4659\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [102] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m103\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 612)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0062\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 613/618\n", - "128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231\n", - "Epoch 614/618\n", - "128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519\n", - "Epoch 615/618\n", - "128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327\n", - "Epoch 616/618\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503\n", - "Epoch 617/618\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407\n", - "Epoch 618/618\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3278\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [103] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m104\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 618)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00614\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 619/624\n", - "128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407\n", - "Epoch 620/624\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087\n", - "Epoch 621/624\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926\n", - "Epoch 622/624\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327\n", - "Epoch 623/624\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359\n", - "Epoch 624/624\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4053\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [104] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m105\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 624)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00608\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 625/630\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551\n", - "Epoch 626/630\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471\n", - "Epoch 627/630\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423\n", - "Epoch 628/630\n", - "128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567\n", - "Epoch 629/630\n", - "128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615\n", - "Epoch 630/630\n", - "128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2148\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [105] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m106\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 630)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00602\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 631/636\n", - "128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631\n", - "Epoch 632/636\n", - "128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679\n", - "Epoch 633/636\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519\n", - "Epoch 634/636\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663\n", - "Epoch 635/636\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583\n", - "Epoch 636/636\n", - "128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-632-0.9679.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1188\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.13646124303340912 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.11880630999803543\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [106] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m107\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 636)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00596\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 637/642\n", - "128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647\n", - "Epoch 638/642\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439\n", - "Epoch 639/642\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487\n", - "Epoch 640/642\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535\n", - "Epoch 641/642\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439\n", - "Epoch 642/642\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2388\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [107] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m108\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 642)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0059\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 643/648\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583\n", - "Epoch 644/648\n", - "128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455\n", - "Epoch 645/648\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391\n", - "Epoch 646/648\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423\n", - "Epoch 647/648\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423\n", - "Epoch 648/648\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2612\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m351.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [108] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m109\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 648)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00584\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 649/654\n", - "128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439\n", - "Epoch 650/654\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407\n", - "Epoch 651/654\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359\n", - "Epoch 652/654\n", - "128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423\n", - "Epoch 653/654\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359\n", - "Epoch 654/654\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3569\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [109] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m110\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 654)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00578\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 655/660\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471\n", - "Epoch 656/660\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295\n", - "Epoch 657/660\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327\n", - "Epoch 658/660\n", - "128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503\n", - "Epoch 659/660\n", - "128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423\n", - "Epoch 660/660\n", - "128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3959\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [110] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m111\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 660)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00572\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 661/666\n", - "128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471\n", - "Epoch 662/666\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439\n", - "Epoch 663/666\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679\n", - "Epoch 664/666\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583\n", - "Epoch 665/666\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535\n", - "Epoch 666/666\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2614\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m287.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [111] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m112\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 666)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00566\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 667/672\n", - "128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135\n", - "Epoch 668/672\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471\n", - "Epoch 669/672\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471\n", - "Epoch 670/672\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519\n", - "Epoch 671/672\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503\n", - "Epoch 672/672\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2773\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [112] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m113\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 672)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0056\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 673/678\n", - "128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487\n", - "Epoch 674/678\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439\n", - "Epoch 675/678\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471\n", - "Epoch 676/678\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455\n", - "Epoch 677/678\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423\n", - "Epoch 678/678\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3138\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m405.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [113] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m114\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 678)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00554\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 679/684\n", - "128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279\n", - "Epoch 680/684\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375\n", - "Epoch 681/684\n", - "128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647\n", - "Epoch 682/684\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423\n", - "Epoch 683/684\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615\n", - "Epoch 684/684\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2130\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m412.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [114] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m115\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 684)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00548\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 685/690\n", - "128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519\n", - "Epoch 686/690\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215\n", - "Epoch 687/690\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343\n", - "Epoch 688/690\n", - "128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359\n", - "Epoch 689/690\n", - "128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359\n", - "Epoch 690/690\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4240\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [115] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m116\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 690)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00542\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 691/696\n", - "128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263\n", - "Epoch 692/696\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375\n", - "Epoch 693/696\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375\n", - "Epoch 694/696\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359\n", - "Epoch 695/696\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375\n", - "Epoch 696/696\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3796\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [116] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m117\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 696)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00536\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 697/702\n", - "128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375\n", - "Epoch 698/702\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375\n", - "Epoch 699/702\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487\n", - "Epoch 700/702\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519\n", - "Epoch 701/702\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295\n", - "Epoch 702/702\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3449\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [117] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m118\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 702)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0053\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 703/708\n", - "128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503\n", - "Epoch 704/708\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615\n", - "Epoch 705/708\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487\n", - "Epoch 706/708\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503\n", - "Epoch 707/708\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519\n", - "Epoch 708/708\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [118] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m119\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 708)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00524\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 709/714\n", - "128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455\n", - "Epoch 710/714\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295\n", - "Epoch 711/714\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439\n", - "Epoch 712/714\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471\n", - "Epoch 713/714\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375\n", - "Epoch 714/714\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3194\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [119] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m120\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 714)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00518\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 715/720\n", - "128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327\n", - "Epoch 716/720\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423\n", - "Epoch 717/720\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391\n", - "Epoch 718/720\n", - "128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439\n", - "Epoch 719/720\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343\n", - "Epoch 720/720\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5143\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [120] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m121\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 720)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00512\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 721/726\n", - "128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391\n", - "Epoch 722/726\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359\n", - "Epoch 723/726\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199\n", - "Epoch 724/726\n", - "128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407\n", - "Epoch 725/726\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311\n", - "Epoch 726/726\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4177\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m414.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [121] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m122\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 726)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00506\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 727/732\n", - "128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359\n", - "Epoch 728/732\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295\n", - "Epoch 729/732\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327\n", - "Epoch 730/732\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311\n", - "Epoch 731/732\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279\n", - "Epoch 732/732\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4436\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [122] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m123\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 732)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.005\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 733/738\n", - "128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455\n", - "Epoch 734/738\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343\n", - "Epoch 735/738\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407\n", - "Epoch 736/738\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407\n", - "Epoch 737/738\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279\n", - "Epoch 738/738\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4303\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m412.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [123] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m124\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 738)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00494\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 739/744\n", - "128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231\n", - "Epoch 740/744\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974\n", - "Epoch 741/744\n", - "128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327\n", - "Epoch 742/744\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439\n", - "Epoch 743/744\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327\n", - "Epoch 744/744\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4234\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [124] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m125\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 744)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00488\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 745/750\n", - "128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455\n", - "Epoch 746/750\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327\n", - "Epoch 747/750\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439\n", - "Epoch 748/750\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359\n", - "Epoch 749/750\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343\n", - "Epoch 750/750\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3677\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [125] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m126\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 750)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00482\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 751/756\n", - "128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375\n", - "Epoch 752/756\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455\n", - "Epoch 753/756\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359\n", - "Epoch 754/756\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391\n", - "Epoch 755/756\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423\n", - "Epoch 756/756\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3409\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [126] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m127\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 756)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00476\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 757/762\n", - "128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375\n", - "Epoch 758/762\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599\n", - "Epoch 759/762\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391\n", - "Epoch 760/762\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391\n", - "Epoch 761/762\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359\n", - "Epoch 762/762\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3632\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [127] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m128\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 762)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0047\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 763/768\n", - "128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423\n", - "Epoch 764/768\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343\n", - "Epoch 765/768\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375\n", - "Epoch 766/768\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503\n", - "Epoch 767/768\n", - "128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391\n", - "Epoch 768/768\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3065\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m425.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [128] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m129\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 768)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00464\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 769/774\n", - "128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343\n", - "Epoch 770/774\n", - "128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439\n", - "Epoch 771/774\n", - "128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487\n", - "Epoch 772/774\n", - "128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503\n", - "Epoch 773/774\n", - "128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519\n", - "Epoch 774/774\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2221\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [129] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m130\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 774)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00458\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 775/780\n", - "128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487\n", - "Epoch 776/780\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535\n", - "Epoch 777/780\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327\n", - "Epoch 778/780\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455\n", - "Epoch 779/780\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423\n", - "Epoch 780/780\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3315\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [130] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m131\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 780)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00452\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 781/786\n", - "128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279\n", - "Epoch 782/786\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247\n", - "Epoch 783/786\n", - "128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391\n", - "Epoch 784/786\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359\n", - "Epoch 785/786\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231\n", - "Epoch 786/786\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5019\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m424.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m300.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [131] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m132\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 786)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00446\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 787/792\n", - "128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247\n", - "Epoch 788/792\n", - "128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263\n", - "Epoch 789/792\n", - "128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990\n", - "Epoch 790/792\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215\n", - "Epoch 791/792\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231\n", - "Epoch 792/792\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5337\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m299.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [132] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m133\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 792)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0044\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 793/798\n", - "128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231\n", - "Epoch 794/798\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375\n", - "Epoch 795/798\n", - "128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487\n", - "Epoch 796/798\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311\n", - "Epoch 797/798\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311\n", - "Epoch 798/798\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5005\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [133] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m134\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 798)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00434\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 799/804\n", - "128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151\n", - "Epoch 800/804\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071\n", - "Epoch 801/804\n", - "128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295\n", - "Epoch 802/804\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231\n", - "Epoch 803/804\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151\n", - "Epoch 804/804\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9151\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5460\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [134] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m135\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 804)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00428\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 805/810\n", - "128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103\n", - "Epoch 806/810\n", - "128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167\n", - "Epoch 807/810\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087\n", - "Epoch 808/810\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103\n", - "Epoch 809/810\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103\n", - "Epoch 810/810\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5751\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m414.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [135] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m136\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 810)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00422\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 811/816\n", - "128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327\n", - "Epoch 812/816\n", - "128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359\n", - "Epoch 813/816\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375\n", - "Epoch 814/816\n", - "128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423\n", - "Epoch 815/816\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407\n", - "Epoch 816/816\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3819\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m300.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [136] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m137\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 816)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00416\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 817/822\n", - "128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375\n", - "Epoch 818/822\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295\n", - "Epoch 819/822\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375\n", - "Epoch 820/822\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391\n", - "Epoch 821/822\n", - "128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407\n", - "Epoch 822/822\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4434\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [137] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m138\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 822)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0041\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 823/828\n", - "128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359\n", - "Epoch 824/828\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263\n", - "Epoch 825/828\n", - "128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407\n", - "Epoch 826/828\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167\n", - "Epoch 827/828\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295\n", - "Epoch 828/828\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [138] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m139\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 828)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00404\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 829/834\n", - "128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263\n", - "Epoch 830/834\n", - "128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375\n", - "Epoch 831/834\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231\n", - "Epoch 832/834\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375\n", - "Epoch 833/834\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359\n", - "Epoch 834/834\n", - "128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5237\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m418.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [139] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m140\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 834)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00398\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 835/840\n", - "128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375\n", - "Epoch 836/840\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151\n", - "Epoch 837/840\n", - "128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183\n", - "Epoch 838/840\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247\n", - "Epoch 839/840\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199\n", - "Epoch 840/840\n", - "128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5710\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [140] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m141\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 840)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00392\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 841/846\n", - "128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183\n", - "Epoch 842/846\n", - "128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231\n", - "Epoch 843/846\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231\n", - "Epoch 844/846\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231\n", - "Epoch 845/846\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199\n", - "Epoch 846/846\n", - "128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5128\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m423.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [141] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m142\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 846)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00386\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 847/852\n", - "128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215\n", - "Epoch 848/852\n", - "128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247\n", - "Epoch 849/852\n", - "128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359\n", - "Epoch 850/852\n", - "128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311\n", - "Epoch 851/852\n", - "128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295\n", - "Epoch 852/852\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5103\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m301.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [142] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m143\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 852)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0038\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 853/858\n", - "128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135\n", - "Epoch 854/858\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263\n", - "Epoch 855/858\n", - "128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151\n", - "Epoch 856/858\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183\n", - "Epoch 857/858\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038\n", - "Epoch 858/858\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9087\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7814\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [143] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m144\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 858)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00374\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 859/864\n", - "128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359\n", - "Epoch 860/864\n", - "128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391\n", - "Epoch 861/864\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343\n", - "Epoch 862/864\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423\n", - "Epoch 863/864\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391\n", - "Epoch 864/864\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3838\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [144] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m145\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 864)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00368\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 865/870\n", - "128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439\n", - "Epoch 866/870\n", - "128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487\n", - "Epoch 867/870\n", - "128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359\n", - "Epoch 868/870\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439\n", - "Epoch 869/870\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439\n", - "Epoch 870/870\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3789\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [145] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m146\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 870)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00362\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 871/876\n", - "128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103\n", - "Epoch 872/876\n", - "128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135\n", - "Epoch 873/876\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279\n", - "Epoch 874/876\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022\n", - "Epoch 875/876\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103\n", - "Epoch 876/876\n", - "128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5834\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [146] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m147\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 876)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00356\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 877/882\n", - "128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215\n", - "Epoch 878/882\n", - "128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439\n", - "Epoch 879/882\n", - "128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455\n", - "Epoch 880/882\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231\n", - "Epoch 881/882\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279\n", - "Epoch 882/882\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3896\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [147] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m148\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 882)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0035\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 883/888\n", - "128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167\n", - "Epoch 884/888\n", - "128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183\n", - "Epoch 885/888\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071\n", - "Epoch 886/888\n", - "128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183\n", - "Epoch 887/888\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199\n", - "Epoch 888/888\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5339\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [148] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m149\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 888)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00344\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 889/894\n", - "128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135\n", - "Epoch 890/894\n", - "128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231\n", - "Epoch 891/894\n", - "128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263\n", - "Epoch 892/894\n", - "128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231\n", - "Epoch 893/894\n", - "128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231\n", - "Epoch 894/894\n", - "128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4329\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [149] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m150\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 894)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00338\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 895/900\n", - "128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231\n", - "Epoch 896/900\n", - "128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103\n", - "Epoch 897/900\n", - "128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006\n", - "Epoch 898/900\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071\n", - "Epoch 899/900\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054\n", - "Epoch 900/900\n", - "128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9087\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.6034\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m292.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [150] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m151\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 900)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00332\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 901/906\n", - "128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151\n", - "Epoch 902/906\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151\n", - "Epoch 903/906\n", - "128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407\n", - "Epoch 904/906\n", - "128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519\n", - "Epoch 905/906\n", - "128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455\n", - "Epoch 906/906\n", - "128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3160\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [151] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m152\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 906)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00326\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 907/912\n", - "128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439\n", - "Epoch 908/912\n", - "128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407\n", - "Epoch 909/912\n", - "128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407\n", - "Epoch 910/912\n", - "128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311\n", - "Epoch 911/912\n", - " 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936\n", - "KeyboardInterrupt.\n", - "Training done.\n", - "\n" - ] - } - ], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "# CONF <-------------------------------------------------------------------------->\n", - "# Hyperparameters for training the model:\n", - "max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n", - "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", - "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", - "PL_epoch = 24 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n", - "subset_size = 2048 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 4096.\n", - "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", - "RES_Train = False # RES_Train: Resume training if True.\n", - "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", - "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n", - "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n", - "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n", - "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n", - "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n", - "Debug_OUTPUT_DPS_freq = 32 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n", - "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n", - "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n", - "USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True.\n", - "AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n", - "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n", - "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n", - "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n", - "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n", - "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n", - "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n", - "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n", - "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n", - "Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️).\n", - "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n", - "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n", - "Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n", - "BEST_RSN = 'PAI_model_T' # Best model save name prefix.\n", - "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n", - "IMAGE_GEN_PATH = 'Data\\\\image_SUB_generator.pkl'\n", - "# CONF END <---------------------------------------------------------------------->\n", - "#Prep\n", - "if RES_Train:\n", - " MAX_LR = RES_LR\n", - " PL_epoch = 1\n", - "#VAR\n", - "Total_SUB_epoch_C = 0 # TO FIX TensorBoard\n", - "CU_LR = MAX_LR\n", - "all_histories = []\n", - "chosen_indices = []\n", - "subset_sizes = []\n", - "best_acc = 0\n", - "best_loss = float('inf')\n", - "#Funcs\n", - "def normalize_TO_RANGE(arr, min_val, max_val):\n", - " arr = arr.astype('float32')\n", - " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", - " arr = arr * (max_val - min_val) + min_val\n", - " return arr\n", - "\n", - "def Z_SCORE_normalize(arr):\n", - " arr = arr.astype('float32')\n", - " mean = np.mean(arr)\n", - " std_dev = np.std(arr)\n", - " arr = (arr - mean) / std_dev\n", - " return arr\n", - "\n", - "def add_image_grain_TRLRev2(image, intensity = 0.01):\n", - " # Generate random noise array\n", - " noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \\\n", - " + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2\n", - "\n", - " # Scale the noise array\n", - " scaled_noise = (noise * intensity).astype(np.float32)\n", - " # Add the noise to the image\n", - " noisy_image = cv2.add(image, scaled_noise)\n", - "\n", - " return noisy_image\n", - "# noise_func_TRLRev2 ([REV1 OLD])\n", - "if not USE_REV2_DP:\n", - " def noise_func_TRLRev2(image): \n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.08, 0.08)\n", - " intensityL1 = random.uniform(-0.05, 0.05)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image.shape[0], block_size_L2):\n", - " for j in range(0, image.shape[1], block_size_L2):\n", - " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image = new_image \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image.shape[0], block_size_L1):\n", - " for j in range(0, image.shape[1], block_size_L1):\n", - " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.07) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", - "# noise_func_TRLRev2 ([REV2 NEW])\n", - "else:\n", - " def noise_func_TRLRev2(image):\n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.07, 0.07)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.07, 0.07)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", - " \n", - " for channel in range(3): # Iterate over each RGB channel\n", - " image_channel = image[:, :, channel]\n", - " new_image_channel = new_image[:, :, channel]\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image_channel.shape[0], block_size_L2):\n", - " for j in range(0, image_channel.shape[1], block_size_L2):\n", - " block = image_channel[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image_channel = new_image_channel \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image_channel.shape[0], block_size_L1):\n", - " for j in range(0, image_channel.shape[1], block_size_L1):\n", - " block = image_channel[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " new_image[:, :, channel] = new_image_channel\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.05) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", - "#CONST\n", - "train_SUB_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.18, \n", - " shear_range=0.18,\n", - " width_shift_range=0.18,\n", - " brightness_range=(0.82, 1.18),\n", - " height_shift_range=0.18,\n", - " channel_shift_range=100,\n", - " featurewise_center=True,\n", - " featurewise_std_normalization=True,\n", - " zca_whitening=False,\n", - " interpolation_order=2,\n", - " fill_mode='nearest',\n", - " preprocessing_function=noise_func_TRLRev2\n", - " )\n", - "class TerminateOnHighTemp(tf.keras.callbacks.Callback):\n", - " def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60):\n", - " super().__init__()\n", - " self.active = active\n", - " self.check_every_n_batches = check_every_n_batches\n", - " self.high_temp = high_temp\n", - " self.low_temp = low_temp\n", - " self.pause_time = pause_time\n", - " self.batch_counter = 0\n", - "\n", - " def on_batch_end(self, batch, logs=None):\n", - " if not self.active:\n", - " return\n", - " self.batch_counter += 1\n", - " if self.batch_counter % self.check_every_n_batches == 0:\n", - " temperature = gpu_control.get_temperature()\n", - " if temperature > self.high_temp:\n", - " print_Color(f'\\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False)\n", - " time.sleep(self.pause_time) \n", - " while gpu_control.get_temperature() > self.low_temp:\n", - " time.sleep(4)\n", - " print_Color('Resuming training...', ['yellow'])\n", - "class ExtendedTensorBoard(TensorBoard):\n", - " def on_epoch_end(self, epoch, logs=None):\n", - " logs = logs or {}\n", - " logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr)\n", - " logs['momentum'] = self.model.optimizer.momentum \n", - " super().on_epoch_end(epoch, logs)\n", - "class DummyCallback(Callback):\n", - " pass\n", - "steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2\n", - "#callbacks>>>\n", - "# EarlyStopping\n", - "early_stopping = EarlyStopping(monitor='val_accuracy',\n", - " patience=EarlyStopping_P,\n", - " verbose=1, restore_best_weights=True,\n", - " mode='max'\n", - " ) if Use_ES_ONSUBT else DummyCallback()\n", - "# ModelCheckpoint \n", - "checkpoint_SUB = ModelCheckpoint(f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', \n", - " monitor=load_SUB_BRW_MODE,\n", - " save_best_only=True, mode=ModelCheckpoint_mode,\n", - " save_weights_only = True\n", - " ) if load_SUB_BRW else DummyCallback()\n", - "checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", - "# TerminateOnHighTemp\n", - "TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M,\n", - " check_every_n_batches=6,\n", - " high_temp=72,\n", - " low_temp=58,\n", - " pause_time=60)\n", - "# TensorBoard\n", - "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - "if Use_extended_tensorboard:\n", - " tensorboard_callback = ExtendedTensorBoard(\n", - " log_dir=log_dir,\n", - " write_images=False, # Uses a lot of memory\n", - " histogram_freq=1,\n", - " update_freq='epoch',\n", - " write_grads=True,\n", - " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", - " )\n", - "else:\n", - " tensorboard_callback = TensorBoard(\n", - " log_dir=log_dir,\n", - " write_images=False, # Uses a lot of memory\n", - " histogram_freq=1,\n", - " update_freq='epoch',\n", - " write_grads=True,\n", - " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", - " )\n", - "# OneCycleLr\n", - "if OneCycleLr_UFTS: \n", - " learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR,\n", - " steps_per_epoch=steps_per_epoch_train_SUB,\n", - " epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) \n", - "#PRES\n", - "# ...\n", - "#MAIN\n", - "print('Training the model...')\n", - "# INFOp\n", - "print_Color('\\nSetup Verbose:', ['yellow'])\n", - "print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "#warnings\n", - "P_warning('RES_Train is True.') if RES_Train else None\n", - "print_Color('Setup Verbose END.', ['yellow'])\n", - "# MAIN LOOP\n", - "try:\n", - " for epoch in range(1, max_epoch):\n", - " # Start Epoch\n", - " STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning'\n", - " C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT\n", - " if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1\n", - " start_FULL_time = time.time()\n", - " if Auto_clear_cache:\n", - " subprocess.run([\"Cache_clear.cmd\"], shell=True)\n", - " # TSEC: Total-Subset-Epoch-Count\n", - " print_Color(f'\\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True)\n", - " # DP\n", - " if not AdvSubsetC:\n", - " print_Color('Shuffling data...', ['yellow'])\n", - " x_train, y_train = shuffle_data(x_train, y_train)\n", - " print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", - " if AdvSubsetC:\n", - " if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0:\n", - " print_Color('└───Shuffling data...', ['yellow'])\n", - " x_train, y_train = shuffle_data(x_train, y_train)\n", - " chosen_indices = [] # Reset chosen_indices\n", - "\n", - " available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices))\n", - " \n", - " if len(available_indices) < subset_size:\n", - " #DEBUG\n", - " # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.')\n", - " # If there are not enough available indices, choose from the indices that were chosen the longest time ago\n", - " old_indices = chosen_indices[:subset_size - len(available_indices)]\n", - " subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False))\n", - " \n", - " # Update the list of chosen indices and their sizes\n", - " chosen_indices = chosen_indices[len(old_indices):] + subset_indices\n", - " subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices)\n", - " else:\n", - " subset_indices = list(np.random.choice(available_indices, subset_size, replace=False))\n", - " \n", - " # Add the chosen indices to the list of already chosen indices\n", - " chosen_indices += subset_indices\n", - " subset_sizes += [subset_size] * len(subset_indices)\n", - " else:\n", - " subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False)\n", - " # Taking the subset\n", - " x_SUB_train = x_train[subset_indices]\n", - " y_SUB_train = y_train[subset_indices]\n", - " x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train)\n", - " assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}'\n", - " print_Color('Preparing train data...', ['yellow']) \n", - " # if epoch == 1: # OLD\n", - " # print_Color('- ImageDataGenerator fit...', ['yellow']) \n", - " # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", - " # print_Color('- ImageDataGenerator fit done.', ['yellow'])\n", - " if epoch == 1 or ALWAYS_REFIT_IDG == 2:\n", - " if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG:\n", - " print_Color('- Loading fitted ImageDataGenerator...', ['yellow'])\n", - " train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) \n", - " else:\n", - " print_Color('- Fitting ImageDataGenerator...', ['yellow'])\n", - " IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n", - " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", - " pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb'))\n", - " print_Color('- ImageDataGenerator fit done.', ['yellow']) \n", - "\n", - " print_Color('- Augmenting Image Data...', ['yellow']) \n", - " train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255,\n", - " y_SUB_train,\n", - " shuffle=False,\n", - " batch_size=len(x_SUB_train)\n", - " ).next()\n", - " print_Color('- Normalizing Image Data...', ['yellow'])\n", - " x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255)\n", - " # x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 1) / 255\n", - " x_SUB_train = x_SUB_train / 255\n", - " x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1)\n", - " y_SUB_train = train_SUB_augmented_images[1]\n", - " # DEBUG\n", - " if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1):\n", - " SITD = np.random.choice(subset_size, size=400, replace=False)\n", - " S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", - " save_images_to_dir(x_SUB_train[SITD], y_SUB_train[SITD], S_dir)\n", - " # learning_rate_schedule_SUB\n", - " if PL_epoch == 0:\n", - " CU_LR = MIN_LR\n", - " elif epoch >= PL_epoch and CU_LR > MIN_LR:\n", - " if (CU_LR - DEC_LR) < MIN_LR:\n", - " CU_LR = MIN_LR\n", - " else:\n", - " CU_LR -= DEC_LR\n", - " if not OneCycleLr_UFTS: \n", - " learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR,\n", - " steps_per_epoch=steps_per_epoch_train_SUB,\n", - " epochs=C_subset_epoch)\n", - " #FV\n", - " print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + \"~*~*\") if not OneCycleLr_UFTS else \"~*OneCycleLr_UFTS Is ON~*\"}]~*...',\n", - " ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True)\n", - " print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", - " # Train\n", - " print_Color('Training on subset...', ['green'])\n", - " start_SUBO_time = time.time()\n", - " SUB_history = model.fit(x_SUB_train,\n", - " y_SUB_train,\n", - " epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C)\n", - " batch_size=Conf_batch_size_REV2,\n", - " validation_data=(x_test, y_test),\n", - " verbose='auto',\n", - " initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard\n", - " callbacks=[\n", - " learning_rate_schedule_SUB,\n", - " TerminateOnHighTemp_CB,\n", - " checkpoint_SUB,\n", - " early_stopping,\n", - " tensorboard_callback\n", - " ]\n", - " )\n", - " end_SUBO_time = time.time()\n", - " print_Color('Subset training done.', ['green'])\n", - " if load_SUB_BRW_LMODE == 1:\n", - " if max(SUB_history.history['val_accuracy']) > best_acc: \n", - " load_weights = True \n", - " elif min(SUB_history.history['val_loss']) < best_loss:\n", - " load_weights = True \n", - " else:\n", - " load_weights = False \n", - " else: \n", - " load_weights = True \n", - " \n", - " if load_SUB_BRW and load_weights:\n", - " print_Color('Loading the best weights...', ['yellow'])\n", - " # Get the filename of the best weights file\n", - " list_of_files = glob.glob('cache\\\\*.h5') \n", - " try:\n", - " best_weights_filename = max(list_of_files, key=os.path.getctime)\n", - " print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow'])\n", - " model.load_weights(best_weights_filename)\n", - " except Exception as Err:\n", - " print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red'])\n", - " elif load_SUB_BRW and (not load_weights):\n", - " # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]',\n", - " # ['yellow']) # OLD\n", - " print_Color_V2(f'Not loading weights[BSR:acc{{{95.675647:.4f}}}, loss{{{0.0111:.4f}}}|BTR:acc{{{97.56456:.4f}}}, loss{{{0.002:.4f}}}]')\n", - " all_histories.append(SUB_history.history)\n", - " checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " tf.keras.backend.clear_session() \n", - " # Evaluate the model on the test data\n", - " evaluation = model.evaluate(x_test, y_test, verbose=0)\n", - " \n", - " # Extract the loss and accuracy from the evaluation results\n", - " loss = evaluation[0]\n", - " acc = evaluation[1]\n", - " print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True)\n", - " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", - " # If the accuracy is higher than the best_acc\n", - " if acc > best_acc:\n", - " print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.')\n", - " # Update the best_acc\n", - " best_acc = acc\n", - " if SAVE_FULLM:\n", - " # Save the model\n", - " if SAVE_TYPE == 'TF':\n", - " print_Color_V2(f'Saving full model tf format...')\n", - " model.save(BEST_RSN, save_format='tf')\n", - " else:\n", - " print_Color_V2(f'Saving full model H5 format...')\n", - " model.save(f'{BEST_RSN}.h5')\n", - " model.save_weights('PAI_model_weights.h5')\n", - " else:\n", - " print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.')\n", - " \n", - " # If the loss is higher than the best_loss\n", - " if loss < best_loss:\n", - " print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.')\n", - " \n", - " # Update the best_acc\n", - " best_loss = loss\n", - " \n", - " if SAVE_FULLM:\n", - " # Save the model\n", - " if SAVE_TYPE == 'TF':\n", - " print_Color_V2(f'Saving full model tf format...')\n", - " model.save(BEST_RSN + '_BL', save_format='tf')\n", - " else:\n", - " print_Color_V2(f'Saving full model H5 format...')\n", - " model.save(f'{BEST_RSN}_BL.h5')\n", - " model.save_weights('PAI_model_weights_BL.h5')\n", - " else:\n", - " print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') \n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " tf.keras.backend.clear_session() \n", - " # Epoch end\n", - " end_time = time.time()\n", - " epoch_time = end_time - start_FULL_time\n", - " print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec')\n", - " epoch_SUB_time = end_SUBO_time - start_SUBO_time\n", - " print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec')\n", - " epoch_OTHERO_time = epoch_time - epoch_SUB_time\n", - " print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec')\n", - " print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan'])\n", - " Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard\n", - "except KeyboardInterrupt:\n", - " print('\\nKeyboardInterrupt.')\n", - "# End\n", - "try:\n", - " history = {}\n", - " for key in all_histories[0].keys():\n", - " # For each metric, concatenate the values from all histories\n", - " history[key] = np.concatenate([h[key] for h in all_histories])\n", - "except Exception as Err:\n", - " print(f'Failed to make model `history` var.\\nERROR: {Err}')\n", - " \n", - "print('Training done.\\n')\n", - "# del vars\n", - "try:\n", - " del train_SUB_datagen\n", - " del train_SUB_augmented_images\n", - "except NameError:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rev1 (⚠️deprecated⚠️)\n", - "```\n", - "Working: βœ…\n", - "Other:\n", - " + Tensorboard works.\n", - " - Can cause overfitting.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "#CONF\n", - "Conf_batch_size = 8 \n", - "OneCycleLr_epoch = 20\n", - "Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training)\n", - "#TensorBoard conf\n", - "TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining)\n", - "# Learning rate configuration\n", - "Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr\n", - "MAX_LR = 0.0174\n", - "# First time\n", - "if Learning_rate_conf == 1:\n", - " learning_rate_start = 8e-04\n", - " learning_rate_max = 5e-03\n", - " learning_rate_min = 5e-05\n", - " learning_rate_rampup_epochs = 5\n", - " learning_rate_sustain_epochs = 1\n", - " learning_rate_exp_decay = .3\n", - " #TEMP\n", - " # learning_rate_start = 8e-04\n", - " # learning_rate_max = 1e-02\n", - " # learning_rate_min = 8e-04\n", - " # learning_rate_rampup_epochs = 5\n", - " # learning_rate_sustain_epochs = 3\n", - " # learning_rate_exp_decay = .45\n", - "# 2th time\n", - "if Learning_rate_conf == 2:\n", - " if Learning_rate_conf_SET2C == 1:\n", - " learning_rate_start = 4.10e-06\n", - " learning_rate_max = 4.10e-06\n", - " learning_rate_min = 4.10e-06\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - " \n", - " elif Learning_rate_conf_SET2C == 2:\n", - " learning_rate_start = 4e-07\n", - " learning_rate_max = 4e-07\n", - " learning_rate_min = 4e-07\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - " \n", - " elif Learning_rate_conf_SET2C == 3:\n", - " learning_rate_start = 5e-04\n", - " learning_rate_max = 5e-04\n", - " learning_rate_min = 5e-04\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - "# Function to build learning rate schedule\n", - "if Learning_rate_conf in [1,2]:\n", - " def build_learning_rate_fn(lr_start=learning_rate_start,\n", - " lr_max=learning_rate_max,\n", - " lr_min=learning_rate_min,\n", - " lr_rampup_epochs=learning_rate_rampup_epochs,\n", - " lr_sustain_epochs=learning_rate_sustain_epochs,\n", - " lr_exp_decay=learning_rate_exp_decay): \n", - " lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync\n", - " def learning_rate_fn(epoch):\n", - " if epoch < lr_rampup_epochs:\n", - " lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n", - " elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n", - " lr = lr_max\n", - " else:\n", - " lr = (lr_max - lr_min) *\\\n", - " lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min\n", - " return lr\n", - " return learning_rate_fn\n", - " \n", - "# Calculate steps per epoch\n", - "steps_per_epoch_train = len(x_train) // Conf_batch_size\n", - "\n", - "# Set up callbacks\n", - "class EpochEndMON(tf.keras.callbacks.Callback):\n", - " def on_epoch_end(self, epoch, logs=None):\n", - " optimizer = self.model.optimizer\n", - " if hasattr(optimizer, 'lr'):\n", - " lr = tf.keras.backend.get_value(optimizer.lr)\n", - " print(f'\\nLearning rate for epoch {epoch+1} is {lr}')\n", - " if hasattr(optimizer, 'momentum'):\n", - " momentum = tf.keras.backend.get_value(optimizer.momentum)\n", - " print(f'Momentum for epoch {epoch+1} is {momentum}')\n", - " if logs:\n", - " val_loss = logs.get('val_loss')\n", - " val_acc = logs.get('val_accuracy')\n", - " print(f'Validation loss for epoch {epoch+1} is {val_loss}')\n", - " print(f'Validation accuracy for epoch {epoch+1} is {val_acc}')\n", - "\n", - " print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`')\n", - "\n", - "# Instantiate the callback\n", - "EpochEndMON_callback = EpochEndMON()\n", - "if Learning_rate_conf in [1,2]:\n", - " learning_rate_fn = build_learning_rate_fn()\n", - " learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1)\n", - "else:\n", - " learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch)\n", - "if SAVE_TYPE == 'TF':\n", - " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", - " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", - "else:\n", - " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", - " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", - "early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True)\n", - "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - "TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch'\n", - "tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True)\n", - "\n", - "# Train the model\n", - "print('Log dir:', log_dir)\n", - "#MInfo\n", - "print('Input Shape:', model.input_shape)\n", - "print('Output Shape:', model.output_shape)\n", - "print('Loss Function:', model.loss)\n", - "print('Training the model...\\n')\n", - "history = model.fit(x_train,\n", - " y_train,\n", - " epochs=256,\n", - " batch_size=Conf_batch_size,\n", - " validation_data=(x_test, y_test),\n", - " verbose='auto',\n", - " callbacks=[early_stopping,\n", - " tensorboard_callback,\n", - " learning_rate_schedule,\n", - " checkpoint_BVAC,\n", - " checkpoint_BVL,\n", - " EpochEndMON_callback])\n", - "print('Training done.\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving model weights\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [], - "source": [ - "Extra_EXT = '_T'\n", - "# Save the weights\n", - "print('Saving weights...')\n", - "model.save_weights('PAI_model_weights.h5')\n", - "print('Saving full model...')\n", - "if SAVE_TYPE == 'TF':\n", - " print('Saving full model tf format...')\n", - " model.save(f'PAI_model{Extra_EXT}', save_format='tf')\n", - "else:\n", - " try:\n", - " model.save(f'PAI_model{Extra_EXT}.h5')\n", - " except ValueError:\n", - " print('failed to save in .h5 format!')\n", - " print('Saving full model in tf format...')\n", - " model.save(f'PAI_model{Extra_EXT}', save_format='tf')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Garbage Collection (memory)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse model Training performance" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Save history\n", - "save_list(history, 'history\\\\model_history.pkl.gz', compress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load history\n", - "history = load_list('history\\\\model_history.pkl.gz', compressed=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import seaborn as sns\n", - "\n", - "# Chunk size for 3D plot\n", - "chunk_size = 6 # Change this to your desired chunk size\n", - " \n", - "def convert_history(history):\n", - " if isinstance(history, tf.keras.callbacks.History):\n", - " return history.history\n", - " else:\n", - " return history\n", - " \n", - "def chunked_data(data, chunk_size):\n", - " return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n", - "\n", - "\n", - "try:\n", - " EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' \n", - " history = convert_history(history)\n", - "\n", - " # Calculate deltas\n", - " delta_loss = np.diff(history['loss'])\n", - " delta_accuracy = np.diff(history['accuracy'])\n", - "\n", - " try:\n", - " delta_val_loss = np.diff(history['val_loss'])\n", - " delta_val_accuracy = np.diff(history['val_accuracy'])\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss or val_accuracy for delta calculation.')\n", - "\n", - " plt.figure(figsize=(16, 10))\n", - " # Loss\n", - " plt.subplot(2, 2, 1)\n", - " plt.plot(history['loss'], label='loss')\n", - " try:\n", - " plt.plot(history['val_loss'], label='val_loss', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss.')\n", - " plt.title('Model Loss')\n", - " plt.ylabel('Loss')\n", - " plt.xlabel(EPM)\n", - " plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", - " plt.grid(True)\n", - " \n", - " # Density plot for loss\n", - " plt.subplot(2, 2, 2)\n", - " plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100)\n", - " try:\n", - " plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100)\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss (density plot).')\n", - " plt.title('Density Plot for Loss')\n", - " plt.xlabel('Loss')\n", - " plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", - " plt.grid(True)\n", - " \n", - " \n", - " # Accuracy\n", - " plt.subplot(2, 2, 3)\n", - " plt.plot(history['accuracy'], label='accuracy')\n", - " try:\n", - " plt.plot(history['val_accuracy'], label='val_accuracy', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_accuracy.')\n", - " plt.title('Model Accuracy')\n", - " plt.ylabel('Accuracy')\n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - " \n", - " # Density plot for accuracy\n", - " plt.subplot(2, 2, 4)\n", - " plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40)\n", - " try:\n", - " plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40)\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_accuracy (density plot).')\n", - " plt.title('Density Plot for Accuracy')\n", - " plt.xlabel('Accuracy')\n", - " plt.grid(True)\n", - "\n", - " # Delta Loss\n", - " plt.figure(figsize=(14, 8))\n", - " plt.subplot(2, 2, 1)\n", - " plt.plot(delta_loss, label='delta_loss')\n", - " try:\n", - " plt.plot(delta_val_loss, label='delta_val_loss', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load delta_val_loss.')\n", - " plt.title('Delta Model Loss')\n", - " plt.ylabel('Delta Loss')\n", - " plt.ylim(top=1.5, bottom=-1.5) \n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - " # Delta Accuracy\n", - " plt.subplot(2, 2, 2)\n", - " plt.plot(delta_accuracy, label='delta_accuracy')\n", - " try:\n", - " plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load delta_val_accuracy.')\n", - " plt.title('Delta Model Accuracy')\n", - " plt.ylabel('Delta Accuracy')\n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - "\n", - " # Calculate chunked data\n", - " chunked_loss = chunked_data(history['val_loss'], chunk_size)\n", - " chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size)\n", - "\n", - " # Clip the loss values to a maximum of max(history['val_loss'][10:])\n", - " max_loss = max(history['val_loss'][10:])\n", - " chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss)\n", - "\n", - " # Create 3D surface plots for each chunk\n", - " fig = plt.figure(figsize=(14, 8))\n", - " ax = fig.add_subplot(121, projection='3d')\n", - " X = np.arange(len(chunked_loss))\n", - " Y = np.arange(chunk_size)\n", - " X, Y = np.meshgrid(X, Y)\n", - " Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y\n", - " ax.plot_surface(X, Y, Z, cmap='viridis')\n", - " ax.set_title('3D Surface Plot of Chunked Loss')\n", - " ax.set_xlabel('Chunk Index')\n", - " ax.set_ylabel('Epoch')\n", - " ax.set_zlabel('Loss')\n", - "\n", - " ax = fig.add_subplot(122, projection='3d')\n", - " X = np.arange(len(chunked_accuracy))\n", - " Y = np.arange(chunk_size)\n", - " X, Y = np.meshgrid(X, Y)\n", - " Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y\n", - " ax.plot_surface(X, Y, Z, cmap='viridis')\n", - " ax.set_title('3D Surface Plot of Chunked Accuracy')\n", - " ax.set_xlabel('Chunk Index')\n", - " ax.set_ylabel('Epoch')\n", - " ax.set_zlabel('Accuracy')\n", - "\n", - " # Function to calculate the average of chunks\n", - " def chunked_average(values, chunk_size):\n", - " return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)]\n", - "\n", - " avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size)\n", - " avg_loss_chunks = chunked_average(history['val_loss'], chunk_size)\n", - "\n", - " # Find the chunk with the highest average accuracy\n", - " max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n", - " max_acc_value = avg_accuracy_chunks[max_acc_chunk_index]\n", - "\n", - " # Create a pile plot for accuracy\n", - " plt.figure(figsize=(10, 6))\n", - " plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy')\n", - " plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy')\n", - " plt.xlabel('Chunk')\n", - " plt.ylabel('Average Accuracy')\n", - " plt.title('Average Validation Accuracy per Chunk')\n", - " plt.legend()\n", - "\n", - " # Create a pile plot for loss\n", - " plt.figure(figsize=(10, 6))\n", - " plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss')\n", - " plt.xlabel('Chunk')\n", - " plt.ylabel('Average Loss')\n", - " plt.title('Average Validation Loss per Chunk')\n", - " plt.legend()\n", - "\n", - " # Function to calculate the average of each epoch across chunks, ignoring the first chunk\n", - " def average_across_chunks(values, chunk_size):\n", - " num_chunks = len(values) // chunk_size\n", - " avg_values = []\n", - " for epoch in range(chunk_size):\n", - " epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)]\n", - " avg_values.append(np.mean(epoch_values))\n", - " return avg_values\n", - "\n", - " # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk\n", - " avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size)\n", - " avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size)\n", - "\n", - " # Create a bar plot for average accuracy and loss of each epoch across chunks\n", - " plt.figure(figsize=(12, 6))\n", - "\n", - " # Create an index for each epoch\n", - " epoch_indices = np.arange(len(avg_accuracy_epochs))\n", - "\n", - " # Plot accuracy and loss as bars\n", - " plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6)\n", - " plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('Epoch (within chunk)')\n", - " plt.ylabel('Average Value')\n", - " plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)')\n", - " plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers\n", - " plt.legend()\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "except (ValueError, NameError) as E:\n", - " print(f'\\033[91mFailed to load model history.\\nError: {E}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse model Predicting performance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradcam heatmap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### V2" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_heatmap(model, img_array, conv_layer_name, pred_index):\n", - " \"\"\"\n", - " Helper function to compute the heatmap for a given convolutional layer.\n", - " \"\"\"\n", - " grad_model = tf.keras.models.Model(\n", - " [model.inputs], \n", - " [model.get_layer(conv_layer_name).output, model.output]\n", - " )\n", - "\n", - " with tf.GradientTape() as tape:\n", - " conv_layer_output, preds = grad_model(img_array)\n", - " class_channel = preds[:, pred_index]\n", - "\n", - " grads = tape.gradient(class_channel, conv_layer_output)\n", - " pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n", - "\n", - " conv_layer_output = conv_layer_output[0]\n", - " heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis]\n", - " heatmap = tf.squeeze(heatmap)\n", - " heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n", - " return heatmap\n", - "\n", - "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0):\n", - " \"\"\"\n", - " Function to compute the Grad-CAM heatmap for a specific class, given an input image.\n", - " \"\"\"\n", - " if pred_index is None:\n", - " preds = model.predict(img_array)\n", - " pred_index = tf.argmax(preds[0])\n", - "\n", - " # Compute heatmap for the last convolutional layer\n", - " heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index)\n", - " \n", - " # Apply threshold and adjust sensitivity\n", - " heatmap = np.where(heatmap > threshold, heatmap, 0)\n", - " heatmap = heatmap ** sensitivity_map\n", - "\n", - " if second_last_conv_layer_name is not None:\n", - " # Compute heatmap for the second last convolutional layer\n", - " heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)\n", - " \n", - " # Apply threshold and adjust sensitivity\n", - " heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)\n", - " heatmap_second = heatmap_second ** sensitivity_map\n", - " \n", - " # Average the two heatmaps\n", - " heatmap = (heatmap + heatmap_second) / 2.0\n", - " \n", - " return heatmap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### V3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main test" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 3s 3s/step\n", - "20/20 [==============================] - 2s 98ms/step\n", - "The accuracy of the model on validation data is 100.00%\n", - "The accuracy of the model on test data is 96.96%\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Predicting: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 156/156 [19:28<00:00, 7.49s/dpb]\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "from sklearn.metrics import confusion_matrix, accuracy_score\n", - "from scipy.stats import binom\n", - "from tqdm import tqdm\n", - "import efficientnet.tfkeras\n", - "import cv2\n", - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "\n", - "Extra_EXT = '_T' # _T or _T_BL\n", - "prob_L = 0.9995\n", - "tick_spacing = 5\n", - "Train_data_test = False\n", - "if SAVE_TYPE == 'TF':\n", - " # Load the pre-trained model\n", - " model = load_model(f'PAI_model{Extra_EXT}')\n", - "else:\n", - " # Load the pre-trained model\n", - " model = load_model(f'PAI_model{Extra_EXT}.h5')\n", - "\n", - "# Ensure the model's input_shape matches your data\n", - "assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.'\n", - "\n", - "# Make predictions on validation data\n", - "val_predictions = model.predict(x_val)\n", - "val_predictions = np.argmax(val_predictions, axis=1)\n", - "\n", - "# Make predictions on Train data\n", - "if Train_data_test:\n", - " Train_predictions = model.predict(x_train)\n", - " Train_predictions = np.argmax(Train_predictions, axis=1)\n", - "\n", - "# Make predictions on test data\n", - "test_predictions = model.predict(x_test)\n", - "test_predictions = np.argmax(test_predictions, axis=1)\n", - "\n", - "# Convert y_val and y_test from one-hot encoder to their original form\n", - "y_val_original = np.argmax(y_val, axis=1)\n", - "y_test_original = np.argmax(y_test, axis=1)\n", - "if Train_data_test:\n", - " y_train_original = np.argmax(y_train, axis=1)\n", - "\n", - "# Calculate accuracy on validation data\n", - "val_accuracy = accuracy_score(y_val_original, val_predictions)\n", - "\n", - "# Calculate accuracy on Train data\n", - "if Train_data_test:\n", - " Train_accuracy = accuracy_score(y_val_original, Train_predictions)\n", - "\n", - "# Calculate accuracy on test data\n", - "test_accuracy = accuracy_score(y_test_original, test_predictions)\n", - "\n", - "# Print acc\n", - "if Train_data_test:\n", - " print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}')\n", - "print(f'The accuracy of the model on validation data is {val_accuracy:.2%}')\n", - "print(f'The accuracy of the model on test data is {test_accuracy:.2%}')\n", - "\n", - "# Visualize the predictions on validation data as a grid of squares\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 5, i+1)\n", - " plt.imshow(x_val[i])\n", - " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", - " plt.axis('off')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "#Heatmap\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 5, i+1)\n", - " img = x_val[i]\n", - " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) \n", - " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", - " heatmap = np.uint8(255 * heatmap)\n", - " # Apply Adaptive Histogram Equalization\n", - " clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) # Create CLAHE object\n", - " # heatmap = clahe.apply(heatmap)\n", - " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n", - " if RANGE_NOM:\n", - " superimposed_img = (heatmap / 255) * 0.7 + img\n", - " else:\n", - " superimposed_img = (heatmap / 255) * 0.5 + (img / 255)\n", - " #clip\n", - " superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1]\n", - " plt.imshow(superimposed_img)\n", - " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", - " plt.axis('off')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Define the list of labels\n", - "labels = ['NORMAL', 'PNEUMONIA']\n", - "\n", - "# Create a confusion matrix for validation data\n", - "val_cm = confusion_matrix(y_val_original, val_predictions)\n", - "\n", - "# Create a confusion matrix for test data\n", - "test_cm = confusion_matrix(y_test_original, test_predictions)\n", - "\n", - "# Plot the confusion matrix as a heatmap for validation data\n", - "plt.figure(figsize=(8, 6))\n", - "sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", - "plt.title('Confusion Matrix - Validation Data')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('True')\n", - "plt.show()\n", - "\n", - "# Plot the confusion matrix as a heatmap for test data\n", - "plt.figure(figsize=(8, 6))\n", - "sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", - "plt.title('Confusion Matrix - Test Data')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('True')\n", - "plt.show()\n", - "\n", - "# Define the range of test data sizes to use\n", - "data_sizes = range(1, len(x_test), 4) \n", - "# Calculate the probability of a wrong prediction based on test accuracy\n", - "prob_wrong = 1 - test_accuracy\n", - "\n", - "# Create a list to store the number of incorrect predictions for each test data size\n", - "incorrect_predictions = []\n", - "\n", - "# Generate predictions and track incorrect predictions for each data size\n", - "for size in tqdm(data_sizes, desc='Predicting', unit='dpb'):\n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " # Randomly select a subset of test data\n", - " indices = np.random.choice(len(x_test), size, replace=False)\n", - " x_test_subset = x_test[indices]\n", - " y_test_subset = y_test[indices]\n", - "\n", - " # Make predictions on the subset of test data\n", - " test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False)\n", - " test_predictions = np.argmax(test_predictions, axis=1)\n", - " y_test_original_subset = np.argmax(y_test_subset, axis=1)\n", - "\n", - " # Calculate the number of incorrect predictions\n", - " incorrect_preds = np.sum(test_predictions != y_test_original_subset)\n", - " incorrect_predictions.append(incorrect_preds)\n", - " \n", - "# Plot the number of incorrect predictions vs. the number of data points\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(data_sizes, incorrect_predictions)\n", - "plt.xlabel('Number of Data Points')\n", - "plt.ylabel('Number of Incorrect Predictions')\n", - "# Add gridlines for the x and y axes\n", - "plt.grid(True)\n", - "\n", - "# Change the tick spacing for the x and y axes\n", - "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50))\n", - "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", - "\n", - "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", - "plt.show()\n", - "\n", - "# Define the range of test data sizes to use\n", - "data_sizes = range(1, len(x_test), 1) \n", - "\n", - "# Calculate the probability of a wrong prediction based on test accuracy\n", - "prob_wrong = 1 - test_accuracy\n", - "\n", - "# Create a list to store the probability of getting at least one wrong answer for each test data size\n", - "probabilities = []\n", - "\n", - "# Calculate the probability of getting at least one wrong answer for each data size\n", - "for size in data_sizes:\n", - " # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", - " cdf = binom.cdf(0, size, prob_wrong)\n", - " # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", - " prob = 1 - cdf\n", - " probabilities.append(prob)\n", - "\n", - "# Find the index of the first data point that has a probability greater than prob_L%\n", - "index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", - "\n", - "# Limit the x-axis to the first data point that has a probability greater than prob_L%\n", - "data_sizes = data_sizes[:index+1]\n", - "probabilities = probabilities[:index+1]\n", - "\n", - "# Plot the probability vs. the number of data points\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(data_sizes, probabilities)\n", - "plt.xlabel('Number of Data Points')\n", - "plt.ylabel('Probability')\n", - "\n", - "# Add gridlines for the x and y axes\n", - "plt.grid(True)\n", - "\n", - "# Change the tick spacing for the x and y axes\n", - "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 10))\n", - "plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100))\n", - "\n", - "plt.ylim(top=1.01)\n", - "\n", - "plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# keras/TF model\n", + "
\n",
+    " Copyright (c) 2023 Aydin Hamedi\n",
+    " \n",
+    " This software is released under the MIT License.\n",
+    " https://opensource.org/licenses/MIT\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre Conf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-25T12:17:58.501889500Z", + "start_time": "2023-12-25T12:17:58.486457700Z" + }, + "notebookRunGroups": { + "groupValue": "21" + } + }, + "outputs": [], + "source": [ + "CPU_only = False # True to Force TF to use the cpu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pylibs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import time\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "if CPU_only:\n", + " os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n", + "import cv2\n", + "import glob \n", + "import keras\n", + "import pprint\n", + "import random\n", + "import shutil\n", + "import gzip\n", + "import glob\n", + "import pickle\n", + "import datetime\n", + "import subprocess\n", + "import gpu_control\n", + "import numpy as np\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", + "import seaborn as sns\n", + "from hyperas import optim\n", + "# import tensorflow_addons as tfa\n", + "from keras_adabound import AdaBound\n", + "from importlib import reload\n", + "from keras.losses import categorical_crossentropy\n", + "import tensorflow as tf\n", + "from keras.models import Model\n", + "from scipy.ndimage import zoom\n", + "import matplotlib.pyplot as plt\n", + "from model_profiler import model_profiler\n", + "from keras_gradient_noise import add_gradient_noise\n", + "from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax\n", + "# from tensorflow_addons.optimizers import Yogi\n", + "from adabelief_tf import AdaBeliefOptimizer\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from imblearn.over_sampling import SMOTE\n", + "from keras.regularizers import l2\n", + "from keras.models import load_model\n", + "from matplotlib import pyplot as plt\n", + "from PIL import Image, ImageDraw, ImageFont\n", + "from keras import Sequential\n", + "from random import randint, choice, shuffle\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.callbacks import TensorBoard\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.layers import Conv2D,\\\n", + " MaxPooling2D,\\\n", + " Flatten,\\\n", + " Dense,\\\n", + " Dropout,\\\n", + " BatchNormalization,\\\n", + " SeparableConv2D,\\\n", + " Input, Concatenate,\\\n", + " GlobalAveragePooling2D,\\\n", + " CuDNNLSTM, concatenate,\\\n", + " Reshape, Multiply\n", + "# Utils\n", + "from Utils.one_cycle import OneCycleLr\n", + "from Utils.lr_find import LrFinder\n", + "from Utils.print_color_V2_NEW import print_Color_V2\n", + "from Utils.print_color_V1_OLD import print_Color\n", + "from Utils.Other import *\n", + "# Other\n", + "tf.get_logger().setLevel('ERROR')\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "for gpu_instance in physical_devices:\n", + " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conf\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data processing conf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [], + "source": [ + "# Directory paths# Directory paths for training, test and validation image data\n", + "train_dir = 'Database\\\\Train\\\\Data\\\\train'\n", + "test_dir = 'Database\\\\Train\\\\Data\\\\test'\n", + "validation_dir = 'Database\\\\Train\\\\Data\\\\val'\n", + "img_res = [224, 224, 3]\n", + "# img_res = [324, 324, 3]\n", + "# img_res = [224, 224, 3]\n", + "# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!)\n", + "interpolation_order_IFG = 2\n", + "categorical_IMP = True\n", + "Make_EV_DATA = False\n", + "R_fill_mode = True\n", + "add_img_grain = True\n", + "Save_TS = True\n", + "Use_SMOTE = False # (⚠️Beta⚠️)\n", + "ADBD = 1\n", + "OP_HDC = False\n", + "SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1\n", + "LNTS = 0\n", + "Debug_OUT = False\n", + "adjust_brightness_Mode = True\n", + "RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️)\n", + "scale_data_NP_M = False # (⚠️deprecated⚠️)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "SAVE_TYPE = 'H5'\n", + "Use_mixed_float16 = False\n", + "#Other\n", + "if Use_mixed_float16:\n", + " tf.keras.mixed_precision.set_global_policy('mixed_float16')\n", + "else:\n", + " tf.keras.mixed_precision.set_global_policy('float32')\n", + " \n", + "print(tf.keras.mixed_precision.global_policy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## data processing \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;33mUsing Def IDG...\u001b[0m\n", + "Found 8818 images belonging to 2 classes.\n", + "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", + "\u001b[0;33mMaking categorical data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m1\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "> Generating ADB[1/1]...\n", + "> β”œβ”€β”€β”€Applying adaptive histogram equalization...\n", + "> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = 0.8\n", + "> └───Adding the Generated ADB...\n", + "\u001b[0;33mNormalizing image data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m64.36% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m35.64% NORMAL\u001b[0m\n", + "\u001b[0;33mSetting LNTS...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m17636\u001b[0m\n", + "\u001b[0;33mshuffling data...\u001b[0m\n", + "\u001b[0;33mSaving TS...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2023_m12_d25-h18_m09_s57\u001b[0m\n", + "\u001b[0;32mDone.\u001b[0m\n" + ] + } + ], + "source": [ + "#Z_SCORE_normalize\n", + "def Z_SCORE_normalize(arr):\n", + " arr = arr.astype('float32')\n", + " mean = np.mean(arr)\n", + " std_dev = np.std(arr)\n", + " arr = (arr - mean) / std_dev\n", + " return arr\n", + "#normalize_TO_RANGE\n", + "def normalize_TO_RANGE(arr, min_val, max_val):\n", + " arr = arr.astype('float32')\n", + " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", + " arr = arr * (max_val - min_val) + min_val\n", + " return arr\n", + "#scale_data\n", + "def scale_data_NP(data):\n", + " if scale_data_NP_M:\n", + " data = data.astype('float32')\n", + " data = (data - 127.5) / 127.5\n", + " return data\n", + " else:\n", + " return data / 255\n", + "#add_image_grain\n", + "def add_image_grain(image, intensity = 0.01):\n", + " # Generate random noise array\n", + " noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8)\n", + "\n", + " # Scale the noise array\n", + " scaled_noise = (noise * intensity).astype(np.float32)\n", + " # Add the noise to the image\n", + " noisy_image = cv2.add(image, scaled_noise)\n", + "\n", + " return noisy_image\n", + "#apply_clahe_rgb_array\n", + "def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)):\n", + " # Create a CLAHE object\n", + " clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n", + " \n", + " # Iterate over each image in the array\n", + " for i in range(len(images)):\n", + " # Split the image into color channels\n", + " b, g, r = cv2.split(images[i])\n", + " \n", + " # Convert the channels to the appropriate format\n", + " b = cv2.convertScaleAbs(b)\n", + " g = cv2.convertScaleAbs(g)\n", + " r = cv2.convertScaleAbs(r)\n", + " \n", + " # Apply adaptive histogram equalization to each channel\n", + " equalized_b = clahe.apply(b)\n", + " equalized_g = clahe.apply(g)\n", + " equalized_r = clahe.apply(r)\n", + "\n", + " # Merge the equalized channels back into an image\n", + " equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r))\n", + "\n", + " # Replace the original image with the equalized image in the array\n", + " images[i] = equalized_image\n", + "\n", + " return images\n", + "#noise_func\n", + "def noise_func(image):\n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.05, 0.05)\n", + " intensityL1 = random.uniform(-0.04, 0.04)\n", + " else:\n", + " intensityL2 = random.uniform(-0.06, 0.06)\n", + " intensityL1 = random.uniform(-0.04, 0.04)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 64)\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image.shape[0], block_size_L2):\n", + " for j in range(0, image.shape[1], block_size_L2):\n", + " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image = new_image \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image.shape[0], block_size_L1):\n", + " for j in range(0, image.shape[1], block_size_L1):\n", + " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026\n", + " new_image = add_image_grain(new_image, intensity=intensity)\n", + " return new_image\n", + "#shuffle_data\n", + "def shuffle_data(x, y):\n", + " indices = np.arange(x.shape[0])\n", + " np.random.shuffle(indices)\n", + " x = x[indices]\n", + " y = y[indices]\n", + " return x, y\n", + "#save_images_to_dir\n", + "def save_images_to_dir(images, labels, dir_path):\n", + " # create the directory if it doesn't exist\n", + " if not os.path.exists(dir_path):\n", + " os.makedirs(dir_path)\n", + " # iterate over the images and labels\n", + " for i, (image, label) in enumerate(zip(images, labels)):\n", + " # get the class label\n", + " class_label = np.argmax(label)\n", + " # create the file path\n", + " file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png')\n", + " # save the image to the file path\n", + " plt.imsave(file_path, image.squeeze())\n", + " # compress the directory\n", + " shutil.make_archive(dir_path, 'gztar', dir_path)\n", + " # remove the original directory\n", + " shutil.rmtree(dir_path)\n", + "#Debug_img_Save\n", + "def Debug_img_Save(img, id = 'DEF'): \n", + " SITD = np.random.choice(img.shape[0], size=400, replace=False)\n", + " S_dir = f'Samples\\\\Debug\\\\{id}\\\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", + " save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir)\n", + "# Create an ImageDataGenerator for the training set\n", + "if OP_HDC:\n", + " print_Color('Using OP_HDC IDG...', ['yellow'])\n", + " train_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.24, \n", + " shear_range=0.22,\n", + " width_shift_range=0.21,\n", + " brightness_range=(0.86, 1.1),\n", + " height_shift_range=0.21,\n", + " channel_shift_range=100,\n", + " featurewise_center=False,\n", + " featurewise_std_normalization=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " fill_mode='nearest', # constant\n", + " preprocessing_function=noise_func\n", + " )\n", + "else:\n", + " print_Color('Using Def IDG...', ['yellow'])\n", + " train_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.26, \n", + " shear_range=0.25,\n", + " width_shift_range=0.25,\n", + " brightness_range=(0.78, 1.1),\n", + " height_shift_range=0.25,\n", + " channel_shift_range=100,\n", + " featurewise_center=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " featurewise_std_normalization=False,\n", + " fill_mode='nearest', # constant\n", + " preprocessing_function=noise_func\n", + " )\n", + "train_datagen_SM = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " vertical_flip=False,\n", + " rotation_range=20,\n", + " zoom_range=0.07, \n", + " shear_range=0.07,\n", + " width_shift_range=0.07,\n", + " brightness_range=(0.99, 1.01),\n", + " height_shift_range=0.07,\n", + " channel_shift_range=0,\n", + " featurewise_center=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " featurewise_std_normalization=False\n", + ")\n", + "# Create an iterator for the training set\n", + "train_generator_SM = train_datagen_SM.flow_from_directory(\n", + " train_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]),\n", + " class_mode='binary')\n", + "# Create an ImageDataGenerator for the validation set (OP)\n", + "if Make_EV_DATA:\n", + " val_datagen = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " zoom_range = 0.01, \n", + " width_shift_range=0.01, \n", + " interpolation_order=interpolation_order_IFG,\n", + " height_shift_range=0.01)\n", + "\n", + " # Create an iterator for the validation set\n", + " val_generator = val_datagen.flow_from_directory(\n", + " validation_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]),\n", + " class_mode='binary',\n", + " color_mode='rgb')\n", + "\n", + " # Create an ImageDataGenerator for the test set\n", + " test_datagen = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " zoom_range = 0.01, \n", + " width_shift_range=0.01, \n", + " interpolation_order=interpolation_order_IFG,\n", + " height_shift_range=0.01)\n", + "\n", + " # Create an iterator for the test set\n", + " test_generator = test_datagen.flow_from_directory(\n", + " test_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]),\n", + " class_mode='binary',\n", + " color_mode='rgb')\n", + "# Load all images and labels into memory\n", + "print_Color('Loading all images and labels into memory...', ['yellow'])\n", + "x_train, y_train = next(iter(train_generator_SM))\n", + "if Make_EV_DATA:\n", + " x_val, y_val = next(iter(val_generator))\n", + " x_test, y_test = next(iter(test_generator))\n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG\n", + "# fit parameters from data\n", + "# train_datagen.fit(x_train)\n", + "#to_categorical (TEMP)\n", + "if categorical_IMP:\n", + " print_Color('Making categorical data...', ['yellow'])\n", + " y_train = to_categorical(y_train, num_classes=2)\n", + " if Make_EV_DATA:\n", + " y_val = to_categorical(y_val, num_classes=2)\n", + " y_test = to_categorical(y_test, num_classes=2)\n", + "# Use_SMOTE\n", + "if Use_SMOTE:\n", + " print_Color('SMOTE...', ['yellow'])\n", + " # Convert y_train from one-hot encoding to label encoding\n", + " y_train_label_encoded = np.argmax(y_train, axis=1)\n", + "\n", + " # Print the original label distribution\n", + " unique, counts = np.unique(y_train_label_encoded, return_counts=True)\n", + " print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True)\n", + "\n", + " # Use SMOTE to oversample the minority class\n", + " smote = SMOTE(random_state=42)\n", + " x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded)\n", + "\n", + " # Print the resampled label distribution\n", + " unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True)\n", + " print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True)\n", + "\n", + " # Reshape x_train_res back to the original x_train shape\n", + " x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3])\n", + "\n", + " # Convert y_train_res from label encoding back to one-hot encoding\n", + " y_train_res = to_categorical(y_train_res_label_encoded)\n", + "\n", + " # Calculate the ratio of two labels after resampling\n", + " pneumonia_count = np.sum(y_train_res[:, 1])\n", + " total_count = y_train_res.shape[0]\n", + " label_ratio_res = pneumonia_count / total_count\n", + " label_ratio_percentage_res = label_ratio_res * 100\n", + "\n", + " # Replace the original data with the resampled data\n", + " x_train = x_train_res\n", + " y_train = y_train_res\n", + "\n", + " # Delete the resampled data to free up memory\n", + " del x_train_res, y_train_res_label_encoded, y_train_res\n", + "# Generating augmented data\n", + "print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...',\n", + " ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'],\n", + " advanced_mode=True)\n", + "if ADBD > 0:\n", + " for i in range(ADBD):\n", + " # ADB_clip_limit Scheduler>>>\n", + " if i == 0:\n", + " ADB_clip_limit = 0.8\n", + " else:\n", + " #V1>>>\n", + " CL_SLM = 2.4\n", + " ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05)\n", + " # Try it in win graphing calculator copy and paste:\n", + " # β”Œ-------------┬--┬---------------┐\n", + " # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚\n", + " # β””-------------β”΄--β”΄---------------β”˜\n", + " #V2>>>\n", + " # CL_SLM_2 = 1.4\n", + " # CL_SLM_Start_2 = 2\n", + " # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) \n", + " # Try it in win graphing calculator copy and paste:\n", + " # β”Œ-----------------┬--┬-------------------┐\n", + " # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚\n", + " # β””-----------------β”΄--β”΄-------------------β”˜\n", + " print(f'> Generating ADB[{i+1}/{ADBD}]...')\n", + " # prepare an iterators to scale images\n", + " train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train))\n", + "\n", + " # get augmented data\n", + " x_train_augmented, y_train_augmented = train_iterator.next()\n", + " print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...')\n", + " print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}')\n", + " x_train_augmented = np.clip(x_train_augmented, 0, 255) \n", + " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG\n", + " #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", + " x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss\n", + " print(f'> └───Adding the Generated ADB...')\n", + " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG\n", + " # append augmented data to original data\n", + " x_train = np.concatenate([x_train, x_train_augmented])\n", + " y_train = np.concatenate([y_train, y_train_augmented])\n", + " #free up memory\n", + " del y_train_augmented\n", + " del x_train_augmented\n", + "# normalizing \n", + "print_Color('Normalizing image data...', ['yellow'])\n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG\n", + "x_train = np.clip(x_train, 0, 255)\n", + "if RANGE_NOM:\n", + " x_train = scale_data_NP(x_train)\n", + "y_train = np.array(y_train) \n", + "if Make_EV_DATA:\n", + " x_test = np.clip(x_test, 0, 255) \n", + " x_val = np.clip(x_val, 0, 255) \n", + " if RANGE_NOM:\n", + " x_val = scale_data_NP(x_val)\n", + " y_val = np.array(y_val) \n", + " if RANGE_NOM: \n", + " x_test = scale_data_NP(x_test)\n", + " y_test = np.array(y_test) \n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG\n", + "# Check the data type of image data\n", + "print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True)\n", + "# Check the range of image data\n", + "print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", + "# Calculate the ratio of two labels\n", + "if categorical_IMP:\n", + " label_sums = np.sum(y_train, axis=0)\n", + " label_ratio = label_sums / (np.sum(y_train) + 1e-10)\n", + " label_ratio_percentage = label_ratio * 100\n", + " print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL',\n", + " ['normal', 'red', 'magenta', 'green'], advanced_mode=True) \n", + "print_Color('Setting LNTS...', ['yellow'])\n", + "# Get the total number of samples in the arrays\n", + "num_samples = x_train.shape[0]\n", + "print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", + "if LNTS != 0:\n", + " print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True)\n", + " print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True)\n", + " # Generate random indices to select LNTS samples\n", + " indices = np.random.choice(num_samples, size=LNTS, replace=False)\n", + " # Select the samples using the generated indices\n", + " x_selected = x_train[indices]\n", + " y_selected = y_train[indices]\n", + " x_train = x_selected\n", + " y_train = y_selected\n", + " #free up memory\n", + " del x_selected\n", + " del y_selected\n", + " del indices\n", + " #Debug\n", + " num_samples = x_train.shape[0]\n", + " print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", + "# Shuffle the training data\n", + "print_Color('shuffling data...', ['yellow'])\n", + "x_train, y_train = shuffle_data(x_train, y_train)\n", + "#save_images_to_dir \n", + "if Save_TS:\n", + " print_Color('Saving TS...', ['yellow'])\n", + " SITD = np.random.choice(num_samples, size=400, replace=False)\n", + " S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True)\n", + " if RANGE_NOM:\n", + " if scale_data_NP_M:\n", + " save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir)\n", + " else:\n", + " save_images_to_dir(x_train[SITD], y_train[SITD], S_dir)\n", + " else:\n", + " save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir)\n", + "print_Color('Done.', ['green'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save EV Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy', x_val)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy', y_val)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy', x_test)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy', y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load EV Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [], + "source": [ + "x_val = np.load(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy')\n", + "y_val = np.load(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy')\n", + "x_test = np.load(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy')\n", + "y_test = np.load(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Analyzation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import seaborn as sns\n", + "from scipy.stats import zscore\n", + "\n", + "# Select a subset of your data\n", + "subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels\n", + "subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values\n", + "indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False)\n", + "indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False)\n", + "subset_pixels = x_train[indices_pixels]\n", + "subset_mean = x_train[indices_mean]\n", + "\n", + "# Reshape the data for calculating Z-scores\n", + "reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1])\n", + "reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1])\n", + "\n", + "# Calculate the mean intensity\n", + "mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1)\n", + "mean_intensity_mean = reshaped_data_mean.mean(axis=-1)\n", + "\n", + "# Stack the mean intensity with the reshaped data\n", + "data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)])\n", + "data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)])\n", + "\n", + "# Calculate Z-scores\n", + "z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))\n", + "z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0))\n", + "\n", + "# Identify outliers\n", + "outliers_pixels = np.where(z_scores_pixels > 3)\n", + "outliers_mean = np.where(z_scores_mean > 3)\n", + "\n", + "# Create a 3D scatter plot for RGB channels\n", + "fig = plt.figure(figsize=(10, 20))\n", + "\n", + "# Plot for individual pixels\n", + "ax = fig.add_subplot(211, projection='3d')\n", + "ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1)\n", + "ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red')\n", + "ax.set_title('Z-Score Scatter Plot for Individual Pixels')\n", + "ax.set_xlabel('Red')\n", + "ax.set_ylabel('Green')\n", + "ax.set_zlabel('Blue')\n", + "\n", + "# Plot for mean RGB values\n", + "ax = fig.add_subplot(212, projection='3d')\n", + "ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1)\n", + "ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red')\n", + "ax.set_title('Z-Score Scatter Plot for Mean RGB Values')\n", + "ax.set_xlabel('Red')\n", + "ax.set_ylabel('Green')\n", + "ax.set_zlabel('Blue')\n", + "\n", + "# Density plot of the mean intensity\n", + "plt.figure(figsize=(10, 5))\n", + "sns.kdeplot(data=z_scores_pixels[:, -1], fill=True)\n", + "plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels')\n", + "plt.xlabel('Z-Score')\n", + "\n", + "sns.kdeplot(data=z_scores_mean[:, -1], fill=True)\n", + "plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values')\n", + "plt.xlabel('Z-Score')\n", + "\n", + "# Display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1\n", + "```\n", + "recommended: ⚠️\n", + "statuses: Ready\n", + "Working: βœ…\n", + "Max fine tuned acc: β‰…95.1\n", + "Max fine tuned acc TLRev2: N/A\n", + "type: transfer learning>>>(EfficientNetB7)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import EfficientNetB7\n", + "\n", + "EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax')\n", + "# define new model\n", + "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.summary()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.1\n", + "```\n", + "recommended: ❌\n", + "statuses: S.Ready (can improve)\n", + "Working: ❌\n", + "Max fine tuned acc: β‰…93.2\n", + "Max fine tuned acc TLRev2: N/A\n", + "type: transfer learning>>>(ConvNeXtLarge)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import ConvNeXtLarge\n", + "\n", + "ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False)\n", + "# define new model\n", + "model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.summary()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "source": [ + "### Rev1.2\n", + "```\n", + "recommended: βœ…\n", + "statuses: Ready\n", + "Working: βœ…\n", + "Max fine tuned acc: 95.3\n", + "Max fine tuned acc TLRev2: 96.96\n", + "type: transfer learning>>>(EfficientNetB7::CCL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total layers in the base model: 806\n", + "Freezing 0 layers in the base model...\n", + "Percentage of the base model that is frozen: 0.00%\n", + "Total model layers: 814\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 65,428,818\n", + "Non-trainable params: 312,768\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB7 as KENB7\n", + "# FUNC\n", + "def Eff_B7_NS(freeze_layers):\n", + " base_model = KENB7(input_shape=(\n", + " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) /\n", + " len(base_model.layers)) * 100\n", + " print(\n", + " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", + " Dense_L1 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(\n", + " inputs=base_model.input, outputs=predictions)\n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " # OPT/compile\n", + " opt = SGD(momentum=0.9, nesterov=False)\n", + " # opt = Nadam()\n", + " # opt = Adamax()\n", + " # opt = RMSprop(momentum=0.9)\n", + " # opt = Adagrad()\n", + " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B7_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.3\n", + "```\n", + "recommended: ❌\n", + "statuses: Test\n", + "Working: βœ…\n", + "Max fine tuned acc: ⚠️\n", + "Max fine tuned acc TLRev2: ⚠️\n", + "type: transfer learning>>>(EfficientNetB7|Xception::CCL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from efficientnet.keras import EfficientNetB7 as KENB7\n", + "from keras.applications.xception import Xception\n", + "\n", + "#FUNC\n", + "def Combo_Model(freeze_layers1, freeze_layers2):\n", + " # Define a common input\n", + " common_input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", + "\n", + " # Base model 1\n", + " base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " # base_model1.load_weights('models\\Ready\\Other\\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True)\n", + " base_model1_out = base_model1(common_input)\n", + " \n", + " # Base model 2\n", + " base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False)\n", + " # base_model1.load_weights('models\\Ready\\Other\\Xception_PRET.h5', by_name=True, skip_mismatch=True)\n", + " base_model2_out = base_model2(common_input)\n", + "\n", + " print('Total base_model1 layers: ', len(base_model1.layers))\n", + " print('Total base_model2 layers: ', len(base_model2.layers))\n", + " \n", + " # Freeze the specified number of layers in both models\n", + " for layer in base_model1.layers[:freeze_layers1]:\n", + " layer.trainable = False\n", + " for layer in base_model2.layers[:freeze_layers2]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest in both models\n", + " for layer in base_model1.layers[freeze_layers1:]:\n", + " layer.trainable = True\n", + " for layer in base_model2.layers[freeze_layers2:]:\n", + " layer.trainable = True\n", + "\n", + " # Combine the output of the two base models\n", + " combined = concatenate([GlobalAveragePooling2D()(base_model1_out), GlobalAveragePooling2D()(base_model2_out)])\n", + "\n", + " # adding CDL\n", + " Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined)\n", + " Dropout_L1 = Dropout(0.4)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " combo_model = Model(inputs=common_input, outputs=predictions) \n", + " print('Total model layers: ', len(combo_model.layers))\n", + " \n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return combo_model\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers_1 = 0\n", + "freeze_layers_2 = 0\n", + "model = Combo_Model(freeze_layers_1, freeze_layers_2)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.4\n", + "```\n", + "recommended: ⚠️\n", + "statuses: Test\n", + "Working: βœ…\n", + "Max fine tuned acc: ⚠️\n", + "Max fine tuned acc TLRev2: β‰…95.64\n", + "type: transfer learning>>>(EfficientNetV2XL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras_efficientnet_v2 import EfficientNetV2XL\n", + "\n", + "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n", + "# define new model\n", + "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", + "\n", + "# compile model\n", + "# opt = SGD(momentum=0.9)\n", + "opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n", + "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "freeze_layers = 0\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from efficientnet.keras import EfficientNetL2 as KENBL2\n", + "#FUNC\n", + "def Eff_B7_NS(freeze_layers):\n", + " base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n", + " weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n", + " include_top=False,\n", + " drop_connect_rate=0)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n", + " print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", + " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) \n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return model_EfficientNetB7_NS\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B7_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aydin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer VarianceScaling is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://github.com/leondgarse/keras_efficientnet_v2/releases/download/effnetv2_pretrained/efficientnetv2-s-imagenet.h5\n", + "87846816/87846816 [==============================] - 430s 5us/step\n", + ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-s-imagenet.h5\n", + "Model: \"model_2\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 24 648 ['input_3[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 24 96 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_swish (Activation) (None, 112, 112, 24 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 24 5184 ['stem_swish[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 24 96 ['stack_0_block0_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block0_fu_swish (Activ (None, 112, 112, 24 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add (Add) (None, 112, 112, 24 0 ['stem_swish[0][0]', Y \n", + " ) 'stack_0_block0_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 24 5184 ['add[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 24 96 ['stack_0_block1_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block1_fu_swish (Activ (None, 112, 112, 24 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add_1 (Add) (None, 112, 112, 24 0 ['add[0][0]', Y \n", + " ) 'stack_0_block1_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_1_block0_sortcut_conv (C (None, 56, 56, 96) 20736 ['add_1[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 96) 384 ['stack_1_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block0_sortcut_swish ( (None, 56, 56, 96) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 48) 4608 ['stack_1_block0_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_1_block1_sortcut_conv (C (None, 56, 56, 192) 82944 ['stack_1_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block1_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block1_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_2 (Add) (None, 56, 56, 48) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_1_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block2_sortcut_conv (C (None, 56, 56, 192) 82944 ['add_2[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block2_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block2_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_3 (Add) (None, 56, 56, 48) 0 ['add_2[0][0]', Y \n", + " 'stack_1_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block3_sortcut_conv (C (None, 56, 56, 192) 82944 ['add_3[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 192) 768 ['stack_1_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block3_sortcut_swish ( (None, 56, 56, 192) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 48) 9216 ['stack_1_block3_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 48) 192 ['stack_1_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_4 (Add) (None, 56, 56, 48) 0 ['add_3[0][0]', Y \n", + " 'stack_1_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block0_sortcut_conv (C (None, 28, 28, 192) 82944 ['add_4[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 192) 768 ['stack_2_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block0_sortcut_swish ( (None, 28, 28, 192) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 64) 12288 ['stack_2_block0_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_2_block1_sortcut_conv (C (None, 28, 28, 256) 147456 ['stack_2_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block1_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block1_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_5 (Add) (None, 28, 28, 64) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_2_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block2_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_5[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block2_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block2_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_6 (Add) (None, 28, 28, 64) 0 ['add_5[0][0]', Y \n", + " 'stack_2_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block3_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_6[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block3_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 64) 16384 ['stack_2_block3_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 64) 256 ['stack_2_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_7 (Add) (None, 28, 28, 64) 0 ['add_6[0][0]', Y \n", + " 'stack_2_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block0_sortcut_conv (C (None, 28, 28, 256) 16384 ['add_7[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_3_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 256) 2304 ['stack_3_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 256) 1024 ['stack_3_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 256) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 256) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", + " a) [0]'] \n", + " \n", + " stack_3_block0_se_1_conv (Conv (None, 1, 1, 16) 4112 ['tf.math.reduce_mean[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation (Activation) (None, 1, 1, 16) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block0_se_2_conv (Conv (None, 1, 1, 256) 4352 ['activation[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_1 (Activation) (None, 1, 1, 256) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply (Multiply) (None, 14, 14, 256) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_1[0][0]'] \n", + " \n", + " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 128) 32768 ['multiply[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_3_block1_sortcut_conv (C (None, 14, 14, 512) 65536 ['stack_3_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block1_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block1_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_1[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_2 (Activation) (None, 1, 1, 32) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block1_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_2[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_3 (Activation) (None, 1, 1, 512) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_1 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_3[0][0]'] \n", + " \n", + " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_1[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_8 (Add) (None, 14, 14, 128) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_3_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block2_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_8[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block2_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block2_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_2[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_4 (Activation) (None, 1, 1, 32) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block2_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_4[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_5 (Activation) (None, 1, 1, 512) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_2 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_5[0][0]'] \n", + " \n", + " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_2[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_9 (Add) (None, 14, 14, 128) 0 ['add_8[0][0]', Y \n", + " 'stack_3_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block3_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_9[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block3_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block3_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_3[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_6 (Activation) (None, 1, 1, 32) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block3_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_6[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_7 (Activation) (None, 1, 1, 512) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_3 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_7[0][0]'] \n", + " \n", + " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_3[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_10 (Add) (None, 14, 14, 128) 0 ['add_9[0][0]', Y \n", + " 'stack_3_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block4_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_10[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block4_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block4_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_4[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_8 (Activation) (None, 1, 1, 32) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block4_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_8[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_9 (Activation) (None, 1, 1, 512) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_4 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_9[0][0]'] \n", + " \n", + " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_4[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_11 (Add) (None, 14, 14, 128) 0 ['add_10[0][0]', Y \n", + " 'stack_3_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block5_sortcut_conv (C (None, 14, 14, 512) 65536 ['add_11[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 512) 2048 ['stack_3_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block5_sortcut_swish ( (None, 14, 14, 512) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 512) 4608 ['stack_3_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 512) 2048 ['stack_3_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 512) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 512) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block5_se_1_conv (Conv (None, 1, 1, 32) 16416 ['tf.math.reduce_mean_5[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_10 (Activation) (None, 1, 1, 32) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block5_se_2_conv (Conv (None, 1, 1, 512) 16896 ['activation_10[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_11 (Activation) (None, 1, 1, 512) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_5 (Multiply) (None, 14, 14, 512) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_11[0][0]'] \n", + " \n", + " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 128) 65536 ['multiply_5[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 128) 512 ['stack_3_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_12 (Add) (None, 14, 14, 128) 0 ['add_11[0][0]', Y \n", + " 'stack_3_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block0_sortcut_conv (C (None, 14, 14, 768) 98304 ['add_12[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_4_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block0_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_4_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_4_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_4_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_4_block0_se_1_conv (Conv (None, 1, 1, 32) 24608 ['tf.math.reduce_mean_6[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_12 (Activation) (None, 1, 1, 32) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block0_se_2_conv (Conv (None, 1, 1, 768) 25344 ['activation_12[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_13[0][0]'] \n", + " \n", + " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 160) 122880 ['multiply_6[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_4_block1_sortcut_conv (C (None, 14, 14, 960) 153600 ['stack_4_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block1_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_4_block1_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_7[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_14 (Activation) (None, 1, 1, 40) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block1_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_14[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_15 (Activation) (None, 1, 1, 960) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_7 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_15[0][0]'] \n", + " \n", + " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_7[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_13 (Add) (None, 14, 14, 160) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_4_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block2_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_13[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block2_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_4_block2_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_8[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_16 (Activation) (None, 1, 1, 40) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block2_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_16[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_17 (Activation) (None, 1, 1, 960) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_8 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_17[0][0]'] \n", + " \n", + " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_8[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_14 (Add) (None, 14, 14, 160) 0 ['add_13[0][0]', Y \n", + " 'stack_4_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block3_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_14[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block3_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 960) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_4_block3_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_9[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_18 (Activation) (None, 1, 1, 40) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block3_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_18[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_19 (Activation) (None, 1, 1, 960) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_9 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_19[0][0]'] \n", + " \n", + " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_9[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_15 (Add) (None, 14, 14, 160) 0 ['add_14[0][0]', Y \n", + " 'stack_4_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block4_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_15[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block4_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block4_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_10[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_20 (Activation) (None, 1, 1, 40) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block4_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_20[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_21 (Activation) (None, 1, 1, 960) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_10 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_21[0][0]'] \n", + " \n", + " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_10[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_16 (Add) (None, 14, 14, 160) 0 ['add_15[0][0]', Y \n", + " 'stack_4_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block5_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_16[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block5_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block5_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_11[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_22 (Activation) (None, 1, 1, 40) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block5_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_22[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_23 (Activation) (None, 1, 1, 960) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_11 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_23[0][0]'] \n", + " \n", + " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_11[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_17 (Add) (None, 14, 14, 160) 0 ['add_16[0][0]', Y \n", + " 'stack_4_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block6_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_17[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block6_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block6_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block6_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block6_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_12[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_24 (Activation) (None, 1, 1, 40) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block6_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_24[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_25 (Activation) (None, 1, 1, 960) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_12 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_25[0][0]'] \n", + " \n", + " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_12[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_18 (Add) (None, 14, 14, 160) 0 ['add_17[0][0]', Y \n", + " 'stack_4_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block7_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_18[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block7_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block7_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block7_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block7_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_13[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_26 (Activation) (None, 1, 1, 40) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block7_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_26[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_27 (Activation) (None, 1, 1, 960) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_13 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_27[0][0]'] \n", + " \n", + " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_13[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_19 (Add) (None, 14, 14, 160) 0 ['add_18[0][0]', Y \n", + " 'stack_4_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block8_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_19[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_4_block8_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_4_block8_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_4_block8_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 960) 8640 ['stack_4_block8_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 960) 3840 ['stack_4_block8_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 960) 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 960) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block8_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_14[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_28 (Activation) (None, 1, 1, 40) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block8_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_28[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_29 (Activation) (None, 1, 1, 960) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_14 (Multiply) (None, 14, 14, 960) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_29[0][0]'] \n", + " \n", + " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 160) 153600 ['multiply_14[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 160) 640 ['stack_4_block8_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_20 (Add) (None, 14, 14, 160) 0 ['add_19[0][0]', Y \n", + " 'stack_4_block8_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block0_sortcut_conv (C (None, 14, 14, 960) 153600 ['add_20[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 960) 3840 ['stack_5_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block0_sortcut_swish ( (None, 14, 14, 960) 0 ['stack_5_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 960) 8640 ['stack_5_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 960) 3840 ['stack_5_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 960) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 960) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block0_se_1_conv (Conv (None, 1, 1, 40) 38440 ['tf.math.reduce_mean_15[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_30 (Activation) (None, 1, 1, 40) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block0_se_2_conv (Conv (None, 1, 1, 960) 39360 ['activation_30[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_31 (Activation) (None, 1, 1, 960) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_15 (Multiply) (None, 7, 7, 960) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_31[0][0]'] \n", + " \n", + " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 256) 245760 ['multiply_15[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_5_block1_sortcut_conv (C (None, 7, 7, 1536) 393216 ['stack_5_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block1_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_16[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_32 (Activation) (None, 1, 1, 64) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_32[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_33 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_16 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_33[0][0]'] \n", + " \n", + " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_16[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_21 (Add) (None, 7, 7, 256) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_5_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block2_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_21[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block2_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_17 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_35[0][0]'] \n", + " \n", + " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_17[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_22 (Add) (None, 7, 7, 256) 0 ['add_21[0][0]', Y \n", + " 'stack_5_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block3_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_22[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block3_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_18 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_37[0][0]'] \n", + " \n", + " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_18[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_23 (Add) (None, 7, 7, 256) 0 ['add_22[0][0]', Y \n", + " 'stack_5_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block4_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_23[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block4_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_19 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_39[0][0]'] \n", + " \n", + " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_19[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_24 (Add) (None, 7, 7, 256) 0 ['add_23[0][0]', Y \n", + " 'stack_5_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block5_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_24[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block5_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_20 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_41[0][0]'] \n", + " \n", + " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_20[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_25 (Add) (None, 7, 7, 256) 0 ['add_24[0][0]', Y \n", + " 'stack_5_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block6_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_25[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block6_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block6_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block6_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_21 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_43[0][0]'] \n", + " \n", + " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_21[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_26 (Add) (None, 7, 7, 256) 0 ['add_25[0][0]', Y \n", + " 'stack_5_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block7_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_26[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block7_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block7_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block7_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_22 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_45[0][0]'] \n", + " \n", + " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_22[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_27 (Add) (None, 7, 7, 256) 0 ['add_26[0][0]', Y \n", + " 'stack_5_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block8_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_27[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block8_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block8_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block8_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block8_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_23 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_47[0][0]'] \n", + " \n", + " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_23[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block8_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_28 (Add) (None, 7, 7, 256) 0 ['add_27[0][0]', Y \n", + " 'stack_5_block8_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block9_sortcut_conv (C (None, 7, 7, 1536) 393216 ['add_28[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 1536) 6144 ['stack_5_block9_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block9_sortcut_swish ( (None, 7, 7, 1536) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block9_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block9_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_24 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_49[0][0]'] \n", + " \n", + " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 256) 393216 ['multiply_24[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 256) 1024 ['stack_5_block9_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_29 (Add) (None, 7, 7, 256) 0 ['add_28[0][0]', Y \n", + " 'stack_5_block9_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block10_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_29[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block10_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block10_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block10_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block10_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block10_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block10_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_5_block10_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block10_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_25 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_51[0][0]'] \n", + " \n", + " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_25[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block10_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_30 (Add) (None, 7, 7, 256) 0 ['add_29[0][0]', Y \n", + " 'stack_5_block10_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block11_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_30[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block11_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block11_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block11_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block11_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block11_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block11_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_5_block11_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block11_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_26 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_53[0][0]'] \n", + " \n", + " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_26[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block11_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_31 (Add) (None, 7, 7, 256) 0 ['add_30[0][0]', Y \n", + " 'stack_5_block11_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block12_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_31[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block12_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block12_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block12_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block12_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block12_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block12_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_5_block12_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block12_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_27 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_55[0][0]'] \n", + " \n", + " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_27[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block12_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_32 (Add) (None, 7, 7, 256) 0 ['add_31[0][0]', Y \n", + " 'stack_5_block12_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block13_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_32[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block13_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block13_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block13_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block13_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block13_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block13_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_5_block13_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block13_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_28 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_57[0][0]'] \n", + " \n", + " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_28[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block13_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_33 (Add) (None, 7, 7, 256) 0 ['add_32[0][0]', Y \n", + " 'stack_5_block13_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block14_sortcut_conv ( (None, 7, 7, 1536) 393216 ['add_33[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 1536) 6144 ['stack_5_block14_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block14_sortcut_swish (None, 7, 7, 1536) 0 ['stack_5_block14_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 1536) 13824 ['stack_5_block14_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 1536) 6144 ['stack_5_block14_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block14_MB_dw_swish (A (None, 7, 7, 1536) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_5_block14_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block14_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_29 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_59[0][0]'] \n", + " \n", + " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 256) 393216 ['multiply_29[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 256) 1024 ['stack_5_block14_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_34 (Add) (None, 7, 7, 256) 0 ['add_33[0][0]', Y \n", + " 'stack_5_block14_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " post_conv (Conv2D) (None, 7, 7, 1280) 327680 ['add_34[0][0]'] Y \n", + " \n", + " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", + " \n", + " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", + " \n", + " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", + " 2D) \n", + " \n", + " dropout_2 (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", + " \n", + " predictions (Dense) (None, 2) 2562 ['dropout_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 20,333,922\n", + "Trainable params: 20,180,050\n", + "Non-trainable params: 153,872\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from keras_efficientnet_v2 import EfficientNetV2S\n", + "\n", + "EfficientNet_M = EfficientNetV2S(input_shape=(img_res[0], img_res[1], img_res[2]), num_classes=2, dropout=0.5)\n", + "# define new model\n", + "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "freeze_layers = 0\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import ConvNeXtXLarge\n", + "from keras.layers import Lambda\n", + "#FUNC\n", + "def Eff_B7_NS():\n", + " # Add a Lambda layer at the beginning to scale the input\n", + " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", + " x = Lambda(lambda image: image * 255)(input)\n", + " \n", + " base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x)\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model)\n", + " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) \n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "model = Eff_B7_NS()\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LR FINDER" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "#CONF/Other\n", + "LRF_OPT = SGD(momentum=0.9)\n", + "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", + "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", + "# Instantiate LrFinder\n", + "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", + "\n", + "# Start range_test\n", + "lr_find.range_test(LRF_dataset)\n", + "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model vis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dot_img_file = 'model_1.png'\n", + "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the full model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92mLoading model done.\n", + "Compiling the AI model...\u001b[0m\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 N \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] N \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] N \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] N \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] N \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] N \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] N \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 63,786,960\n", + "Non-trainable params: 1,954,626\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "import efficientnet.tfkeras\n", + "# Configuration\n", + "PRMC = False\n", + "freeze_from_opposite = False\n", + "Extra_EXT = '_T'\n", + "freeze_layers = 0 \n", + "randomly_frozen_layers = 0 \n", + "freeze_last_seven = True \n", + "# CEC_opt = Adagrad()\n", + "# CEC_opt = Yogi()\n", + "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", + "# CEC_opt = Adam()\n", + "# Main\n", + "try:\n", + " if SAVE_TYPE == 'TF':\n", + " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", + " else:\n", + " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", + "except (ImportError, IOError) as e:\n", + " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", + "else:\n", + " print('\\033[92mLoading model done.')\n", + " if not PRMC:\n", + " print('Compiling the AI model...\\033[0m')\n", + " \n", + " for layer in model.layers:\n", + " layer.trainable = True\n", + " \n", + " # Select random layers to freeze\n", + " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", + " \n", + " for i, layer in enumerate(model.layers):\n", + " if i in frozen_layer_indices:\n", + " layer.trainable = False\n", + " else:\n", + " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", + " layer.trainable = False\n", + " elif (not freeze_from_opposite) and i < freeze_layers:\n", + " layer.trainable = False\n", + " else:\n", + " layer.trainable = True\n", + " \n", + " for layer in model.layers[-7:]:\n", + " layer.trainable = not freeze_last_seven\n", + " \n", + " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + " model.summary(show_trainable=True, expand_nested=True)\n", + " print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading model weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.load_weights('PAI_model_weights.h5')\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reset FC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for layer in model.layers[-7:]:\n", + " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", + " weight_initializer = layer.kernel_initializer\n", + " bias_initializer = layer.bias_initializer\n", + "\n", + " old_weights, old_biases = layer.get_weights()\n", + "\n", + " layer.set_weights([\n", + " weight_initializer(shape=old_weights.shape),\n", + " bias_initializer(shape=len(old_biases))\n", + " ])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Rev2 (THE BEST)\n", + "```\n", + "Working: βœ…\n", + "Other:\n", + " + Tensorboard works.\n", + " + Perverts overfitting.\n", + " + Lower memory usage.\n", + " - Slow training.\n", + " + Achieving higher acc.\n", + " - Some models dont work.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training the model...\n", + "\u001b[0;33m\n", + "Setup Verbose:\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2023_m12_d26-h05_m19_s58]\u001b[0m\u001b[0;36m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0;33mSetup Verbose END.\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", + "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 1/6\n", + "128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260\n", + "Epoch 2/6\n", + "128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740\n", + "Epoch 3/6\n", + "128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926\n", + "Epoch 4/6\n", + "128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173\n", + "Epoch 5/6\n", + "128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894\n", + "Epoch 6/6\n", + "128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-006-0.8974.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8974\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m2.4019\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.8974359035491943\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m2.4019267559051514\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m676.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m405.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 7/12\n", + "128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029\n", + "Epoch 8/12\n", + "128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295\n", + "Epoch 9/12\n", + "128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926\n", + "Epoch 10/12\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702\n", + "Epoch 11/12\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071\n", + "Epoch 12/12\n", + "128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-012-0.9407.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5406\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.8974359035491943 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9407051205635071\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m2.4019267559051514 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.5405705571174622\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 13/18\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423\n", + "Epoch 14/18\n", + "128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247\n", + "Epoch 15/18\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958\n", + "Epoch 16/18\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135\n", + "Epoch 17/18\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247\n", + "Epoch 18/18\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-013-0.9423.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4656\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9407051205635071 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.942307710647583\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.5405705571174622 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.4656426012516022\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 19/24\n", + "128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263\n", + "Epoch 20/24\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343\n", + "Epoch 21/24\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199\n", + "Epoch 22/24\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263\n", + "Epoch 23/24\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327\n", + "Epoch 24/24\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-020-0.9343.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4089\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.942307710647583. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.4656426012516022 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.40894174575805664\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 25/30\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054\n", + "Epoch 26/30\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407\n", + "Epoch 27/30\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455\n", + "Epoch 28/30\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439\n", + "Epoch 29/30\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519\n", + "Epoch 30/30\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-029-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2338\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.942307710647583 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9519230723381042\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.40894174575805664 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.23381969332695007\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 31/36\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471\n", + "Epoch 32/36\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439\n", + "Epoch 33/36\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487\n", + "Epoch 34/36\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311\n", + "Epoch 35/36\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391\n", + "Epoch 36/36\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2253\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.23381969332695007 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.2253303825855255\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m65.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 37/42\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439\n", + "Epoch 38/42\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455\n", + "Epoch 39/42\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215\n", + "Epoch 40/42\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471\n", + "Epoch 41/42\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439\n", + "Epoch 42/42\n", + "128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-042-0.9503.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2070\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.2253303825855255 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.20697814226150513\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 43/48\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503\n", + "Epoch 44/48\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439\n", + "Epoch 45/48\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215\n", + "Epoch 46/48\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375\n", + "Epoch 47/48\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439\n", + "Epoch 48/48\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-043-0.9503.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2199\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9519230723381042. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.20697814226150513. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m66.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 49/54\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487\n", + "Epoch 50/54\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535\n", + "Epoch 51/54\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054\n", + "Epoch 52/54\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471\n", + "Epoch 53/54\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519\n", + "Epoch 54/54\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-050-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1662\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9519230723381042 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9535256624221802\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.20697814226150513 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.16622641682624817\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 55/60\n", + "128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439\n", + "Epoch 56/60\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519\n", + "Epoch 57/60\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343\n", + "Epoch 58/60\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535\n", + "Epoch 59/60\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487\n", + "Epoch 60/60\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-058-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2059\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624221802. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 61/66\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487\n", + "Epoch 62/66\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471\n", + "Epoch 63/66\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487\n", + "Epoch 64/66\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391\n", + "Epoch 65/66\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487\n", + "Epoch 66/66\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2938\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9535256624221802. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 67/72\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535\n", + "Epoch 68/72\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471\n", + "Epoch 69/72\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567\n", + "Epoch 70/72\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567\n", + "Epoch 71/72\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631\n", + "Epoch 72/72\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-071-0.9631.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2115\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9535256624221802 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9631410241127014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.16622641682624817. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 73/78\n", + "128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599\n", + "Epoch 74/78\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567\n", + "Epoch 75/78\n", + "128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551\n", + "Epoch 76/78\n", + "128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439\n", + "Epoch 77/78\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647\n", + "Epoch 78/78\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-077-0.9647.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1545\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9631410241127014 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9647436141967773\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.16622641682624817 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1544923484325409\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 79/84\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359\n", + "Epoch 80/84\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535\n", + "Epoch 81/84\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359\n", + "Epoch 82/84\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599\n", + "Epoch 83/84\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567\n", + "Epoch 84/84\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-082-0.9599.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1780\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 85/90\n", + "128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487\n", + "Epoch 86/90\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327\n", + "Epoch 87/90\n", + "128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926\n", + "Epoch 88/90\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327\n", + "Epoch 89/90\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535\n", + "Epoch 90/90\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9615.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2233\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 91/96\n", + "128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599\n", + "Epoch 92/96\n", + "128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990\n", + "Epoch 93/96\n", + "128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583\n", + "Epoch 94/96\n", + "128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519\n", + "Epoch 95/96\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311\n", + "Epoch 96/96\n", + "128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9599.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1648\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m319.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m249.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 97/102\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311\n", + "Epoch 98/102\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135\n", + "Epoch 99/102\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022\n", + "Epoch 100/102\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519\n", + "Epoch 101/102\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471\n", + "Epoch 102/102\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-100-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1764\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m67.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 103/108\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423\n", + "Epoch 104/108\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423\n", + "Epoch 105/108\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391\n", + "Epoch 106/108\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535\n", + "Epoch 107/108\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423\n", + "Epoch 108/108\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-106-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2677\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 109/114\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439\n", + "Epoch 110/114\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343\n", + "Epoch 111/114\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439\n", + "Epoch 112/114\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471\n", + "Epoch 113/114\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535\n", + "Epoch 114/114\n", + "128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-113-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2055\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 115/120\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119\n", + "Epoch 116/120\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638\n", + "Epoch 117/120\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439\n", + "Epoch 118/120\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407\n", + "Epoch 119/120\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022\n", + "Epoch 120/120\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-117-0.9439.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3367\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 121/126\n", + "128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567\n", + "Epoch 122/126\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487\n", + "Epoch 123/126\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503\n", + "Epoch 124/126\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535\n", + "Epoch 125/126\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583\n", + "Epoch 126/126\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-125-0.9583.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2373\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 127/132\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942\n", + "Epoch 128/132\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455\n", + "Epoch 129/132\n", + "128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439\n", + "Epoch 130/132\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535\n", + "Epoch 131/132\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455\n", + "Epoch 132/132\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-130-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2035\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m321.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 133/138\n", + "128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423\n", + "Epoch 134/138\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519\n", + "Epoch 135/138\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487\n", + "Epoch 136/138\n", + "128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503\n", + "Epoch 137/138\n", + "128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519\n", + "Epoch 138/138\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-134-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1983\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 139/144\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439\n", + "Epoch 140/144\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567\n", + "Epoch 141/144\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551\n", + "Epoch 142/144\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487\n", + "Epoch 143/144\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535\n", + "Epoch 144/144\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-140-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2415\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m324.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m251.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 145/150\n", + "128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503\n", + "Epoch 146/150\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375\n", + "Epoch 147/150\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750\n", + "Epoch 148/150\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423\n", + "Epoch 149/150\n", + "128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487\n", + "Epoch 150/150\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2961\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m250.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 151/156\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487\n", + "Epoch 152/156\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311\n", + "Epoch 153/156\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455\n", + "Epoch 154/156\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519\n", + "Epoch 155/156\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551\n", + "Epoch 156/156\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2054\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 157/162\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519\n", + "Epoch 158/162\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535\n", + "Epoch 159/162\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974\n", + "Epoch 160/162\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487\n", + "Epoch 161/162\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551\n", + "Epoch 162/162\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2812\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 163/168\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439\n", + "Epoch 164/168\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535\n", + "Epoch 165/168\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471\n", + "Epoch 166/168\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295\n", + "Epoch 167/168\n", + "128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391\n", + "Epoch 168/168\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3527\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m320.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m68.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 169/174\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247\n", + "Epoch 170/174\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343\n", + "Epoch 171/174\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487\n", + "Epoch 172/174\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135\n", + "Epoch 173/174\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439\n", + "Epoch 174/174\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3280\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m323.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 175/180\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567\n", + "Epoch 176/180\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215\n", + "Epoch 177/180\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551\n", + "Epoch 178/180\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519\n", + "Epoch 179/180\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567\n", + "Epoch 180/180\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2430\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 181/186\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231\n", + "Epoch 182/186\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279\n", + "Epoch 183/186\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535\n", + "Epoch 184/186\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375\n", + "Epoch 185/186\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135\n", + "Epoch 186/186\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9022\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5451\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m322.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.50 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 187/192\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295\n", + "Epoch 188/192\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567\n", + "Epoch 189/192\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471\n", + "Epoch 190/192\n", + "128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455\n", + "Epoch 191/192\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519\n", + "Epoch 192/192\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2548\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9647436141967773. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m338.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 193/198\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583\n", + "Epoch 194/198\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391\n", + "Epoch 195/198\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663\n", + "Epoch 196/198\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583\n", + "Epoch 197/198\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583\n", + "Epoch 198/198\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-195-0.9663.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9663\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1816\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9647436141967773 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9663461446762085\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m328.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 199/204\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551\n", + "Epoch 200/204\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599\n", + "Epoch 201/204\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647\n", + "Epoch 202/204\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567\n", + "Epoch 203/204\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599\n", + "Epoch 204/204\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2118\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1544923484325409. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 205/210\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551\n", + "Epoch 206/210\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503\n", + "Epoch 207/210\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391\n", + "Epoch 208/210\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551\n", + "Epoch 209/210\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503\n", + "Epoch 210/210\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-205-0.9551.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1544\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1544923484325409 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.15437141060829163\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m329.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 211/216\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551\n", + "Epoch 212/216\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263\n", + "Epoch 213/216\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750\n", + "Epoch 214/216\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615\n", + "Epoch 215/216\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327\n", + "Epoch 216/216\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3144\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m328.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 217/222\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199\n", + "Epoch 218/222\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391\n", + "Epoch 219/222\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535\n", + "Epoch 220/222\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199\n", + "Epoch 221/222\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247\n", + "Epoch 222/222\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4185\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 223/228\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519\n", + "Epoch 224/228\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119\n", + "Epoch 225/228\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455\n", + "Epoch 226/228\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295\n", + "Epoch 227/228\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599\n", + "Epoch 228/228\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2645\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 229/234\n", + "128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942\n", + "Epoch 230/234\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231\n", + "Epoch 231/234\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471\n", + "Epoch 232/234\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551\n", + "Epoch 233/234\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022\n", + "Epoch 234/234\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3982\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 235/240\n", + "128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183\n", + "Epoch 236/240\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583\n", + "Epoch 237/240\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551\n", + "Epoch 238/240\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439\n", + "Epoch 239/240\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599\n", + "Epoch 240/240\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2006\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m325.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 241/246\n", + "128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487\n", + "Epoch 242/246\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535\n", + "Epoch 243/246\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631\n", + "Epoch 244/246\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615\n", + "Epoch 245/246\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599\n", + "Epoch 246/246\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2011\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 247/252\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599\n", + "Epoch 248/252\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535\n", + "Epoch 249/252\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535\n", + "Epoch 250/252\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615\n", + "Epoch 251/252\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583\n", + "Epoch 252/252\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2136\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 253/258\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583\n", + "Epoch 254/258\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631\n", + "Epoch 255/258\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599\n", + "Epoch 256/258\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599\n", + "Epoch 257/258\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599\n", + "Epoch 258/258\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2042\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m327.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 259/264\n", + "128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599\n", + "Epoch 260/264\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439\n", + "Epoch 261/264\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343\n", + "Epoch 262/264\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439\n", + "Epoch 263/264\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343\n", + "Epoch 264/264\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3485\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m252.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 265/270\n", + "128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535\n", + "Epoch 266/270\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471\n", + "Epoch 267/270\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455\n", + "Epoch 268/270\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599\n", + "Epoch 269/270\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535\n", + "Epoch 270/270\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2324\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 271/276\n", + "128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407\n", + "Epoch 272/276\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199\n", + "Epoch 273/276\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631\n", + "Epoch 274/276\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519\n", + "Epoch 275/276\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487\n", + "Epoch 276/276\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3108\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 277/282\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519\n", + "Epoch 278/282\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215\n", + "Epoch 279/282\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894\n", + "Epoch 280/282\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471\n", + "Epoch 281/282\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407\n", + "Epoch 282/282\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3641\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m326.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m253.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 283/288\n", + "128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638\n", + "Epoch 284/288\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183\n", + "Epoch 285/288\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327\n", + "Epoch 286/288\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478\n", + "Epoch 287/288\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862\n", + "Epoch 288/288\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8958\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7104\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m330.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 289/294\n", + "128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167\n", + "Epoch 290/294\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247\n", + "Epoch 291/294\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103\n", + "Epoch 292/294\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279\n", + "Epoch 293/294\n", + "128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215\n", + "Epoch 294/294\n", + "128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5959\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m330.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m254.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 295/300\n", + "128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151\n", + "Epoch 296/300\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494\n", + "Epoch 297/300\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279\n", + "Epoch 298/300\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327\n", + "Epoch 299/300\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311\n", + "Epoch 300/300\n", + "128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4308\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m331.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m255.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 301/306\n", + "128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846\n", + "Epoch 302/306\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990\n", + "Epoch 303/306\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311\n", + "Epoch 304/306\n", + "128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151\n", + "Epoch 305/306\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990\n", + "Epoch 306/306\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8974\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7052\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m362.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 307/312\n", + "128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183\n", + "Epoch 308/312\n", + "128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022\n", + "Epoch 309/312\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974\n", + "Epoch 310/312\n", + "128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279\n", + "Epoch 311/312\n", + "128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135\n", + "Epoch 312/312\n", + "128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5680\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 313/318\n", + "128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247\n", + "Epoch 314/318\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766\n", + "Epoch 315/318\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375\n", + "Epoch 316/318\n", + "128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375\n", + "Epoch 317/318\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231\n", + "Epoch 318/318\n", + "128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4632\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m286.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 319/324\n", + "128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263\n", + "Epoch 320/324\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439\n", + "Epoch 321/324\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455\n", + "Epoch 322/324\n", + "128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375\n", + "Epoch 323/324\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423\n", + "Epoch 324/324\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5101\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m389.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m287.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00908\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 325/330\n", + "128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263\n", + "Epoch 326/330\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455\n", + "Epoch 327/330\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391\n", + "Epoch 328/330\n", + "128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359\n", + "Epoch 329/330\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375\n", + "Epoch 330/330\n", + "128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3231\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m380.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m281.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00902\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 331/336\n", + "128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535\n", + "Epoch 332/336\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519\n", + "Epoch 333/336\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583\n", + "Epoch 334/336\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439\n", + "Epoch 335/336\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311\n", + "Epoch 336/336\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3479\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m392.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00896\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 337/342\n", + "128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327\n", + "Epoch 338/342\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167\n", + "Epoch 339/342\n", + "128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247\n", + "Epoch 340/342\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183\n", + "Epoch 341/342\n", + "128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263\n", + "Epoch 342/342\n", + "128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4083\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m387.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m285.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0089\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 343/348\n", + "128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343\n", + "Epoch 344/348\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279\n", + "Epoch 345/348\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359\n", + "Epoch 346/348\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343\n", + "Epoch 347/348\n", + "128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375\n", + "Epoch 348/348\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3776\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m268.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00884\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 349/354\n", + "128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439\n", + "Epoch 350/354\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135\n", + "Epoch 351/354\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327\n", + "Epoch 352/354\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599\n", + "Epoch 353/354\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375\n", + "Epoch 354/354\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4066\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00878\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 355/360\n", + "128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455\n", + "Epoch 356/360\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119\n", + "Epoch 357/360\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071\n", + "Epoch 358/360\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022\n", + "Epoch 359/360\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295\n", + "Epoch 360/360\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4142\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00872\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 361/366\n", + "128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311\n", + "Epoch 362/366\n", + "128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471\n", + "Epoch 363/366\n", + "128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135\n", + "Epoch 364/366\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263\n", + "Epoch 365/366\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151\n", + "Epoch 366/366\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7276\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00866\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 367/372\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391\n", + "Epoch 368/372\n", + "128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407\n", + "Epoch 369/372\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551\n", + "Epoch 370/372\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407\n", + "Epoch 371/372\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407\n", + "Epoch 372/372\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2727\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m266.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0086\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 373/378\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279\n", + "Epoch 374/378\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535\n", + "Epoch 375/378\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311\n", + "Epoch 376/378\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343\n", + "Epoch 377/378\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295\n", + "Epoch 378/378\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4211\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m334.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00854\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 379/384\n", + "128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006\n", + "Epoch 380/384\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263\n", + "Epoch 381/384\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183\n", + "Epoch 382/384\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359\n", + "Epoch 383/384\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519\n", + "Epoch 384/384\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3692\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9663461446762085. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00848\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 385/390\n", + "128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423\n", + "Epoch 386/390\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359\n", + "Epoch 387/390\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679\n", + "Epoch 388/390\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503\n", + "Epoch 389/390\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423\n", + "Epoch 390/390\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-387-0.9679.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1823\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9663461446762085 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9679487347602844\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m257.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00842\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 391/396\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647\n", + "Epoch 392/396\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519\n", + "Epoch 393/396\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359\n", + "Epoch 394/396\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423\n", + "Epoch 395/396\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615\n", + "Epoch 396/396\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2462\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m340.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00836\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 397/402\n", + "128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583\n", + "Epoch 398/402\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583\n", + "Epoch 399/402\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215\n", + "Epoch 400/402\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647\n", + "Epoch 401/402\n", + "128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599\n", + "Epoch 402/402\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2736\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m348.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m267.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0083\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 403/408\n", + "128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503\n", + "Epoch 404/408\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295\n", + "Epoch 405/408\n", + "128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423\n", + "Epoch 406/408\n", + "128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423\n", + "Epoch 407/408\n", + "128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423\n", + "Epoch 408/408\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2983\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.47 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m276.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.08 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00824\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 409/414\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487\n", + "Epoch 410/414\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343\n", + "Epoch 411/414\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551\n", + "Epoch 412/414\n", + "128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567\n", + "Epoch 413/414\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407\n", + "Epoch 414/414\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3477\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00818\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 415/420\n", + "128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455\n", + "Epoch 416/420\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407\n", + "Epoch 417/420\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455\n", + "Epoch 418/420\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263\n", + "Epoch 419/420\n", + "128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295\n", + "Epoch 420/420\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4541\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m342.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m79.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00812\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 421/426\n", + "128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615\n", + "Epoch 422/426\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567\n", + "Epoch 423/426\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199\n", + "Epoch 424/426\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631\n", + "Epoch 425/426\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599\n", + "Epoch 426/426\n", + "128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2110\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15437141060829163. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00806\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 427/432\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567\n", + "Epoch 428/432\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535\n", + "Epoch 429/432\n", + "128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631\n", + "Epoch 430/432\n", + "128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583\n", + "Epoch 431/432\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599\n", + "Epoch 432/432\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-429-0.9631.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1528\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9679487347602844. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.15437141060829163 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.15280155837535858\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m265.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.008\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 433/438\n", + "128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375\n", + "Epoch 434/438\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712\n", + "Epoch 435/438\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343\n", + "Epoch 436/438\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567\n", + "Epoch 437/438\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535\n", + "Epoch 438/438\n", + "128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-434-0.9712.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9696\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1676\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9679487347602844 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9695512652397156\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00794\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 439/444\n", + "128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696\n", + "Epoch 440/444\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663\n", + "Epoch 441/444\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615\n", + "Epoch 442/444\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455\n", + "Epoch 443/444\n", + "128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663\n", + "Epoch 444/444\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3061\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m288.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00788\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 445/450\n", + "128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647\n", + "Epoch 446/450\n", + "128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647\n", + "Epoch 447/450\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631\n", + "Epoch 448/450\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407\n", + "Epoch 449/450\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551\n", + "Epoch 450/450\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-445-0.9647.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9647\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2149\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m397.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00782\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 451/456\n", + "128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599\n", + "Epoch 452/456\n", + "128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567\n", + "Epoch 453/456\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551\n", + "Epoch 454/456\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679\n", + "Epoch 455/456\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679\n", + "Epoch 456/456\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-454-0.9679.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1664\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m400.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00776\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 457/462\n", + "128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551\n", + "Epoch 458/462\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696\n", + "Epoch 459/462\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663\n", + "Epoch 460/462\n", + "128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583\n", + "Epoch 461/462\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519\n", + "Epoch 462/462\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-458-0.9696.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9696\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1549\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.15280155837535858. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m394.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0077\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 463/468\n", + "128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647\n", + "Epoch 464/468\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631\n", + "Epoch 465/468\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551\n", + "Epoch 466/468\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503\n", + "Epoch 467/468\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679\n", + "Epoch 468/468\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-467-0.9679.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1365\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.15280155837535858 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.13646124303340912\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m398.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00764\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 469/474\n", + "128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535\n", + "Epoch 470/474\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327\n", + "Epoch 471/474\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359\n", + "Epoch 472/474\n", + "128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535\n", + "Epoch 473/474\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599\n", + "Epoch 474/474\n", + "128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2348\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m395.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00758\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 475/480\n", + "128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519\n", + "Epoch 476/480\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423\n", + "Epoch 477/480\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247\n", + "Epoch 478/480\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599\n", + "Epoch 479/480\n", + "128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647\n", + "Epoch 480/480\n", + "128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2441\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m399.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00752\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 481/486\n", + "128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551\n", + "Epoch 482/486\n", + "128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503\n", + "Epoch 483/486\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423\n", + "Epoch 484/486\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311\n", + "Epoch 485/486\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375\n", + "Epoch 486/486\n", + "128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m360.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00746\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 487/492\n", + "128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343\n", + "Epoch 488/492\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375\n", + "Epoch 489/492\n", + "128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071\n", + "Epoch 490/492\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135\n", + "Epoch 491/492\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503\n", + "Epoch 492/492\n", + "128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2877\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m338.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m256.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0074\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 493/498\n", + "128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503\n", + "Epoch 494/498\n", + "128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359\n", + "Epoch 495/498\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551\n", + "Epoch 496/498\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343\n", + "Epoch 497/498\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391\n", + "Epoch 498/498\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3008\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m341.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00734\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 499/504\n", + "128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247\n", + "Epoch 500/504\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103\n", + "Epoch 501/504\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263\n", + "Epoch 502/504\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343\n", + "Epoch 503/504\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359\n", + "Epoch 504/504\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3369\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m401.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00728\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 505/510\n", + "128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343\n", + "Epoch 506/510\n", + "128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487\n", + "Epoch 507/510\n", + "128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535\n", + "Epoch 508/510\n", + "128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631\n", + "Epoch 509/510\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519\n", + "Epoch 510/510\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2355\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m378.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m271.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m107.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00722\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 511/516\n", + "128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455\n", + "Epoch 512/516\n", + "128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567\n", + "Epoch 513/516\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279\n", + "Epoch 514/516\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423\n", + "Epoch 515/516\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567\n", + "Epoch 516/516\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2163\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m348.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.53 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00716\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 517/522\n", + "128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327\n", + "Epoch 518/522\n", + "128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375\n", + "Epoch 519/522\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327\n", + "Epoch 520/522\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215\n", + "Epoch 521/522\n", + "128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535\n", + "Epoch 522/522\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3130\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m265.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0071\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 523/528\n", + "128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471\n", + "Epoch 524/528\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503\n", + "Epoch 525/528\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471\n", + "Epoch 526/528\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439\n", + "Epoch 527/528\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455\n", + "Epoch 528/528\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3755\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00704\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 529/534\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487\n", + "Epoch 530/534\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615\n", + "Epoch 531/534\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599\n", + "Epoch 532/534\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631\n", + "Epoch 533/534\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615\n", + "Epoch 534/534\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1451\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m344.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [89] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m90\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 534)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00698\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 535/540\n", + "128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615\n", + "Epoch 536/540\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551\n", + "Epoch 537/540\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471\n", + "Epoch 538/540\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519\n", + "Epoch 539/540\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567\n", + "Epoch 540/540\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2261\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m376.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [90] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m91\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 540)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00692\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 541/546\n", + "128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263\n", + "Epoch 542/546\n", + "128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487\n", + "Epoch 543/546\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439\n", + "Epoch 544/546\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423\n", + "Epoch 545/546\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471\n", + "Epoch 546/546\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2810\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [91] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m92\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 546)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00686\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 547/552\n", + "128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439\n", + "Epoch 548/552\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471\n", + "Epoch 549/552\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215\n", + "Epoch 550/552\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391\n", + "Epoch 551/552\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503\n", + "Epoch 552/552\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2734\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m289.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [92] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m93\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 552)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0068\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 553/558\n", + "128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407\n", + "Epoch 554/558\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103\n", + "Epoch 555/558\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343\n", + "Epoch 556/558\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327\n", + "Epoch 557/558\n", + "128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295\n", + "Epoch 558/558\n", + "128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4314\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m343.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m258.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [93] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m94\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 558)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00674\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 559/564\n", + "128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231\n", + "Epoch 560/564\n", + "128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519\n", + "Epoch 561/564\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455\n", + "Epoch 562/564\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503\n", + "Epoch 563/564\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359\n", + "Epoch 564/564\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2974\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.88 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [94] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m95\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 564)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00668\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 565/570\n", + "128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455\n", + "Epoch 566/570\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279\n", + "Epoch 567/570\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183\n", + "Epoch 568/570\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503\n", + "Epoch 569/570\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391\n", + "Epoch 570/570\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4469\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m349.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [95] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m96\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 570)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00662\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 571/576\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247\n", + "Epoch 572/576\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958\n", + "Epoch 573/576\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455\n", + "Epoch 574/576\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343\n", + "Epoch 575/576\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391\n", + "Epoch 576/576\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3027\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m360.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [96] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m97\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 576)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00656\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 577/582\n", + "128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295\n", + "Epoch 578/582\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375\n", + "Epoch 579/582\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535\n", + "Epoch 580/582\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487\n", + "Epoch 581/582\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359\n", + "Epoch 582/582\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3618\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [97] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m98\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 582)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0065\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 583/588\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295\n", + "Epoch 584/588\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423\n", + "Epoch 585/588\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455\n", + "Epoch 586/588\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535\n", + "Epoch 587/588\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503\n", + "Epoch 588/588\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3557\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [98] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m99\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 588)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00644\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 589/594\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423\n", + "Epoch 590/594\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423\n", + "Epoch 591/594\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375\n", + "Epoch 592/594\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455\n", + "Epoch 593/594\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471\n", + "Epoch 594/594\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3592\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m262.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [99] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m100\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 594)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00638\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 595/600\n", + "128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407\n", + "Epoch 596/600\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359\n", + "Epoch 597/600\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311\n", + "Epoch 598/600\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439\n", + "Epoch 599/600\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455\n", + "Epoch 600/600\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3042\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m345.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m82.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [100] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m101\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 600)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00632\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 601/606\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503\n", + "Epoch 602/606\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311\n", + "Epoch 603/606\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487\n", + "Epoch 604/606\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471\n", + "Epoch 605/606\n", + "128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583\n", + "Epoch 606/606\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1757\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.73 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [101] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m102\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 606)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00626\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 607/612\n", + "128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471\n", + "Epoch 608/612\n", + "128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423\n", + "Epoch 609/612\n", + "128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247\n", + "Epoch 610/612\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103\n", + "Epoch 611/612\n", + "128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215\n", + "Epoch 612/612\n", + "128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4659\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m350.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [102] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m103\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 612)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0062\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 613/618\n", + "128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231\n", + "Epoch 614/618\n", + "128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519\n", + "Epoch 615/618\n", + "128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327\n", + "Epoch 616/618\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503\n", + "Epoch 617/618\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407\n", + "Epoch 618/618\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3278\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [103] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m104\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 618)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00614\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 619/624\n", + "128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407\n", + "Epoch 620/624\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087\n", + "Epoch 621/624\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926\n", + "Epoch 622/624\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327\n", + "Epoch 623/624\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359\n", + "Epoch 624/624\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4053\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [104] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m105\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 624)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00608\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 625/630\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551\n", + "Epoch 626/630\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471\n", + "Epoch 627/630\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423\n", + "Epoch 628/630\n", + "128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567\n", + "Epoch 629/630\n", + "128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615\n", + "Epoch 630/630\n", + "128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2148\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.13646124303340912. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m264.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [105] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m106\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 630)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00602\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 631/636\n", + "128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631\n", + "Epoch 632/636\n", + "128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679\n", + "Epoch 633/636\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519\n", + "Epoch 634/636\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663\n", + "Epoch 635/636\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583\n", + "Epoch 636/636\n", + "128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-632-0.9679.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9679\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1188\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.13646124303340912 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.11880630999803543\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m263.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [106] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m107\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 636)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00596\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 637/642\n", + "128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647\n", + "Epoch 638/642\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439\n", + "Epoch 639/642\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487\n", + "Epoch 640/642\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535\n", + "Epoch 641/642\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439\n", + "Epoch 642/642\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2388\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m353.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [107] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m108\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 642)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0059\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 643/648\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583\n", + "Epoch 644/648\n", + "128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455\n", + "Epoch 645/648\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391\n", + "Epoch 646/648\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423\n", + "Epoch 647/648\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423\n", + "Epoch 648/648\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2612\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m351.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m260.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [108] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m109\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 648)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00584\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 649/654\n", + "128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439\n", + "Epoch 650/654\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407\n", + "Epoch 651/654\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359\n", + "Epoch 652/654\n", + "128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423\n", + "Epoch 653/654\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359\n", + "Epoch 654/654\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3569\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m346.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m259.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [109] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m110\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 654)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00578\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 655/660\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471\n", + "Epoch 656/660\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295\n", + "Epoch 657/660\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327\n", + "Epoch 658/660\n", + "128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503\n", + "Epoch 659/660\n", + "128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423\n", + "Epoch 660/660\n", + "128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3959\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m347.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m261.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.56 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [110] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m111\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 660)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00572\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 661/666\n", + "128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471\n", + "Epoch 662/666\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439\n", + "Epoch 663/666\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679\n", + "Epoch 664/666\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583\n", + "Epoch 665/666\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535\n", + "Epoch 666/666\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2614\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m374.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m287.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [111] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m112\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 666)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00566\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 667/672\n", + "128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135\n", + "Epoch 668/672\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471\n", + "Epoch 669/672\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471\n", + "Epoch 670/672\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519\n", + "Epoch 671/672\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503\n", + "Epoch 672/672\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2773\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [112] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m113\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 672)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0056\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 673/678\n", + "128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487\n", + "Epoch 674/678\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439\n", + "Epoch 675/678\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471\n", + "Epoch 676/678\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455\n", + "Epoch 677/678\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423\n", + "Epoch 678/678\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3138\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m405.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m290.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [113] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m114\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 678)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00554\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 679/684\n", + "128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279\n", + "Epoch 680/684\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375\n", + "Epoch 681/684\n", + "128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647\n", + "Epoch 682/684\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423\n", + "Epoch 683/684\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615\n", + "Epoch 684/684\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2130\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m412.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [114] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m115\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 684)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00548\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 685/690\n", + "128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519\n", + "Epoch 686/690\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215\n", + "Epoch 687/690\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343\n", + "Epoch 688/690\n", + "128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359\n", + "Epoch 689/690\n", + "128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359\n", + "Epoch 690/690\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4240\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [115] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m116\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 690)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00542\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 691/696\n", + "128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263\n", + "Epoch 692/696\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375\n", + "Epoch 693/696\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375\n", + "Epoch 694/696\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359\n", + "Epoch 695/696\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375\n", + "Epoch 696/696\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3796\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [116] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m117\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 696)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00536\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 697/702\n", + "128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375\n", + "Epoch 698/702\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375\n", + "Epoch 699/702\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487\n", + "Epoch 700/702\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519\n", + "Epoch 701/702\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295\n", + "Epoch 702/702\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3449\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [117] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m118\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 702)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0053\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 703/708\n", + "128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503\n", + "Epoch 704/708\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615\n", + "Epoch 705/708\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487\n", + "Epoch 706/708\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503\n", + "Epoch 707/708\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519\n", + "Epoch 708/708\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.34 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [118] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m119\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 708)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00524\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 709/714\n", + "128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455\n", + "Epoch 710/714\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295\n", + "Epoch 711/714\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439\n", + "Epoch 712/714\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471\n", + "Epoch 713/714\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375\n", + "Epoch 714/714\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3194\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [119] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m120\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 714)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00518\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 715/720\n", + "128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327\n", + "Epoch 716/720\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423\n", + "Epoch 717/720\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391\n", + "Epoch 718/720\n", + "128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439\n", + "Epoch 719/720\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343\n", + "Epoch 720/720\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5143\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m408.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [120] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m121\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 720)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00512\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 721/726\n", + "128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391\n", + "Epoch 722/726\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359\n", + "Epoch 723/726\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199\n", + "Epoch 724/726\n", + "128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407\n", + "Epoch 725/726\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311\n", + "Epoch 726/726\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4177\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m414.06 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [121] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m122\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 726)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00506\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 727/732\n", + "128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359\n", + "Epoch 728/732\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295\n", + "Epoch 729/732\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327\n", + "Epoch 730/732\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311\n", + "Epoch 731/732\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279\n", + "Epoch 732/732\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4436\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [122] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m123\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 732)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.005\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 733/738\n", + "128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455\n", + "Epoch 734/738\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343\n", + "Epoch 735/738\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407\n", + "Epoch 736/738\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407\n", + "Epoch 737/738\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279\n", + "Epoch 738/738\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4303\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m412.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m294.39 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.57 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [123] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m124\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 738)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00494\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 739/744\n", + "128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231\n", + "Epoch 740/744\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974\n", + "Epoch 741/744\n", + "128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327\n", + "Epoch 742/744\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439\n", + "Epoch 743/744\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327\n", + "Epoch 744/744\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4234\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.41 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [124] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m125\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 744)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00488\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 745/750\n", + "128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455\n", + "Epoch 746/750\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327\n", + "Epoch 747/750\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439\n", + "Epoch 748/750\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359\n", + "Epoch 749/750\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343\n", + "Epoch 750/750\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3677\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [125] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m126\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 750)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00482\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 751/756\n", + "128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375\n", + "Epoch 752/756\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455\n", + "Epoch 753/756\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359\n", + "Epoch 754/756\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391\n", + "Epoch 755/756\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423\n", + "Epoch 756/756\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3409\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [126] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m127\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 756)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00476\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 757/762\n", + "128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375\n", + "Epoch 758/762\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599\n", + "Epoch 759/762\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391\n", + "Epoch 760/762\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391\n", + "Epoch 761/762\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359\n", + "Epoch 762/762\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3632\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m403.89 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [127] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m128\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 762)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0047\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 763/768\n", + "128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423\n", + "Epoch 764/768\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343\n", + "Epoch 765/768\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375\n", + "Epoch 766/768\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503\n", + "Epoch 767/768\n", + "128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391\n", + "Epoch 768/768\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3065\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m425.95 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m291.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [128] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m129\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 768)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00464\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 769/774\n", + "128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343\n", + "Epoch 770/774\n", + "128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439\n", + "Epoch 771/774\n", + "128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487\n", + "Epoch 772/774\n", + "128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503\n", + "Epoch 773/774\n", + "128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519\n", + "Epoch 774/774\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2221\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [129] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m130\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 774)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00458\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 775/780\n", + "128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487\n", + "Epoch 776/780\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535\n", + "Epoch 777/780\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327\n", + "Epoch 778/780\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455\n", + "Epoch 779/780\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423\n", + "Epoch 780/780\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3315\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m402.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [130] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m131\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 780)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00452\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 781/786\n", + "128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279\n", + "Epoch 782/786\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247\n", + "Epoch 783/786\n", + "128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391\n", + "Epoch 784/786\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359\n", + "Epoch 785/786\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231\n", + "Epoch 786/786\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5019\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m424.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m300.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [131] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m132\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 786)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00446\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 787/792\n", + "128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247\n", + "Epoch 788/792\n", + "128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263\n", + "Epoch 789/792\n", + "128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990\n", + "Epoch 790/792\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215\n", + "Epoch 791/792\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231\n", + "Epoch 792/792\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5337\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m299.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [132] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m133\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 792)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0044\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 793/798\n", + "128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231\n", + "Epoch 794/798\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375\n", + "Epoch 795/798\n", + "128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487\n", + "Epoch 796/798\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311\n", + "Epoch 797/798\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311\n", + "Epoch 798/798\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5005\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [133] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m134\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 798)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00434\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 799/804\n", + "128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151\n", + "Epoch 800/804\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071\n", + "Epoch 801/804\n", + "128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295\n", + "Epoch 802/804\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231\n", + "Epoch 803/804\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151\n", + "Epoch 804/804\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9151\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5460\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.80 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [134] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m135\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 804)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00428\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 805/810\n", + "128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103\n", + "Epoch 806/810\n", + "128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167\n", + "Epoch 807/810\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087\n", + "Epoch 808/810\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103\n", + "Epoch 809/810\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103\n", + "Epoch 810/810\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5751\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m414.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.13 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [135] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m136\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 810)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00422\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 811/816\n", + "128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327\n", + "Epoch 812/816\n", + "128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359\n", + "Epoch 813/816\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375\n", + "Epoch 814/816\n", + "128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423\n", + "Epoch 815/816\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407\n", + "Epoch 816/816\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3819\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.58 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m300.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [136] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m137\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 816)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00416\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 817/822\n", + "128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375\n", + "Epoch 818/822\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295\n", + "Epoch 819/822\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375\n", + "Epoch 820/822\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391\n", + "Epoch 821/822\n", + "128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407\n", + "Epoch 822/822\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4434\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.54 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [137] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m138\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 822)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0041\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 823/828\n", + "128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359\n", + "Epoch 824/828\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263\n", + "Epoch 825/828\n", + "128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407\n", + "Epoch 826/828\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167\n", + "Epoch 827/828\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295\n", + "Epoch 828/828\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4551\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.46 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [138] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m139\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 828)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00404\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 829/834\n", + "128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263\n", + "Epoch 830/834\n", + "128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375\n", + "Epoch 831/834\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231\n", + "Epoch 832/834\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375\n", + "Epoch 833/834\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359\n", + "Epoch 834/834\n", + "128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5237\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m418.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [139] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m140\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 834)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00398\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 835/840\n", + "128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375\n", + "Epoch 836/840\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151\n", + "Epoch 837/840\n", + "128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183\n", + "Epoch 838/840\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247\n", + "Epoch 839/840\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199\n", + "Epoch 840/840\n", + "128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5710\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m420.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [140] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m141\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 840)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00392\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 841/846\n", + "128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183\n", + "Epoch 842/846\n", + "128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231\n", + "Epoch 843/846\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231\n", + "Epoch 844/846\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231\n", + "Epoch 845/846\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199\n", + "Epoch 846/846\n", + "128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5128\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m423.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [141] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m142\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 846)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00386\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 847/852\n", + "128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215\n", + "Epoch 848/852\n", + "128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247\n", + "Epoch 849/852\n", + "128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359\n", + "Epoch 850/852\n", + "128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311\n", + "Epoch 851/852\n", + "128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295\n", + "Epoch 852/852\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5103\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m301.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [142] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m143\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 852)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0038\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 853/858\n", + "128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135\n", + "Epoch 854/858\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263\n", + "Epoch 855/858\n", + "128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151\n", + "Epoch 856/858\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183\n", + "Epoch 857/858\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038\n", + "Epoch 858/858\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9087\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7814\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.81 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [143] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m144\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 858)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00374\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 859/864\n", + "128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359\n", + "Epoch 860/864\n", + "128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391\n", + "Epoch 861/864\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343\n", + "Epoch 862/864\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423\n", + "Epoch 863/864\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391\n", + "Epoch 864/864\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3838\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [144] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m145\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 864)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00368\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 865/870\n", + "128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439\n", + "Epoch 866/870\n", + "128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487\n", + "Epoch 867/870\n", + "128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359\n", + "Epoch 868/870\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439\n", + "Epoch 869/870\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439\n", + "Epoch 870/870\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3789\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m413.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m295.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [145] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m146\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 870)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00362\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 871/876\n", + "128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103\n", + "Epoch 872/876\n", + "128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135\n", + "Epoch 873/876\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279\n", + "Epoch 874/876\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022\n", + "Epoch 875/876\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103\n", + "Epoch 876/876\n", + "128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9071\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5834\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.45 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [146] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m147\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 876)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00356\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 877/882\n", + "128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215\n", + "Epoch 878/882\n", + "128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439\n", + "Epoch 879/882\n", + "128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455\n", + "Epoch 880/882\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231\n", + "Epoch 881/882\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279\n", + "Epoch 882/882\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3896\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m417.42 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m297.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [147] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m148\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 882)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0035\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 883/888\n", + "128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167\n", + "Epoch 884/888\n", + "128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183\n", + "Epoch 885/888\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071\n", + "Epoch 886/888\n", + "128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183\n", + "Epoch 887/888\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199\n", + "Epoch 888/888\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5339\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m411.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m293.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [148] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m149\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 888)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00344\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 889/894\n", + "128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135\n", + "Epoch 890/894\n", + "128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231\n", + "Epoch 891/894\n", + "128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263\n", + "Epoch 892/894\n", + "128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231\n", + "Epoch 893/894\n", + "128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231\n", + "Epoch 894/894\n", + "128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4329\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m415.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m298.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [149] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m150\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 894)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00338\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 895/900\n", + "128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231\n", + "Epoch 896/900\n", + "128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103\n", + "Epoch 897/900\n", + "128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006\n", + "Epoch 898/900\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071\n", + "Epoch 899/900\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054\n", + "Epoch 900/900\n", + "128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9087\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.6034\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m409.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m292.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [150] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m151\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 900)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00332\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 901/906\n", + "128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151\n", + "Epoch 902/906\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151\n", + "Epoch 903/906\n", + "128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407\n", + "Epoch 904/906\n", + "128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519\n", + "Epoch 905/906\n", + "128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455\n", + "Epoch 906/906\n", + "128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{95.6756}, \u001b[0m\u001b[0;33mloss{0.0111}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{97.5646}, loss{0.0020}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3160\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9695512652397156. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.11880630999803543. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m416.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m296.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [151] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m152\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 906)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|2048|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00326\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 907/912\n", + "128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439\n", + "Epoch 908/912\n", + "128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407\n", + "Epoch 909/912\n", + "128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407\n", + "Epoch 910/912\n", + "128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311\n", + "Epoch 911/912\n", + " 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936\n", + "KeyboardInterrupt.\n", + "Training done.\n", + "\n" + ] + } + ], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "# CONF <-------------------------------------------------------------------------->\n", + "# Hyperparameters for training the model:\n", + "max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n", + "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", + "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", + "PL_epoch = 24 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n", + "subset_size = 2048 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 4096.\n", + "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", + "RES_Train = False # RES_Train: Resume training if True.\n", + "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", + "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n", + "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n", + "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n", + "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n", + "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n", + "Debug_OUTPUT_DPS_freq = 32 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n", + "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n", + "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n", + "USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True.\n", + "AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n", + "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n", + "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n", + "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n", + "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n", + "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n", + "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n", + "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n", + "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n", + "Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️).\n", + "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n", + "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n", + "Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n", + "BEST_RSN = 'PAI_model_T' # Best model save name prefix.\n", + "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n", + "IMAGE_GEN_PATH = 'Data\\\\image_SUB_generator.pkl'\n", + "# CONF END <---------------------------------------------------------------------->\n", + "#Prep\n", + "if RES_Train:\n", + " MAX_LR = RES_LR\n", + " PL_epoch = 1\n", + "#VAR\n", + "Total_SUB_epoch_C = 0 # TO FIX TensorBoard\n", + "CU_LR = MAX_LR\n", + "all_histories = []\n", + "chosen_indices = []\n", + "subset_sizes = []\n", + "best_acc = 0\n", + "best_loss = float('inf')\n", + "#Funcs\n", + "def normalize_TO_RANGE(arr, min_val, max_val):\n", + " arr = arr.astype('float32')\n", + " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", + " arr = arr * (max_val - min_val) + min_val\n", + " return arr\n", + "\n", + "def Z_SCORE_normalize(arr):\n", + " arr = arr.astype('float32')\n", + " mean = np.mean(arr)\n", + " std_dev = np.std(arr)\n", + " arr = (arr - mean) / std_dev\n", + " return arr\n", + "\n", + "def add_image_grain_TRLRev2(image, intensity = 0.01):\n", + " # Generate random noise array\n", + " noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \\\n", + " + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2\n", + "\n", + " # Scale the noise array\n", + " scaled_noise = (noise * intensity).astype(np.float32)\n", + " # Add the noise to the image\n", + " noisy_image = cv2.add(image, scaled_noise)\n", + "\n", + " return noisy_image\n", + "# noise_func_TRLRev2 ([REV1 OLD])\n", + "if not USE_REV2_DP:\n", + " def noise_func_TRLRev2(image): \n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.08, 0.08)\n", + " intensityL1 = random.uniform(-0.05, 0.05)\n", + " else:\n", + " intensityL2 = random.uniform(-0.09, 0.09)\n", + " intensityL1 = random.uniform(-0.06, 0.06)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 112)\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image.shape[0], block_size_L2):\n", + " for j in range(0, image.shape[1], block_size_L2):\n", + " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image = new_image \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image.shape[0], block_size_L1):\n", + " for j in range(0, image.shape[1], block_size_L1):\n", + " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.07) # Random intensity \n", + " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", + " return new_image\n", + "# noise_func_TRLRev2 ([REV2 NEW])\n", + "else:\n", + " def noise_func_TRLRev2(image):\n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.07, 0.07)\n", + " intensityL1 = random.uniform(-0.06, 0.06)\n", + " else:\n", + " intensityL2 = random.uniform(-0.09, 0.09)\n", + " intensityL1 = random.uniform(-0.07, 0.07)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 112)\n", + " \n", + " for channel in range(3): # Iterate over each RGB channel\n", + " image_channel = image[:, :, channel]\n", + " new_image_channel = new_image[:, :, channel]\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image_channel.shape[0], block_size_L2):\n", + " for j in range(0, image_channel.shape[1], block_size_L2):\n", + " block = image_channel[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image_channel = new_image_channel \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image_channel.shape[0], block_size_L1):\n", + " for j in range(0, image_channel.shape[1], block_size_L1):\n", + " block = image_channel[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " new_image[:, :, channel] = new_image_channel\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.05) # Random intensity \n", + " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", + " return new_image\n", + "#CONST\n", + "train_SUB_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.18, \n", + " shear_range=0.18,\n", + " width_shift_range=0.18,\n", + " brightness_range=(0.82, 1.18),\n", + " height_shift_range=0.18,\n", + " channel_shift_range=100,\n", + " featurewise_center=True,\n", + " featurewise_std_normalization=True,\n", + " zca_whitening=False,\n", + " interpolation_order=2,\n", + " fill_mode='nearest',\n", + " preprocessing_function=noise_func_TRLRev2\n", + " )\n", + "class TerminateOnHighTemp(tf.keras.callbacks.Callback):\n", + " def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60):\n", + " super().__init__()\n", + " self.active = active\n", + " self.check_every_n_batches = check_every_n_batches\n", + " self.high_temp = high_temp\n", + " self.low_temp = low_temp\n", + " self.pause_time = pause_time\n", + " self.batch_counter = 0\n", + "\n", + " def on_batch_end(self, batch, logs=None):\n", + " if not self.active:\n", + " return\n", + " self.batch_counter += 1\n", + " if self.batch_counter % self.check_every_n_batches == 0:\n", + " temperature = gpu_control.get_temperature()\n", + " if temperature > self.high_temp:\n", + " print_Color(f'\\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False)\n", + " time.sleep(self.pause_time) \n", + " while gpu_control.get_temperature() > self.low_temp:\n", + " time.sleep(4)\n", + " print_Color('Resuming training...', ['yellow'])\n", + "class ExtendedTensorBoard(TensorBoard):\n", + " def on_epoch_end(self, epoch, logs=None):\n", + " logs = logs or {}\n", + " logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr)\n", + " logs['momentum'] = self.model.optimizer.momentum \n", + " super().on_epoch_end(epoch, logs)\n", + "class DummyCallback(Callback):\n", + " pass\n", + "steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2\n", + "#callbacks>>>\n", + "# EarlyStopping\n", + "early_stopping = EarlyStopping(monitor='val_accuracy',\n", + " patience=EarlyStopping_P,\n", + " verbose=1, restore_best_weights=True,\n", + " mode='max'\n", + " ) if Use_ES_ONSUBT else DummyCallback()\n", + "# ModelCheckpoint \n", + "checkpoint_SUB = ModelCheckpoint(f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', \n", + " monitor=load_SUB_BRW_MODE,\n", + " save_best_only=True, mode=ModelCheckpoint_mode,\n", + " save_weights_only = True\n", + " ) if load_SUB_BRW else DummyCallback()\n", + "checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", + "# TerminateOnHighTemp\n", + "TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M,\n", + " check_every_n_batches=6,\n", + " high_temp=72,\n", + " low_temp=58,\n", + " pause_time=60)\n", + "# TensorBoard\n", + "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + "if Use_extended_tensorboard:\n", + " tensorboard_callback = ExtendedTensorBoard(\n", + " log_dir=log_dir,\n", + " write_images=False, # Uses a lot of memory\n", + " histogram_freq=1,\n", + " update_freq='epoch',\n", + " write_grads=True,\n", + " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", + " )\n", + "else:\n", + " tensorboard_callback = TensorBoard(\n", + " log_dir=log_dir,\n", + " write_images=False, # Uses a lot of memory\n", + " histogram_freq=1,\n", + " update_freq='epoch',\n", + " write_grads=True,\n", + " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", + " )\n", + "# OneCycleLr\n", + "if OneCycleLr_UFTS: \n", + " learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR,\n", + " steps_per_epoch=steps_per_epoch_train_SUB,\n", + " epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) \n", + "#PRES\n", + "# ...\n", + "#MAIN\n", + "print('Training the model...')\n", + "# INFOp\n", + "print_Color('\\nSetup Verbose:', ['yellow'])\n", + "print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "#warnings\n", + "P_warning('RES_Train is True.') if RES_Train else None\n", + "print_Color('Setup Verbose END.', ['yellow'])\n", + "# MAIN LOOP\n", + "try:\n", + " for epoch in range(1, max_epoch):\n", + " # Start Epoch\n", + " STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning'\n", + " C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT\n", + " if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1\n", + " start_FULL_time = time.time()\n", + " if Auto_clear_cache:\n", + " subprocess.run([\"Cache_clear.cmd\"], shell=True)\n", + " # TSEC: Total-Subset-Epoch-Count\n", + " print_Color(f'\\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True)\n", + " # DP\n", + " if not AdvSubsetC:\n", + " print_Color('Shuffling data...', ['yellow'])\n", + " x_train, y_train = shuffle_data(x_train, y_train)\n", + " print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", + " if AdvSubsetC:\n", + " if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0:\n", + " print_Color('└───Shuffling data...', ['yellow'])\n", + " x_train, y_train = shuffle_data(x_train, y_train)\n", + " chosen_indices = [] # Reset chosen_indices\n", + "\n", + " available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices))\n", + " \n", + " if len(available_indices) < subset_size:\n", + " #DEBUG\n", + " # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.')\n", + " # If there are not enough available indices, choose from the indices that were chosen the longest time ago\n", + " old_indices = chosen_indices[:subset_size - len(available_indices)]\n", + " subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False))\n", + " \n", + " # Update the list of chosen indices and their sizes\n", + " chosen_indices = chosen_indices[len(old_indices):] + subset_indices\n", + " subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices)\n", + " else:\n", + " subset_indices = list(np.random.choice(available_indices, subset_size, replace=False))\n", + " \n", + " # Add the chosen indices to the list of already chosen indices\n", + " chosen_indices += subset_indices\n", + " subset_sizes += [subset_size] * len(subset_indices)\n", + " else:\n", + " subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False)\n", + " # Taking the subset\n", + " x_SUB_train = x_train[subset_indices]\n", + " y_SUB_train = y_train[subset_indices]\n", + " x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train)\n", + " assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}'\n", + " print_Color('Preparing train data...', ['yellow']) \n", + " # if epoch == 1: # OLD\n", + " # print_Color('- ImageDataGenerator fit...', ['yellow']) \n", + " # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", + " # print_Color('- ImageDataGenerator fit done.', ['yellow'])\n", + " if epoch == 1 or ALWAYS_REFIT_IDG == 2:\n", + " if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG:\n", + " print_Color('- Loading fitted ImageDataGenerator...', ['yellow'])\n", + " train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) \n", + " else:\n", + " print_Color('- Fitting ImageDataGenerator...', ['yellow'])\n", + " IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n", + " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", + " pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb'))\n", + " print_Color('- ImageDataGenerator fit done.', ['yellow']) \n", + "\n", + " print_Color('- Augmenting Image Data...', ['yellow']) \n", + " train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255,\n", + " y_SUB_train,\n", + " shuffle=False,\n", + " batch_size=len(x_SUB_train)\n", + " ).next()\n", + " print_Color('- Normalizing Image Data...', ['yellow'])\n", + " x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255)\n", + " # x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 1) / 255\n", + " x_SUB_train = x_SUB_train / 255\n", + " x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1)\n", + " y_SUB_train = train_SUB_augmented_images[1]\n", + " # DEBUG\n", + " if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1):\n", + " SITD = np.random.choice(subset_size, size=400, replace=False)\n", + " S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", + " save_images_to_dir(x_SUB_train[SITD], y_SUB_train[SITD], S_dir)\n", + " # learning_rate_schedule_SUB\n", + " if PL_epoch == 0:\n", + " CU_LR = MIN_LR\n", + " elif epoch >= PL_epoch and CU_LR > MIN_LR:\n", + " if (CU_LR - DEC_LR) < MIN_LR:\n", + " CU_LR = MIN_LR\n", + " else:\n", + " CU_LR -= DEC_LR\n", + " if not OneCycleLr_UFTS: \n", + " learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR,\n", + " steps_per_epoch=steps_per_epoch_train_SUB,\n", + " epochs=C_subset_epoch)\n", + " #FV\n", + " print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + \"~*~*\") if not OneCycleLr_UFTS else \"~*OneCycleLr_UFTS Is ON~*\"}]~*...',\n", + " ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True)\n", + " print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", + " # Train\n", + " print_Color('Training on subset...', ['green'])\n", + " start_SUBO_time = time.time()\n", + " SUB_history = model.fit(x_SUB_train,\n", + " y_SUB_train,\n", + " epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C)\n", + " batch_size=Conf_batch_size_REV2,\n", + " validation_data=(x_test, y_test),\n", + " verbose='auto',\n", + " initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard\n", + " callbacks=[\n", + " learning_rate_schedule_SUB,\n", + " TerminateOnHighTemp_CB,\n", + " checkpoint_SUB,\n", + " early_stopping,\n", + " tensorboard_callback\n", + " ]\n", + " )\n", + " end_SUBO_time = time.time()\n", + " print_Color('Subset training done.', ['green'])\n", + " if load_SUB_BRW_LMODE == 1:\n", + " if max(SUB_history.history['val_accuracy']) > best_acc: \n", + " load_weights = True \n", + " elif min(SUB_history.history['val_loss']) < best_loss:\n", + " load_weights = True \n", + " else:\n", + " load_weights = False \n", + " else: \n", + " load_weights = True \n", + " \n", + " if load_SUB_BRW and load_weights:\n", + " print_Color('Loading the best weights...', ['yellow'])\n", + " # Get the filename of the best weights file\n", + " list_of_files = glob.glob('cache\\\\*.h5') \n", + " try:\n", + " best_weights_filename = max(list_of_files, key=os.path.getctime)\n", + " print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow'])\n", + " model.load_weights(best_weights_filename)\n", + " except Exception as Err:\n", + " print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red'])\n", + " elif load_SUB_BRW and (not load_weights):\n", + " # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_loss:.4f}}}]',\n", + " # ['yellow']) # OLD\n", + " print_Color_V2(f'Not loading weights[BSR:acc{{{95.675647:.4f}}}, loss{{{0.0111:.4f}}}|BTR:acc{{{97.56456:.4f}}}, loss{{{0.002:.4f}}}]')\n", + " all_histories.append(SUB_history.history)\n", + " checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " tf.keras.backend.clear_session() \n", + " # Evaluate the model on the test data\n", + " evaluation = model.evaluate(x_test, y_test, verbose=0)\n", + " \n", + " # Extract the loss and accuracy from the evaluation results\n", + " loss = evaluation[0]\n", + " acc = evaluation[1]\n", + " print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True)\n", + " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", + " # If the accuracy is higher than the best_acc\n", + " if acc > best_acc:\n", + " print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.')\n", + " # Update the best_acc\n", + " best_acc = acc\n", + " if SAVE_FULLM:\n", + " # Save the model\n", + " if SAVE_TYPE == 'TF':\n", + " print_Color_V2(f'Saving full model tf format...')\n", + " model.save(BEST_RSN, save_format='tf')\n", + " else:\n", + " print_Color_V2(f'Saving full model H5 format...')\n", + " model.save(f'{BEST_RSN}.h5')\n", + " model.save_weights('PAI_model_weights.h5')\n", + " else:\n", + " print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.')\n", + " \n", + " # If the loss is higher than the best_loss\n", + " if loss < best_loss:\n", + " print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.')\n", + " \n", + " # Update the best_acc\n", + " best_loss = loss\n", + " \n", + " if SAVE_FULLM:\n", + " # Save the model\n", + " if SAVE_TYPE == 'TF':\n", + " print_Color_V2(f'Saving full model tf format...')\n", + " model.save(BEST_RSN + '_BL', save_format='tf')\n", + " else:\n", + " print_Color_V2(f'Saving full model H5 format...')\n", + " model.save(f'{BEST_RSN}_BL.h5')\n", + " model.save_weights('PAI_model_weights_BL.h5')\n", + " else:\n", + " print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') \n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " tf.keras.backend.clear_session() \n", + " # Epoch end\n", + " end_time = time.time()\n", + " epoch_time = end_time - start_FULL_time\n", + " print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec')\n", + " epoch_SUB_time = end_SUBO_time - start_SUBO_time\n", + " print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec')\n", + " epoch_OTHERO_time = epoch_time - epoch_SUB_time\n", + " print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec')\n", + " print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan'])\n", + " Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard\n", + "except KeyboardInterrupt:\n", + " print('\\nKeyboardInterrupt.')\n", + "# End\n", + "try:\n", + " history = {}\n", + " for key in all_histories[0].keys():\n", + " # For each metric, concatenate the values from all histories\n", + " history[key] = np.concatenate([h[key] for h in all_histories])\n", + "except Exception as Err:\n", + " print(f'Failed to make model `history` var.\\nERROR: {Err}')\n", + " \n", + "print('Training done.\\n')\n", + "# del vars\n", + "try:\n", + " del train_SUB_datagen\n", + " del train_SUB_augmented_images\n", + "except NameError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Rev1 (⚠️deprecated⚠️)\n", + "```\n", + "Working: βœ…\n", + "Other:\n", + " + Tensorboard works.\n", + " - Can cause overfitting.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "#CONF\n", + "Conf_batch_size = 8 \n", + "OneCycleLr_epoch = 20\n", + "Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training)\n", + "#TensorBoard conf\n", + "TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining)\n", + "# Learning rate configuration\n", + "Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr\n", + "MAX_LR = 0.0174\n", + "# First time\n", + "if Learning_rate_conf == 1:\n", + " learning_rate_start = 8e-04\n", + " learning_rate_max = 5e-03\n", + " learning_rate_min = 5e-05\n", + " learning_rate_rampup_epochs = 5\n", + " learning_rate_sustain_epochs = 1\n", + " learning_rate_exp_decay = .3\n", + " #TEMP\n", + " # learning_rate_start = 8e-04\n", + " # learning_rate_max = 1e-02\n", + " # learning_rate_min = 8e-04\n", + " # learning_rate_rampup_epochs = 5\n", + " # learning_rate_sustain_epochs = 3\n", + " # learning_rate_exp_decay = .45\n", + "# 2th time\n", + "if Learning_rate_conf == 2:\n", + " if Learning_rate_conf_SET2C == 1:\n", + " learning_rate_start = 4.10e-06\n", + " learning_rate_max = 4.10e-06\n", + " learning_rate_min = 4.10e-06\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + " \n", + " elif Learning_rate_conf_SET2C == 2:\n", + " learning_rate_start = 4e-07\n", + " learning_rate_max = 4e-07\n", + " learning_rate_min = 4e-07\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + " \n", + " elif Learning_rate_conf_SET2C == 3:\n", + " learning_rate_start = 5e-04\n", + " learning_rate_max = 5e-04\n", + " learning_rate_min = 5e-04\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + "# Function to build learning rate schedule\n", + "if Learning_rate_conf in [1,2]:\n", + " def build_learning_rate_fn(lr_start=learning_rate_start,\n", + " lr_max=learning_rate_max,\n", + " lr_min=learning_rate_min,\n", + " lr_rampup_epochs=learning_rate_rampup_epochs,\n", + " lr_sustain_epochs=learning_rate_sustain_epochs,\n", + " lr_exp_decay=learning_rate_exp_decay): \n", + " lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync\n", + " def learning_rate_fn(epoch):\n", + " if epoch < lr_rampup_epochs:\n", + " lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n", + " elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n", + " lr = lr_max\n", + " else:\n", + " lr = (lr_max - lr_min) *\\\n", + " lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min\n", + " return lr\n", + " return learning_rate_fn\n", + " \n", + "# Calculate steps per epoch\n", + "steps_per_epoch_train = len(x_train) // Conf_batch_size\n", + "\n", + "# Set up callbacks\n", + "class EpochEndMON(tf.keras.callbacks.Callback):\n", + " def on_epoch_end(self, epoch, logs=None):\n", + " optimizer = self.model.optimizer\n", + " if hasattr(optimizer, 'lr'):\n", + " lr = tf.keras.backend.get_value(optimizer.lr)\n", + " print(f'\\nLearning rate for epoch {epoch+1} is {lr}')\n", + " if hasattr(optimizer, 'momentum'):\n", + " momentum = tf.keras.backend.get_value(optimizer.momentum)\n", + " print(f'Momentum for epoch {epoch+1} is {momentum}')\n", + " if logs:\n", + " val_loss = logs.get('val_loss')\n", + " val_acc = logs.get('val_accuracy')\n", + " print(f'Validation loss for epoch {epoch+1} is {val_loss}')\n", + " print(f'Validation accuracy for epoch {epoch+1} is {val_acc}')\n", + "\n", + " print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`')\n", + "\n", + "# Instantiate the callback\n", + "EpochEndMON_callback = EpochEndMON()\n", + "if Learning_rate_conf in [1,2]:\n", + " learning_rate_fn = build_learning_rate_fn()\n", + " learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1)\n", + "else:\n", + " learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch)\n", + "if SAVE_TYPE == 'TF':\n", + " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", + " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", + "else:\n", + " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", + " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", + "early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True)\n", + "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + "TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch'\n", + "tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True)\n", + "\n", + "# Train the model\n", + "print('Log dir:', log_dir)\n", + "#MInfo\n", + "print('Input Shape:', model.input_shape)\n", + "print('Output Shape:', model.output_shape)\n", + "print('Loss Function:', model.loss)\n", + "print('Training the model...\\n')\n", + "history = model.fit(x_train,\n", + " y_train,\n", + " epochs=256,\n", + " batch_size=Conf_batch_size,\n", + " validation_data=(x_test, y_test),\n", + " verbose='auto',\n", + " callbacks=[early_stopping,\n", + " tensorboard_callback,\n", + " learning_rate_schedule,\n", + " checkpoint_BVAC,\n", + " checkpoint_BVL,\n", + " EpochEndMON_callback])\n", + "print('Training done.\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving model weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [], + "source": [ + "Extra_EXT = '_T'\n", + "# Save the weights\n", + "print('Saving weights...')\n", + "model.save_weights('PAI_model_weights.h5')\n", + "print('Saving full model...')\n", + "if SAVE_TYPE == 'TF':\n", + " print('Saving full model tf format...')\n", + " model.save(f'PAI_model{Extra_EXT}', save_format='tf')\n", + "else:\n", + " try:\n", + " model.save(f'PAI_model{Extra_EXT}.h5')\n", + " except ValueError:\n", + " print('failed to save in .h5 format!')\n", + " print('Saving full model in tf format...')\n", + " model.save(f'PAI_model{Extra_EXT}', save_format='tf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Garbage Collection (memory)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse model Training performance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Save history\n", + "save_list(history, 'history\\\\model_history.pkl.gz', compress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load history\n", + "history = load_list('history\\\\model_history.pkl.gz', compressed=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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yfjh58qTq1asni8WiQYMGycPDQ19//XW6TwW8cOGCzX1HR0f5+vqm2v7ff//V008/rbZt26pDhw7Kly+fYmJirN933nrrLeXOnVvHjh3TwoULJUn+/v4aP368XnnlFTVv3lzPPfecJKlcuXKpHmfJkiVyc3NL16jUB/0cyax9nj9/Xg0bNtSFCxe0ceNGFS1a1LrOx8dHRYsW1ebNm9WvX78M1QTkCAaAHOW3334zJBlr1qwxDMMwEhMTjccee8x49dVXrW1Wr15tSDKWLl1qs22TJk2MIkWKWO9/++23hoODg/HTTz/ZtJswYYIhydi8ebN1mSTDwcHB2Lt3b7Ka4uLibO7Hx8cbZcqUMerXr29dtn37dkOS8dprr9m07dKliyHJGDp0qHVZt27djKCgIOP8+fM2bdu2bWv4+PgkO97dvvzyS0OSsXr1auuyhIQEo0CBAkaNGjVSrdswDOOll14y3N3djf/++8+6rHPnzkahQoVs2t1dc2RkpOHq6mocP37cuuzPP/80HB0djbs/ilM6bnh4uM1rYxiGUbp0aSM0NDRZ2w0bNhiSjA0bNhiGcfv5DggIMMqUKWNcv37d2m7ZsmWGJGPIkCE2j0WSMWLECJt9Pvnkk0alSpWSHSslvXv3NgoWLGgkJiYahmEYP/zwgyHJ2Llzp7XNrVu3jMKFCxuFChUyLl68aLN90naGYRh169Y1vLy8bJ63u9uk9PwbhmEMHTo02XP7oP30r7/+MhwcHIzmzZsbCQkJKdaUkJBgPPbYY0abNm1s1o8ZM8awWCzGkSNHkh37XnUYhmGMHDnSsFgsNs9DWl+rxYsXG5KMUaNGWZfdunXLqFOnjiHJmDZt2j3rSepP8+bNS7XNa6+9Zkiy+ay4evWqUbhwYSM4ONj6XD377LNG6dKl73k8Hx8fo1evXvdsk546K1SoYAQEBBj//vuvddnvv/9uODg4GJ06dbIuS+ov7dq1S9fxUrudPn3a2jYt7+lLly4ZXl5eRrVq1Wzep4Zh299DQ0MNScaMGTOsy27cuGEEBgYaLVq0uG/dhQoVSrXmkSNHWtsl9a8+ffrY1NG0aVPD2dnZOHfunGEY/9+/3n//fZvjtGzZ0rBYLMahQ4cMw0jbe+fO+n788UfrspiYGMPFxcV4/fXX7/v4DMMwypYta7Rv3956/+233zb8/PyMmzdvWpelt55Vq1bZtLF3n798+bIhyXj22WfTvI0kw9nZ2fqaGMbt94Ik4/PPP7cuS+9nalr2mbTtuXPnjH379hn58+c3qlSpYly4cMHaZufOnff9rElN7dq1U/w/KqXPhWbNmhnu7u7GyZMnrcv++usvw8nJKcOPLzIy0nB2djYOHz5sXXbq1CnDy8vLqFu3rnXZtGnTDElG7dq1jVu3btkcK2nd0aNHrcvS+n7o06ePYbFYbP6f/ffff408efIk22dKkl6fu29J/eDu7xWG8f+fRRMmTLDZ16JFiwxJxrZt21I93rlz55J9T7oXX19fo3z58mlqaxhpf95S6tOG8WCvRdK227ZtM06fPm2ULl3aKFKkiHHs2LEUa23UqJFRsmTJND82ICfi9D0gh4mKilK+fPlUr149SbeHnrdp00azZ8+2ntJTv359+fn5ac6cOdbtLl68qDVr1qhNmzbWZfPmzVPJkiVVokQJnT9/3nqrX7++JGnDhg02xw4NDVWpUqWS1XTnL+8XL17U5cuXVadOHZtTE5KGwvfs2dNm2z59+tjcNwxDCxYsULNmzWQYhk1d4eHhunz58n1PeWjTpo1y5cplc/rExo0bdfLkSeupe3fXnTQaok6dOoqLi9P+/fvveYw7JSQkaPXq1YqMjNTjjz9uXV6yZEmFh4cna3/ncS9fvqzz588rNDRUR44cue8pDCn57bffFBMTo549e9rMCdG0aVOVKFEixdNNXn75ZZv7derU0ZEjR+57rFu3bmnOnDlq06aNdZRI0mlLd55GunPnTh09elSvvfZasjmAkrY7d+6cfvzxR73wwgs2z9udbTLiQfrp4sWLlZiYqCFDhsjBwfa/0KSaHBwc1L59ey1ZskRXr161rk8aqVG4cOF71ndnHdeuXdP58+dVs2ZNGYahnTt3Jmt/v9dqxYoVcnJyshnl5ujomOy99SBWrFihqlWr2pzu5unpqR49eujYsWP6888/Jd2e7+mff/7Rtm3bUt1X7ty59euvv+rUqVMPXNfp06e1a9cudenSRXny5LEuL1eunBo2bKgVK1Yk2+bu5/N+hgwZojVr1iS73Xm8tLyn16xZo6tXr+qtt95KNnfL3f3d09PTZk4oZ2dnVa1aNU3vUen2xMUp1dyuXbtkbXv37m1TR+/evRUfH6+1a9dKuv3aOzo6qm/fvjbbvf766zIMQytXrpSUtvdOklKlSqlOnTrW+/7+/ipevHiaHt/u3bu1Z88em8fSrl07nT9/XqtXr7YuS089hQsXTvZZbe8+f+XKFUmSl5dXmreRbp8udOdIjXLlysnb2zvNfedB9/nHH38oNDRUwcHBWrt2rc0onKSRUKtXr1ZcXFy6avj333/vOaInSUJCgtauXavIyEibif1DQkJSHWV4v8eXkJCgH374QZGRkTbzAgUFBen555/Xpk2brK9Xku7du6d5/qi0vB9WrVqlGjVqqEKFCtZlefLksflOkxYLFiyw+Uy48//tlLi4uCSbDiLp//Rly5aleBp7Rly5ciXdff1BPkcyY5///POPQkNDdfPmTf3444+pjuLz9fVNceQq8CghlAJykISEBM2ePVv16tXT0aNHdejQIR06dEjVqlXT2bNntW7dOkmSk5OTWrRooe+//946N9TChQt18+ZNm1Dqr7/+0t69e+Xv729ze+KJJyT9/8TCSVL7Y3vZsmWqXr26XF1dlSdPHuvQ7TsDluPHj8vBwSHZPu6+is65c+d06dIlTZo0KVldSV+M7q7rbnnz5lV4eLgWLVqk//77T9LtU/ecnJzUunVra7u9e/eqefPm8vHxkbe3t/z9/a1/CKYnHDp37pyuX7+uYsWKJVuX0vxCmzdvVlhYmHX+G39/f+upbxkJpY4fP57qsUqUKGFdn8TV1TXZaQy+vr66ePHifY/1ww8/6Ny5c6pataq1/x09elT16tXTrFmzlJiYKOn2qRuSVKZMmVT3lfQl715tMuJB+unhw4fl4OCQYqh1p06dOun69etatGiRpNun/2zfvl0dO3a8b30nTpywhihJ80QlzZd09+ufltfq+PHjCgoKSnZFpfvNbZUex48fT3F/JUuWtK6XpIEDB8rT01NVq1ZVsWLF1KtXL23evNlmm1GjRumPP/5QwYIFVbVqVQ0bNizDf0Tcq++XLFlS58+fT3bqzP1Cw7uVLVtWYWFhyW53nhqdlvd0Wt4TSR577LFkwUla36OS5Ofnl2LNd//R5ODgkGzy3aTP/6S5Vo4fP678+fMn+4Px7tc+re8dSclC6PQ8vu+++04eHh4qUqSI9TPI1dXVeiXQJOmpJ6U+Ye8+7+3tLUk2wXdaPMhzmxn7bNasmby8vLR69WrrY0hSuHBh9e/fX19//bX8/PwUHh6uL7/8Ms3/7xlpON02JiZG169fT/EKfaldte9+j+/cuXOKi4tLtT8kJibq77//tlmens+ZtDy/SfNz3S29VyKsW7euzWdCrVq17tm+QIECya7IFxoaqhYtWmj48OHy8/PTs88+q2nTpiWbizQ9vL29s11f79ixo2JiYrRx40YVKFAg1X0ahvFAP7QBOQGhFJCDrF+/XqdPn9bs2bNVrFgx6y0paLnzC3nbtm119epV66/Yc+fOVYkSJVS+fHlrm8TERJUtWzbFX9TXrFmTbFRTSlcj+umnnxQRESFXV1d99dVXWrFihdasWaPnn38+TV8g75YUanTo0CHVuu73JSpp+ytXrmjZsmWKj4/XggULrHMgSLcnhg4NDdXvv/+uESNGaOnSpVqzZo3+97//2dSR2Q4fPqwGDRro/PnzGjNmjJYvX641a9ZY5xrIquPe6UGu/pPUx1q3bm3TB+fMmaOTJ09q48aNmVWmVWpf5lKbmN2MflqqVClVqlRJ3333naTbfyg7OzvbhJ6p1dywYUMtX75cAwcO1OLFi7VmzRpNnz5dUvLX395XakqvkiVL6sCBA5o9e7Zq166tBQsWqHbt2jbzbLVu3VpHjhzR559/rvz58+vjjz9W6dKlrZ9VWS2zr6qWFe/p1F73jPTVh1FGH59hGJo1a5auXbumUqVK2XwGHTt2TN9//71iY2PTXc+D9Ims6vPe3t7Knz+//vjjj3TVk5bnNr2fqel5vVq0aKHDhw+nOgLnk08+0e7du/X2229bL35SunRp/fPPPym2T5I3b94HChvuJSveb+npUw/z+z2lx2GxWDR//nxt2bJFvXv3tl6YplKlShl6/0m3f0A7ePCg4uPj07yNvfv6c889p0uXLiW7eMvdLl68mOoVD4FHBROdAzlIVFSUAgIC9OWXXyZbt3DhQi1atEgTJkyQm5ub6tatq6CgIM2ZM0e1a9fW+vXrba5oJklFixbV77//rgYNGmT4V5wFCxbI1dVVq1evtplwc9q0aTbtChUqpMTERB09etRmRNGhQ4ds2vn7+8vLy0sJCQlpvspOSiIiIuTl5aWZM2cqV65cunjxos0w9+joaP37779auHCh6tata11+9OjRdB8r6apxf/31V7J1d16FRbo9ce2NGze0ZMkSm1/k7j5VUkr7KWxJox8OHDhgPfXyzuOnZWLYtLh27Zq+//57tWnTJsXJSPv27auoqCjVq1fPeirEH3/8kerrmDRC435/dPn6+qZ4Jbm7R4DdS1r7adGiRZWYmKg///zT5jSJlHTq1En9+/fX6dOnNXPmTDVt2vS+p5fs2bNHBw8e1DfffKNOnTpZl69ZsybNj+VuhQoV0rp16xQbG2szWuruvvcgChUqlOL+kk5zvbOPeXh4qE2bNmrTpo3i4+P13HPP6YMPPtCgQYOsp60FBQWpZ8+e6tmzp2JiYlSxYkV98MEHaZrE++66pJQf6/79++Xn5ycPD4907TO90vqevvM9kd7RDVklMTFRR44csY6OkqSDBw9KknWC+EKFCmnt2rW6evWqzWipu1/79Lx3Mmrjxo36559/NGLECOuIpSQXL15Ujx49tHjxYnXo0OGB63kY+vwzzzyjSZMmacuWLapRo0a6H0NqMuMzNTUff/yxnJycrJOG3zmBf5KyZcuqbNmyevfdd/Xzzz+rVq1amjBhgt5///1U91uiRAktWLDgvscPCAiQq6trsu8WUvLvG2nl7+8vd3f3VPuDg4ODChYsmKF9p1WhQoUy9TFlhurVq6t69er64IMPNHPmTLVv316zZ8/Wiy++mO7vlM2aNdOWLVu0YMGCFE8zzqik/5MvXbpkM5VAZvT1Pn36KCQkREOGDJGPj4/eeuutFNsdPXrU5gdh4FHESCkgh7h+/boWLlyoZ555Ri1btkx26927t65evaolS5ZIun1aRsuWLbV06VJ9++23unXrls2pe9LtX29PnjypyZMnp3i8+10xRrr9q5LFYrH51enYsWPJrtyXNF/HV199ZbP8888/T7a/Fi1aaMGCBSmGFefOnbtvTdLtX/eaN2+uFStWaPz48fLw8NCzzz5rcxzJ9tev+Pj4ZPWlhaOjo8LDw7V48WKdOHHCunzfvn02c5ykdtzLly8nC0ek23/opPSHw90qV66sgIAATZgwwWb4/MqVK7Vv374Ur3CYEYsWLdK1a9fUq1evFPvgM888owULFujGjRuqWLGiChcurHHjxiV7DEmP3d/fX3Xr1tXUqVNtnrc720i3/9i9fPmydu/ebV12+vRp66lzaZHWfhoZGSkHBweNGDEi2QiXu38pbdeunSwWi1599VUdOXLEZg6ge9Vx974Mw7jvL6330qRJE926dUvjx4+3LktISEj23noQTZo00datW7VlyxbrsmvXrmnSpEkKDg62niL177//2mzn7OysUqVKyTAM3bx5UwkJCclO1QkICFD+/PkzdOpHUFCQKlSooG+++camn/3xxx/64Ycf1KRJk3TvM73S+p5u1KiRvLy8NHLkSOtpxUnsOSLiiy++sKnjiy++UK5cudSgQQNJt1/7hIQEm3aSNHbsWFksFmuokp73TkYlnbr3xhtvJPv86d69u4oVK2YdofOg9TwMff7NN9+Uh4eHXnzxRZ09ezbZ+sOHD2fosyMzPlNTY7FYNGnSJLVs2VKdO3e2fieRbs8bdOvWLZv2ZcuWlYODw32fixo1aujixYv3Pe3R0dFRYWFhWrx4sc0cXocOHcrwaExHR0c1atRI33//vfW0Vun2VUlnzpyp2rVrJztVMbOFh4dry5Yt2rVrl3XZhQsX7jsnVFa4ePFisvdQUvCb9Dq6u7tLUpq+w0i35/oLCgrS66+/bg3G7xQTE3PP0DI1ST8G/Pjjj9Zl165d0zfffJPufaVk8ODBGjBggAYNGmTzf3CSy5cv6/Dhw+m6giaQEzFSCsghkiZVjoiISHF99erV5e/vr6ioKGv41KZNG33++ecaOnSoypYtm+yX5Y4dO2ru3Ll6+eWXtWHDBtWqVUsJCQnav3+/5s6dq9WrV6ty5cr3rKtp06YaM2aMGjdurOeff14xMTH68ssvFRISYvOFt1KlSmrRooXGjRunf//9V9WrV9fGjRutXz7u/FXto48+0oYNG1StWjV1795dpUqV0oULF7Rjxw6tXbs22SWNU9OhQwfNmDFDq1evVvv27W1GTNSsWVO+vr7q3Lmz+vbtK4vFom+//TbDfzwNHz5cq1atUp06ddSzZ0/dunVLn3/+uUqXLm3zPDRq1EjOzs5q1qyZXnrpJcXGxmry5MkKCAjQ6dOnbfZZqVIljR8/Xu+//75CQkIUEBCQbCSUJOXKlUv/+9//1LVrV4WGhqpdu3Y6e/asPv30UwUHB2faZYijoqKUN2/eVL9cRUREaPLkyVq+fLmee+45jR8/Xs2aNVOFChXUtWtXBQUFaf/+/dq7d681rPvss89Uu3ZtVaxYUT169FDhwoV17NgxLV++3Prlu23btho4cKCaN2+uvn37Ki4uTuPHj9cTTzxx30nvk6S1n4aEhOidd97Re++9pzp16ui5556Ti4uLtm3bpvz582vkyJHWtv7+/mrcuLHmzZun3Llzpyn8K1GihIoWLaoBAwbo5MmT8vb21oIFCx7otJRmzZqpVq1aeuutt3Ts2DGVKlVKCxcuTPf8ZAsWLEhxgv/OnTvrrbfe0qxZs/T000+rb9++ypMnj7755hsdPXpUCxYssE4k3ahRIwUGBqpWrVrKly+f9u3bpy+++EJNmzaVl5eXLl26pMcee0wtW7ZU+fLl5enpqbVr12rbtm365JNPMvT4P/74Yz399NOqUaOGunXrpuvXr+vzzz+Xj4+Phg0blqF93umnn35KFiJJtydDLleuXJrf097e3ho7dqxefPFFValSRc8//7x8fX31+++/Ky4uLtP+SJJuXz4+6dTSO3l6eioyMtJ639XVVatWrVLnzp1VrVo1rVy5UsuXL9fbb79tPdW5WbNmqlevnt555x0dO3ZM5cuX1w8//KDvv/9er732mvWPvvS8dzLixo0bWrBggRo2bJhsovgkERER+vTTTxUTE/PA9TwMfb5o0aKaOXOm2rRpo5IlS6pTp04qU6aM4uPj9fPPP2vevHnq0qVLup/LzPhMvRcHBwd99913ioyMVOvWrbVixQrVr19f69evV+/evdWqVSs98cQTunXrlr799lvrj1H30rRpUzk5OWnt2rXq0aPHPdsOGzZMP/zwg2rVqqVXXnnFGqqWKVPGJtRJj/fff19r1qxR7dq11bNnTzk5OWnixIm6ceOGRo0alaF9psebb76p7777Tg0bNlSfPn3k4eGhr7/+Wo8//rguXLhg6pxF33zzjb766is1b95cRYsW1dWrVzV58mR5e3tbfwhwc3NTqVKlNGfOHD3xxBPKkyePypQpk+qcer6+vlq0aJGaNGmiChUqqEOHDqpUqZIkaceOHZo1a1aGRgs2atRIjz/+uLp166Y33nhDjo6Omjp1qvz9/ZP9GJZRH3/8sS5fvqxevXrJy8vL5geqtWvXyjAMmx9FgUdSVl/eD4A5mjVrZri6uhrXrl1LtU2XLl2MXLlyGefPnzcM4/ZlrwsWLJji5byTxMfHG//73/+M0qVLGy4uLoavr69RqVIlY/jw4cbly5et7SSleknrKVOmGMWKFTNcXFyMEiVKGNOmTUvxMrzXrl0zevXqZeTJk8fw9PQ0IiMjjQMHDhiSjI8++sim7dmzZ41evXoZBQsWNHLlymUEBgYaDRo0MCZNmpSm58swDOPWrVtGUFCQIclYsWJFsvWbN282qlevbri5uRn58+c33nzzTWP16tXJLouc0uWzlcKljjdu3GhUqlTJcHZ2NooUKWJMmDAhxedhyZIlRrly5QxXV1cjODjY+N///mdMnTo12eWJz5w5YzRt2tTw8vIyJBmhoaGGYaR86WbDMIw5c+YYTz75pOHi4mLkyZPHaN++vfHPP//YtOncubPh4eGR7LlI7bLJSc6ePWs4OTkZHTt2TLVNXFyc4e7ubjRv3ty6bNOmTUbDhg0NLy8vw8PDwyhXrpzNZbYNwzD++OMPo3nz5kbu3LkNV1dXo3jx4sbgwYNt2vzwww9GmTJlDGdnZ6N48eLGd999l+rlyx+0nxqGYUydOtX6XPr6+hqhoaHGmjVrkrWbO3euIcno0aNHqs/L3f78808jLCzM8PT0NPz8/Izu3btbL0E+bdo0a7v0vFb//vuv0bFjR8Pb29vw8fExOnbsaL38+p37TElSf0rt9tNPPxmGYRiHDx82WrZsaX2dqlataixbtsxmXxMnTjTq1q1r5M2b13BxcTGKFi1qvPHGG9bPkhs3bhhvvPGGUb58eWufKF++vPHVV1/d93lL6dLvSdauXWvUqlXLcHNzM7y9vY1mzZoZf/75Z4rP27lz5+57rLQ8L3e+/9P6nk5qW7NmTWutVatWNWbNmmVdHxoaapQuXTpZPSl9DqUk6bLmKd3u3D6pfx0+fNho1KiR4e7ubuTLl88YOnSokZCQYLPPq1evGv369TPy589v5MqVyyhWrJjx8ccfG4mJicmOf7/3TqFChYymTZsm2y40NNT6GZeSBQsWGJKMKVOmpNomOjrakGR8+umnD1yPYdi/zyc5ePCg0b17dyM4ONhwdnY2vLy8jFq1ahmff/658d9//1nbpfb5V6hQIaNz5842yx70M/Xufab0/oqLizNCQ0MNT09P45dffjGOHDlivPDCC0bRokUNV1dXI0+ePEa9evWMtWvXpul5iIiIMBo0aGCzLLXPhXXr1hlPPvmk4ezsbBQtWtT4+uuvjddff91wdXXN0OMzDMPYsWOHER4ebnh6ehru7u5GvXr1jJ9//tmmzbRp0wxJxrZt25LtM2ndnZ8J6Xk/7Ny506hTp47h4uJiPPbYY8bIkSONzz77zJBknDlzJtk+7nS/z7+Uvlek9lm0Y8cOo127dsbjjz9uuLi4GAEBAcYzzzxj/Pbbbzbtfv75Z+t3opS+M6Xk1KlTRr9+/YwnnnjCcHV1Ndzd3Y1KlSoZH3zwgc130vQ8b9u3bzeqVatmODs7G48//rgxZsyYB3otUnqNExISjHbt2hlOTk7G4sWLrcvbtGlj1K5d+76PG8jpLIbxEMySBwCp2LVrl5588kl999136b60MWBv33//vSIjI/Xjjz/aXEYaeNh16dJF8+fPz/DExIDZfvrpJz311FPav39/ile7vZ/IyEjt3bs3xfkfs6vXXntNEydOVGxsbLa7MEZOd+bMGRUuXFizZ89mpBQeecwpBeChcf369WTLxo0bJwcHB5vJxoHsYvLkySpSpIhq165t71IAIEerU6eOGjVqlKbT5e7+vvHXX39pxYoVeuq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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import seaborn as sns\n", + "\n", + "# Chunk size for 3D plot\n", + "chunk_size = 6 # Change this to your desired chunk size\n", + " \n", + "def convert_history(history):\n", + " if isinstance(history, tf.keras.callbacks.History):\n", + " return history.history\n", + " else:\n", + " return history\n", + " \n", + "def chunked_data(data, chunk_size):\n", + " return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n", + "\n", + "\n", + "try:\n", + " EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' \n", + " history = convert_history(history)\n", + "\n", + " # Calculate deltas\n", + " delta_loss = np.diff(history['loss'])\n", + " delta_accuracy = np.diff(history['accuracy'])\n", + "\n", + " try:\n", + " delta_val_loss = np.diff(history['val_loss'])\n", + " delta_val_accuracy = np.diff(history['val_accuracy'])\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss or val_accuracy for delta calculation.')\n", + "\n", + " plt.figure(figsize=(16, 10))\n", + " # Loss\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history['loss'], label='loss')\n", + " try:\n", + " plt.plot(history['val_loss'], label='val_loss', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss.')\n", + " plt.title('Model Loss')\n", + " plt.ylabel('Loss')\n", + " plt.xlabel(EPM)\n", + " plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", + " plt.grid(True)\n", + " \n", + " # Density plot for loss\n", + " plt.subplot(2, 2, 2)\n", + " plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100)\n", + " try:\n", + " plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100)\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss (density plot).')\n", + " plt.title('Density Plot for Loss')\n", + " plt.xlabel('Loss')\n", + " plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", + " plt.grid(True)\n", + " \n", + " \n", + " # Accuracy\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history['accuracy'], label='accuracy')\n", + " try:\n", + " plt.plot(history['val_accuracy'], label='val_accuracy', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_accuracy.')\n", + " plt.title('Model Accuracy')\n", + " plt.ylabel('Accuracy')\n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + " \n", + " # Density plot for accuracy\n", + " plt.subplot(2, 2, 4)\n", + " plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40)\n", + " try:\n", + " plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40)\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_accuracy (density plot).')\n", + " plt.title('Density Plot for Accuracy')\n", + " plt.xlabel('Accuracy')\n", + " plt.grid(True)\n", + "\n", + " # Delta Loss\n", + " plt.figure(figsize=(14, 8))\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(delta_loss, label='delta_loss')\n", + " try:\n", + " plt.plot(delta_val_loss, label='delta_val_loss', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load delta_val_loss.')\n", + " plt.title('Delta Model Loss')\n", + " plt.ylabel('Delta Loss')\n", + " plt.ylim(top=1.5, bottom=-1.5) \n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + " # Delta Accuracy\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(delta_accuracy, label='delta_accuracy')\n", + " try:\n", + " plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load delta_val_accuracy.')\n", + " plt.title('Delta Model Accuracy')\n", + " plt.ylabel('Delta Accuracy')\n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + "\n", + " # Calculate chunked data\n", + " chunked_loss = chunked_data(history['val_loss'], chunk_size)\n", + " chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size)\n", + "\n", + " # Clip the loss values to a maximum of max(history['val_loss'][10:])\n", + " max_loss = max(history['val_loss'][10:])\n", + " chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss)\n", + "\n", + " # Create 3D surface plots for each chunk\n", + " fig = plt.figure(figsize=(14, 8))\n", + " ax = fig.add_subplot(121, projection='3d')\n", + " X = np.arange(len(chunked_loss))\n", + " Y = np.arange(chunk_size)\n", + " X, Y = np.meshgrid(X, Y)\n", + " Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y\n", + " ax.plot_surface(X, Y, Z, cmap='viridis')\n", + " ax.set_title('3D Surface Plot of Chunked Loss')\n", + " ax.set_xlabel('Chunk Index')\n", + " ax.set_ylabel('Epoch')\n", + " ax.set_zlabel('Loss')\n", + "\n", + " ax = fig.add_subplot(122, projection='3d')\n", + " X = np.arange(len(chunked_accuracy))\n", + " Y = np.arange(chunk_size)\n", + " X, Y = np.meshgrid(X, Y)\n", + " Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y\n", + " ax.plot_surface(X, Y, Z, cmap='viridis')\n", + " ax.set_title('3D Surface Plot of Chunked Accuracy')\n", + " ax.set_xlabel('Chunk Index')\n", + " ax.set_ylabel('Epoch')\n", + " ax.set_zlabel('Accuracy')\n", + "\n", + " # Function to calculate the average of chunks\n", + " def chunked_average(values, chunk_size):\n", + " return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)]\n", + "\n", + " avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size)\n", + " avg_loss_chunks = chunked_average(history['val_loss'], chunk_size)\n", + "\n", + " # Find the chunk with the highest average accuracy\n", + " max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n", + " max_acc_value = avg_accuracy_chunks[max_acc_chunk_index]\n", + "\n", + " # Create a pile plot for accuracy\n", + " plt.figure(figsize=(10, 6))\n", + " plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy')\n", + " plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy')\n", + " plt.xlabel('Chunk')\n", + " plt.ylabel('Average Accuracy')\n", + " plt.title('Average Validation Accuracy per Chunk')\n", + " plt.legend()\n", + "\n", + " # Create a pile plot for loss\n", + " plt.figure(figsize=(10, 6))\n", + " plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss')\n", + " plt.xlabel('Chunk')\n", + " plt.ylabel('Average Loss')\n", + " plt.title('Average Validation Loss per Chunk')\n", + " plt.legend()\n", + "\n", + " # Function to calculate the average of each epoch across chunks, ignoring the first chunk\n", + " def average_across_chunks(values, chunk_size):\n", + " num_chunks = len(values) // chunk_size\n", + " avg_values = []\n", + " for epoch in range(chunk_size):\n", + " epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)]\n", + " avg_values.append(np.mean(epoch_values))\n", + " return avg_values\n", + "\n", + " # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk\n", + " avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size)\n", + " avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size)\n", + "\n", + " # Create a bar plot for average accuracy and loss of each epoch across chunks\n", + " plt.figure(figsize=(12, 6))\n", + "\n", + " # Create an index for each epoch\n", + " epoch_indices = np.arange(len(avg_accuracy_epochs))\n", + "\n", + " # Plot accuracy and loss as bars\n", + " plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6)\n", + " plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6)\n", + "\n", + " # Add labels and title\n", + " plt.xlabel('Epoch (within chunk)')\n", + " plt.ylabel('Average Value')\n", + " plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)')\n", + " plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers\n", + " plt.legend()\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "except (ValueError, NameError) as E:\n", + " print(f'\\033[91mFailed to load model history.\\nError: {E}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse model Predicting performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradcam heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### V2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_heatmap(model, img_array, conv_layer_name, pred_index):\n", + " \"\"\"\n", + " Helper function to compute the heatmap for a given convolutional layer.\n", + " \"\"\"\n", + " grad_model = tf.keras.models.Model(\n", + " [model.inputs], \n", + " [model.get_layer(conv_layer_name).output, model.output]\n", + " )\n", + "\n", + " with tf.GradientTape() as tape:\n", + " conv_layer_output, preds = grad_model(img_array)\n", + " class_channel = preds[:, pred_index]\n", + "\n", + " grads = tape.gradient(class_channel, conv_layer_output)\n", + " pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n", + "\n", + " conv_layer_output = conv_layer_output[0]\n", + " heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis]\n", + " heatmap = tf.squeeze(heatmap)\n", + " heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n", + " return heatmap\n", + "\n", + "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0):\n", + " \"\"\"\n", + " Function to compute the Grad-CAM heatmap for a specific class, given an input image.\n", + " \"\"\"\n", + " if pred_index is None:\n", + " preds = model.predict(img_array)\n", + " pred_index = tf.argmax(preds[0])\n", + "\n", + " # Compute heatmap for the last convolutional layer\n", + " heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index)\n", + " \n", + " # Apply threshold and adjust sensitivity\n", + " heatmap = np.where(heatmap > threshold, heatmap, 0)\n", + " heatmap = heatmap ** sensitivity_map\n", + "\n", + " if second_last_conv_layer_name is not None:\n", + " # Compute heatmap for the second last convolutional layer\n", + " heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)\n", + " \n", + " # Apply threshold and adjust sensitivity\n", + " heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)\n", + " heatmap_second = heatmap_second ** sensitivity_map\n", + " \n", + " # Average the two heatmaps\n", + " heatmap = (heatmap + heatmap_second) / 2.0\n", + " \n", + " return heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### V3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Main test" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 3s 3s/step\n", + "20/20 [==============================] - 2s 98ms/step\n", + "The accuracy of the model on validation data is 100.00%\n", + "The accuracy of the model on test data is 96.96%\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Predicting: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 156/156 [19:28<00:00, 7.49s/dpb]\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix, accuracy_score\n", + "from scipy.stats import binom\n", + "from tqdm import tqdm\n", + "import efficientnet.tfkeras\n", + "import cv2\n", + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "\n", + "Extra_EXT = '_T' # _T or _T_BL\n", + "prob_L = 0.9995\n", + "tick_spacing = 5\n", + "Train_data_test = False\n", + "if SAVE_TYPE == 'TF':\n", + " # Load the pre-trained model\n", + " model = load_model(f'PAI_model{Extra_EXT}')\n", + "else:\n", + " # Load the pre-trained model\n", + " model = load_model(f'PAI_model{Extra_EXT}.h5')\n", + "\n", + "# Ensure the model's input_shape matches your data\n", + "assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.'\n", + "\n", + "# Make predictions on validation data\n", + "val_predictions = model.predict(x_val)\n", + "val_predictions = np.argmax(val_predictions, axis=1)\n", + "\n", + "# Make predictions on Train data\n", + "if Train_data_test:\n", + " Train_predictions = model.predict(x_train)\n", + " Train_predictions = np.argmax(Train_predictions, axis=1)\n", + "\n", + "# Make predictions on test data\n", + "test_predictions = model.predict(x_test)\n", + "test_predictions = np.argmax(test_predictions, axis=1)\n", + "\n", + "# Convert y_val and y_test from one-hot encoder to their original form\n", + "y_val_original = np.argmax(y_val, axis=1)\n", + "y_test_original = np.argmax(y_test, axis=1)\n", + "if Train_data_test:\n", + " y_train_original = np.argmax(y_train, axis=1)\n", + "\n", + "# Calculate accuracy on validation data\n", + "val_accuracy = accuracy_score(y_val_original, val_predictions)\n", + "\n", + "# Calculate accuracy on Train data\n", + "if Train_data_test:\n", + " Train_accuracy = accuracy_score(y_val_original, Train_predictions)\n", + "\n", + "# Calculate accuracy on test data\n", + "test_accuracy = accuracy_score(y_test_original, test_predictions)\n", + "\n", + "# Print acc\n", + "if Train_data_test:\n", + " print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}')\n", + "print(f'The accuracy of the model on validation data is {val_accuracy:.2%}')\n", + "print(f'The accuracy of the model on test data is {test_accuracy:.2%}')\n", + "\n", + "# Visualize the predictions on validation data as a grid of squares\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i+1)\n", + " plt.imshow(x_val[i])\n", + " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "#Heatmap\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i+1)\n", + " img = x_val[i]\n", + " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) \n", + " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", + " heatmap = np.uint8(255 * heatmap)\n", + " # Apply Adaptive Histogram Equalization\n", + " clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8,8)) # Create CLAHE object\n", + " # heatmap = clahe.apply(heatmap)\n", + " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n", + " if RANGE_NOM:\n", + " superimposed_img = (heatmap / 255) * 0.7 + img\n", + " else:\n", + " superimposed_img = (heatmap / 255) * 0.5 + (img / 255)\n", + " #clip\n", + " superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1]\n", + " plt.imshow(superimposed_img)\n", + " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Define the list of labels\n", + "labels = ['NORMAL', 'PNEUMONIA']\n", + "\n", + "# Create a confusion matrix for validation data\n", + "val_cm = confusion_matrix(y_val_original, val_predictions)\n", + "\n", + "# Create a confusion matrix for test data\n", + "test_cm = confusion_matrix(y_test_original, test_predictions)\n", + "\n", + "# Plot the confusion matrix as a heatmap for validation data\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", + "plt.title('Confusion Matrix - Validation Data')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('True')\n", + "plt.show()\n", + "\n", + "# Plot the confusion matrix as a heatmap for test data\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", + "plt.title('Confusion Matrix - Test Data')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('True')\n", + "plt.show()\n", + "\n", + "# Define the range of test data sizes to use\n", + "data_sizes = range(1, len(x_test), 4) \n", + "# Calculate the probability of a wrong prediction based on test accuracy\n", + "prob_wrong = 1 - test_accuracy\n", + "\n", + "# Create a list to store the number of incorrect predictions for each test data size\n", + "incorrect_predictions = []\n", + "\n", + "# Generate predictions and track incorrect predictions for each data size\n", + "for size in tqdm(data_sizes, desc='Predicting', unit='dpb'):\n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " # Randomly select a subset of test data\n", + " indices = np.random.choice(len(x_test), size, replace=False)\n", + " x_test_subset = x_test[indices]\n", + " y_test_subset = y_test[indices]\n", + "\n", + " # Make predictions on the subset of test data\n", + " test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False)\n", + " test_predictions = np.argmax(test_predictions, axis=1)\n", + " y_test_original_subset = np.argmax(y_test_subset, axis=1)\n", + "\n", + " # Calculate the number of incorrect predictions\n", + " incorrect_preds = np.sum(test_predictions != y_test_original_subset)\n", + " incorrect_predictions.append(incorrect_preds)\n", + " \n", + "# Plot the number of incorrect predictions vs. the number of data points\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_sizes, incorrect_predictions)\n", + "plt.xlabel('Number of Data Points')\n", + "plt.ylabel('Number of Incorrect Predictions')\n", + "# Add gridlines for the x and y axes\n", + "plt.grid(True)\n", + "\n", + "# Change the tick spacing for the x and y axes\n", + "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50))\n", + "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", + "\n", + "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", + "plt.show()\n", + "\n", + "# Define the range of test data sizes to use\n", + "data_sizes = range(1, len(x_test), 1) \n", + "\n", + "# Calculate the probability of a wrong prediction based on test accuracy\n", + "prob_wrong = 1 - test_accuracy\n", + "\n", + "# Create a list to store the probability of getting at least one wrong answer for each test data size\n", + "probabilities = []\n", + "\n", + "# Calculate the probability of getting at least one wrong answer for each data size\n", + "for size in data_sizes:\n", + " # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", + " cdf = binom.cdf(0, size, prob_wrong)\n", + " # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", + " prob = 1 - cdf\n", + " probabilities.append(prob)\n", + "\n", + "# Find the index of the first data point that has a probability greater than prob_L%\n", + "index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", + "\n", + "# Limit the x-axis to the first data point that has a probability greater than prob_L%\n", + "data_sizes = data_sizes[:index+1]\n", + "probabilities = probabilities[:index+1]\n", + "\n", + "# Plot the probability vs. the number of data points\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_sizes, probabilities)\n", + "plt.xlabel('Number of Data Points')\n", + "plt.ylabel('Probability')\n", + "\n", + "# Add gridlines for the x and y axes\n", + "plt.grid(True)\n", + "\n", + "# Change the tick spacing for the x and y axes\n", + "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 10))\n", + "plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100))\n", + "\n", + "plt.ylim(top=1.01)\n", + "\n", + "plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/backup/V5/TRAIN_LOG_ANSI.ans b/backup/V5/TRAIN_LOG_ANSI.ans index 89cc95c..fd415a6 100644 --- a/backup/V5/TRAIN_LOG_ANSI.ans +++ b/backup/V5/TRAIN_LOG_ANSI.ans @@ -1,4779 +1,4779 @@ -Training the model... - -Setup Verbose: -Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... -Use_extended_tensorboard [False]. -Debug_OUTPUT_DPS [True]. -OneCycleLr_UFTS [False]. -Setup Verbose END. - -Epoch: 1/486 (TSEC: 0) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Fitting ImageDataGenerator... -- ImageDataGenerator fit done. -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 -Epoch 2/6 -128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 -Epoch 3/6 -128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 -Epoch 4/6 -128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 -Epoch 5/6 -128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 -Epoch 6/6 -128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... -Model Test acc: 0.8974 -Model Test loss: 2.4019 -Improved model accuracy from 0 to 0.8974359035491943. Saving model. -Saving full model H5 format... -Improved model loss from inf to 2.4019267559051514. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 676.74 sec -Time taken for epoch(SUBo): 271.12 sec -Time taken for epoch(OTHERo): 405.62 sec -<---------------------------------------|Epoch [1] END|---------------------------------------> - -Epoch: 2/486 (TSEC: 6) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 7/12 -128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 -Epoch 8/12 -128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 -Epoch 9/12 -128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 -Epoch 10/12 -128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 -Epoch 11/12 -128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 -Epoch 12/12 -128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... -Model Test acc: 0.9407 -Model Test loss: 0.5406 -Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. -Saving full model H5 format... -Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.91 sec -Time taken for epoch(SUBo): 257.59 sec -Time taken for epoch(OTHERo): 68.33 sec -<---------------------------------------|Epoch [2] END|---------------------------------------> - -Epoch: 3/486 (TSEC: 12) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 13/18 -128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 -Epoch 14/18 -128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 -Epoch 15/18 -128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 -Epoch 16/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 -Epoch 17/18 -128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 -Epoch 18/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... -Model Test acc: 0.9423 -Model Test loss: 0.4656 -Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. -Saving full model H5 format... -Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 324.58 sec -Time taken for epoch(SUBo): 255.82 sec -Time taken for epoch(OTHERo): 68.76 sec -<---------------------------------------|Epoch [3] END|---------------------------------------> - -Epoch: 4/486 (TSEC: 18) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 19/24 -128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 -Epoch 20/24 -128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 -Epoch 21/24 -128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 -Epoch 22/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 -Epoch 23/24 -128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 -Epoch 24/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... -Model Test acc: 0.9343 -Model Test loss: 0.4089 -Model accuracy did not improve from 0.942307710647583. Not saving model. -Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 323.62 sec -Time taken for epoch(SUBo): 256.60 sec -Time taken for epoch(OTHERo): 67.02 sec -<---------------------------------------|Epoch [4] END|---------------------------------------> - -Epoch: 5/486 (TSEC: 24) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 25/30 -128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 -Epoch 26/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 -Epoch 27/30 -128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 -Epoch 28/30 -128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 -Epoch 29/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 -Epoch 30/30 -128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.2338 -Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. -Saving full model H5 format... -Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.89 sec -Time taken for epoch(SUBo): 258.52 sec -Time taken for epoch(OTHERo): 67.37 sec -<---------------------------------------|Epoch [5] END|---------------------------------------> - -Epoch: 6/486 (TSEC: 30) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 31/36 -128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 -Epoch 32/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 -Epoch 33/36 -128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 -Epoch 34/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 -Epoch 35/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 -Epoch 36/36 -128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2253 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 321.73 sec -Time taken for epoch(SUBo): 256.17 sec -Time taken for epoch(OTHERo): 65.57 sec -<---------------------------------------|Epoch [6] END|---------------------------------------> - -Epoch: 7/486 (TSEC: 36) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 37/42 -128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 -Epoch 38/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 -Epoch 39/42 -128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 -Epoch 40/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 -Epoch 41/42 -128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 -Epoch 42/42 -128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2070 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 326.03 sec -Time taken for epoch(SUBo): 259.27 sec -Time taken for epoch(OTHERo): 66.76 sec -<---------------------------------------|Epoch [7] END|---------------------------------------> - -Epoch: 8/486 (TSEC: 42) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 43/48 -128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 -Epoch 44/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 -Epoch 45/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 -Epoch 46/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 -Epoch 47/48 -128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 -Epoch 48/48 -128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2199 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Model loss did not improve from 0.20697814226150513. Not saving model. -Time taken for epoch(FULL): 322.67 sec -Time taken for epoch(SUBo): 256.59 sec -Time taken for epoch(OTHERo): 66.08 sec -<---------------------------------------|Epoch [8] END|---------------------------------------> - -Epoch: 9/486 (TSEC: 48) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 49/54 -128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 -Epoch 50/54 -128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 -Epoch 51/54 -128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 -Epoch 52/54 -128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 -Epoch 53/54 -128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 -Epoch 54/54 -128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.1662 -Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. -Saving full model H5 format... -Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 327.90 sec -Time taken for epoch(SUBo): 257.53 sec -Time taken for epoch(OTHERo): 70.37 sec -<---------------------------------------|Epoch [9] END|---------------------------------------> - -Epoch: 10/486 (TSEC: 54) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 55/60 -128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 -Epoch 56/60 -128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 -Epoch 57/60 -128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 -Epoch 58/60 -128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 -Epoch 59/60 -128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 -Epoch 60/60 -128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2059 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 327.86 sec -Time taken for epoch(SUBo): 259.66 sec -Time taken for epoch(OTHERo): 68.20 sec -<---------------------------------------|Epoch [10] END|---------------------------------------> - -Epoch: 11/486 (TSEC: 60) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 61/66 -128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 -Epoch 62/66 -128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 -Epoch 63/66 -128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 -Epoch 64/66 -128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 -Epoch 65/66 -128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 -Epoch 66/66 -128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2938 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 257.49 sec -Time taken for epoch(OTHERo): 69.06 sec -<---------------------------------------|Epoch [11] END|---------------------------------------> - -Epoch: 12/486 (TSEC: 66) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 67/72 -128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 -Epoch 68/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 -Epoch 69/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 -Epoch 70/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 -Epoch 71/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 -Epoch 72/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... -Model Test acc: 0.9631 -Model Test loss: 0.2115 -Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 324.68 sec -Time taken for epoch(SUBo): 253.65 sec -Time taken for epoch(OTHERo): 71.03 sec -<---------------------------------------|Epoch [12] END|---------------------------------------> - -Epoch: 13/486 (TSEC: 72) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 73/78 -128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 -Epoch 74/78 -128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 -Epoch 75/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 -Epoch 76/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 -Epoch 77/78 -128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 -Epoch 78/78 -128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.1545 -Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. -Saving full model H5 format... -Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.97 sec -Time taken for epoch(SUBo): 251.55 sec -Time taken for epoch(OTHERo): 74.42 sec -<---------------------------------------|Epoch [13] END|---------------------------------------> - -Epoch: 14/486 (TSEC: 78) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 79/84 -128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 -Epoch 80/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 -Epoch 81/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 -Epoch 82/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 -Epoch 83/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 -Epoch 84/84 -128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1780 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 71.59 sec -<---------------------------------------|Epoch [14] END|---------------------------------------> - -Epoch: 15/486 (TSEC: 84) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 85/90 -128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 -Epoch 86/90 -128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 -Epoch 87/90 -128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 -Epoch 88/90 -128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 -Epoch 89/90 -128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 -Epoch 90/90 -128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... -Model Test acc: 0.9615 -Model Test loss: 0.2233 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.17 sec -Time taken for epoch(SUBo): 252.81 sec -Time taken for epoch(OTHERo): 70.36 sec -<---------------------------------------|Epoch [15] END|---------------------------------------> - -Epoch: 16/486 (TSEC: 90) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 91/96 -128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 -Epoch 92/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 -Epoch 93/96 -128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 -Epoch 94/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 -Epoch 95/96 -128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 -Epoch 96/96 -128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1648 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 319.96 sec -Time taken for epoch(SUBo): 249.52 sec -Time taken for epoch(OTHERo): 70.43 sec -<---------------------------------------|Epoch [16] END|---------------------------------------> - -Epoch: 17/486 (TSEC: 96) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 97/102 -128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 -Epoch 98/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 -Epoch 99/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 -Epoch 100/102 -128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 -Epoch 101/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 -Epoch 102/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1764 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.46 sec -Time taken for epoch(SUBo): 253.14 sec -Time taken for epoch(OTHERo): 67.31 sec -<---------------------------------------|Epoch [17] END|---------------------------------------> - -Epoch: 18/486 (TSEC: 102) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 103/108 -128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 -Epoch 104/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 -Epoch 105/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 -Epoch 106/108 -128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 -Epoch 107/108 -128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 -Epoch 108/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2677 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.64 sec -Time taken for epoch(SUBo): 253.71 sec -Time taken for epoch(OTHERo): 70.93 sec -<---------------------------------------|Epoch [18] END|---------------------------------------> - -Epoch: 19/486 (TSEC: 108) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 109/114 -128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 -Epoch 110/114 -128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 -Epoch 111/114 -128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 -Epoch 112/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 -Epoch 113/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 -Epoch 114/114 -128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2055 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.02 sec -Time taken for epoch(SUBo): 253.02 sec -Time taken for epoch(OTHERo): 69.00 sec -<---------------------------------------|Epoch [19] END|---------------------------------------> - -Epoch: 20/486 (TSEC: 114) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 115/120 -128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 -Epoch 116/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 -Epoch 117/120 -128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 -Epoch 118/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 -Epoch 119/120 -128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 -Epoch 120/120 -128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... -Model Test acc: 0.9439 -Model Test loss: 0.3367 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 252.69 sec -Time taken for epoch(OTHERo): 70.57 sec -<---------------------------------------|Epoch [20] END|---------------------------------------> - -Epoch: 21/486 (TSEC: 120) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 121/126 -128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 -Epoch 122/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 -Epoch 123/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 -Epoch 124/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 -Epoch 125/126 -128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 -Epoch 126/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... -Model Test acc: 0.9583 -Model Test loss: 0.2373 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.10 sec -Time taken for epoch(SUBo): 252.57 sec -Time taken for epoch(OTHERo): 68.53 sec -<---------------------------------------|Epoch [21] END|---------------------------------------> - -Epoch: 22/486 (TSEC: 126) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 127/132 -128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 -Epoch 128/132 -128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 -Epoch 129/132 -128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 -Epoch 130/132 -128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 -Epoch 131/132 -128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 -Epoch 132/132 -128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2035 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.92 sec -Time taken for epoch(SUBo): 252.61 sec -Time taken for epoch(OTHERo): 69.31 sec -<---------------------------------------|Epoch [22] END|---------------------------------------> - -Epoch: 23/486 (TSEC: 132) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 133/138 -128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 -Epoch 134/138 -128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 -Epoch 135/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 -Epoch 136/138 -128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 -Epoch 137/138 -128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 -Epoch 138/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1983 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 251.30 sec -Time taken for epoch(OTHERo): 71.96 sec -<---------------------------------------|Epoch [23] END|---------------------------------------> - -Epoch: 24/486 (TSEC: 138) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01094]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 139/144 -128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 -Epoch 140/144 -128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 -Epoch 141/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 -Epoch 142/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 -Epoch 143/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 144/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... -Model Test acc: 0.9567 -Model Test loss: 0.2415 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.37 sec -Time taken for epoch(SUBo): 251.97 sec -Time taken for epoch(OTHERo): 72.40 sec -<---------------------------------------|Epoch [24] END|---------------------------------------> - -Epoch: 25/486 (TSEC: 144) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01088]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 145/150 -128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 -Epoch 146/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 -Epoch 147/150 -128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 -Epoch 148/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 -Epoch 149/150 -128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 -Epoch 150/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2961 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.24 sec -Time taken for epoch(SUBo): 250.77 sec -Time taken for epoch(OTHERo): 69.47 sec -<---------------------------------------|Epoch [25] END|---------------------------------------> - -Epoch: 26/486 (TSEC: 150) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01082]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 151/156 -128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 -Epoch 152/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 -Epoch 153/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 -Epoch 154/156 -128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 -Epoch 155/156 -128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 -Epoch 156/156 -128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2054 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.50 sec -Time taken for epoch(SUBo): 253.89 sec -Time taken for epoch(OTHERo): 68.61 sec -<---------------------------------------|Epoch [26] END|---------------------------------------> - -Epoch: 27/486 (TSEC: 156) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01076]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 157/162 -128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 -Epoch 158/162 -128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 -Epoch 159/162 -128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 -Epoch 160/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 -Epoch 161/162 -128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 -Epoch 162/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2812 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.25 sec -Time taken for epoch(SUBo): 253.57 sec -Time taken for epoch(OTHERo): 69.69 sec -<---------------------------------------|Epoch [27] END|---------------------------------------> - -Epoch: 28/486 (TSEC: 162) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0107]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 163/168 -128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 -Epoch 164/168 -128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 165/168 -128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 -Epoch 166/168 -128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 -Epoch 167/168 -128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 -Epoch 168/168 -128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3527 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.79 sec -Time taken for epoch(SUBo): 252.26 sec -Time taken for epoch(OTHERo): 68.52 sec -<---------------------------------------|Epoch [28] END|---------------------------------------> - -Epoch: 29/486 (TSEC: 168) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01064]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 169/174 -128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 -Epoch 170/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 -Epoch 171/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 -Epoch 172/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 -Epoch 173/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 -Epoch 174/174 -128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3280 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.85 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 70.35 sec -<---------------------------------------|Epoch [29] END|---------------------------------------> - -Epoch: 30/486 (TSEC: 174) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01058]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 175/180 -128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 -Epoch 176/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 -Epoch 177/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 -Epoch 178/180 -128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 -Epoch 179/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 -Epoch 180/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2430 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.08 sec -Time taken for epoch(SUBo): 252.22 sec -Time taken for epoch(OTHERo): 69.87 sec -<---------------------------------------|Epoch [30] END|---------------------------------------> - -Epoch: 31/486 (TSEC: 180) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01052]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 181/186 -128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 -Epoch 182/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 -Epoch 183/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 -Epoch 184/186 -128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 -Epoch 185/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 -Epoch 186/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9022 -Model Test loss: 0.5451 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.75 sec -Time taken for epoch(SUBo): 253.25 sec -Time taken for epoch(OTHERo): 69.50 sec -<---------------------------------------|Epoch [31] END|---------------------------------------> - -Epoch: 32/486 (TSEC: 186) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 -Setting training OneCycleLr::maxlr to [0.01046]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 187/192 -128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 -Epoch 188/192 -128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 -Epoch 189/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 -Epoch 190/192 -128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 -Epoch 191/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 -Epoch 192/192 -128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.2548 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 338.08 sec -Time taken for epoch(SUBo): 252.96 sec -Time taken for epoch(OTHERo): 85.12 sec -<---------------------------------------|Epoch [32] END|---------------------------------------> - -Epoch: 33/486 (TSEC: 192) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0104]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 193/198 -128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 -Epoch 194/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 -Epoch 195/198 -128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 -Epoch 196/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 -Epoch 197/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 -Epoch 198/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... -Model Test acc: 0.9663 -Model Test loss: 0.1816 -Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 328.41 sec -Time taken for epoch(SUBo): 253.11 sec -Time taken for epoch(OTHERo): 75.30 sec -<---------------------------------------|Epoch [33] END|---------------------------------------> - -Epoch: 34/486 (TSEC: 198) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01034]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 199/204 -128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 -Epoch 200/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 -Epoch 201/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 -Epoch 202/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 -Epoch 203/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 -Epoch 204/204 -128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2118 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 327.41 sec -Time taken for epoch(SUBo): 254.14 sec -Time taken for epoch(OTHERo): 73.27 sec -<---------------------------------------|Epoch [34] END|---------------------------------------> - -Epoch: 35/486 (TSEC: 204) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01028]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 205/210 -128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 -Epoch 206/210 -128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 -Epoch 207/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 -Epoch 208/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 -Epoch 209/210 -128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 -Epoch 210/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... -Model Test acc: 0.9551 -Model Test loss: 0.1544 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 329.96 sec -Time taken for epoch(SUBo): 252.88 sec -Time taken for epoch(OTHERo): 77.08 sec -<---------------------------------------|Epoch [35] END|---------------------------------------> - -Epoch: 36/486 (TSEC: 210) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01022]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 211/216 -128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 -Epoch 212/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 -Epoch 213/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 -Epoch 214/216 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 -Epoch 215/216 -128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 -Epoch 216/216 -128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3144 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 328.83 sec -Time taken for epoch(SUBo): 253.49 sec -Time taken for epoch(OTHERo): 75.34 sec -<---------------------------------------|Epoch [36] END|---------------------------------------> - -Epoch: 37/486 (TSEC: 216) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01016]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 217/222 -128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 -Epoch 218/222 -128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 -Epoch 219/222 -128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 -Epoch 220/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 -Epoch 221/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 -Epoch 222/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.4185 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.53 sec -Time taken for epoch(SUBo): 253.85 sec -Time taken for epoch(OTHERo): 73.68 sec -<---------------------------------------|Epoch [37] END|---------------------------------------> - -Epoch: 38/486 (TSEC: 222) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0101]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 223/228 -128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 -Epoch 224/228 -128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 -Epoch 225/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 -Epoch 226/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 -Epoch 227/228 -128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 -Epoch 228/228 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2645 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 252.83 sec -Time taken for epoch(OTHERo): 72.28 sec -<---------------------------------------|Epoch [38] END|---------------------------------------> - -Epoch: 39/486 (TSEC: 228) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01004]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 229/234 -128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 -Epoch 230/234 -128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 -Epoch 231/234 -128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 -Epoch 232/234 -128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 -Epoch 233/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 -Epoch 234/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.3982 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.39 sec -Time taken for epoch(SUBo): 254.95 sec -Time taken for epoch(OTHERo): 71.43 sec -<---------------------------------------|Epoch [39] END|---------------------------------------> - -Epoch: 40/486 (TSEC: 234) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00998]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 235/240 -128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 -Epoch 236/240 -128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 -Epoch 237/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 -Epoch 238/240 -128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 -Epoch 239/240 -128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 -Epoch 240/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2006 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.76 sec -Time taken for epoch(SUBo): 253.96 sec -Time taken for epoch(OTHERo): 71.80 sec -<---------------------------------------|Epoch [40] END|---------------------------------------> - -Epoch: 41/486 (TSEC: 240) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00992]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 241/246 -128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 -Epoch 242/246 -128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 -Epoch 243/246 -128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 -Epoch 244/246 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 -Epoch 245/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 -Epoch 246/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2011 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.07 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.68 sec -<---------------------------------------|Epoch [41] END|---------------------------------------> - -Epoch: 42/486 (TSEC: 246) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00986]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 247/252 -128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 -Epoch 248/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 -Epoch 249/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 -Epoch 250/252 -128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 -Epoch 251/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 -Epoch 252/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2136 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 253.59 sec -Time taken for epoch(OTHERo): 72.54 sec -<---------------------------------------|Epoch [42] END|---------------------------------------> - -Epoch: 43/486 (TSEC: 252) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0098]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 253/258 -128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 -Epoch 254/258 -128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 -Epoch 255/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 -Epoch 256/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 -Epoch 257/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 -Epoch 258/258 -128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2042 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.11 sec -Time taken for epoch(SUBo): 254.27 sec -Time taken for epoch(OTHERo): 72.84 sec -<---------------------------------------|Epoch [43] END|---------------------------------------> - -Epoch: 44/486 (TSEC: 258) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00974]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 259/264 -128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 -Epoch 260/264 -128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 -Epoch 261/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 -Epoch 262/264 -128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 -Epoch 263/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 -Epoch 264/264 -128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.3485 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 252.93 sec -Time taken for epoch(OTHERo): 73.19 sec -<---------------------------------------|Epoch [44] END|---------------------------------------> - -Epoch: 45/486 (TSEC: 264) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00968]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 265/270 -128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 -Epoch 266/270 -128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 -Epoch 267/270 -128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 -Epoch 268/270 -128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 -Epoch 269/270 -128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 -Epoch 270/270 -128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2324 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.17 sec -<---------------------------------------|Epoch [45] END|---------------------------------------> - -Epoch: 46/486 (TSEC: 270) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00962]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 271/276 -128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 -Epoch 272/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 -Epoch 273/276 -128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 -Epoch 274/276 -128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 -Epoch 275/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 -Epoch 276/276 -128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3108 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.76 sec -Time taken for epoch(SUBo): 254.60 sec -Time taken for epoch(OTHERo): 72.16 sec -<---------------------------------------|Epoch [46] END|---------------------------------------> - -Epoch: 47/486 (TSEC: 276) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00956]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 277/282 -128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 -Epoch 278/282 -128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 -Epoch 279/282 -128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 -Epoch 280/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 -Epoch 281/282 -128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 -Epoch 282/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3641 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.51 sec -Time taken for epoch(SUBo): 253.63 sec -Time taken for epoch(OTHERo): 72.88 sec -<---------------------------------------|Epoch [47] END|---------------------------------------> - -Epoch: 48/486 (TSEC: 282) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0095]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 283/288 -128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 -Epoch 284/288 -128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 -Epoch 285/288 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 -Epoch 286/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 -Epoch 287/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 -Epoch 288/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8958 -Model Test loss: 0.7104 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.17 sec -Time taken for epoch(SUBo): 255.62 sec -Time taken for epoch(OTHERo): 74.55 sec -<---------------------------------------|Epoch [48] END|---------------------------------------> - -Epoch: 49/486 (TSEC: 288) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00944]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 289/294 -128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 -Epoch 290/294 -128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 -Epoch 291/294 -128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 -Epoch 292/294 -128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 -Epoch 293/294 -128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 -Epoch 294/294 -128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9183 -Model Test loss: 0.5959 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.11 sec -Time taken for epoch(SUBo): 254.80 sec -Time taken for epoch(OTHERo): 75.32 sec -<---------------------------------------|Epoch [49] END|---------------------------------------> - -Epoch: 50/486 (TSEC: 294) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00938]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 295/300 -128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 -Epoch 296/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 -Epoch 297/300 -128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 -Epoch 298/300 -128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 -Epoch 299/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 -Epoch 300/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4308 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 331.10 sec -Time taken for epoch(SUBo): 255.03 sec -Time taken for epoch(OTHERo): 76.07 sec -<---------------------------------------|Epoch [50] END|---------------------------------------> - -Epoch: 51/486 (TSEC: 300) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00932]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 301/306 -128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 -Epoch 302/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 -Epoch 303/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 -Epoch 304/306 -128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 -Epoch 305/306 -128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 -Epoch 306/306 -128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8974 -Model Test loss: 0.7052 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 362.92 sec -Time taken for epoch(SUBo): 286.09 sec -Time taken for epoch(OTHERo): 76.83 sec -<---------------------------------------|Epoch [51] END|---------------------------------------> - -Epoch: 52/486 (TSEC: 306) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00926]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 307/312 -128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 -Epoch 308/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 -Epoch 309/312 -128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 -Epoch 310/312 -128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 -Epoch 311/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 -Epoch 312/312 -128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9135 -Model Test loss: 0.5680 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.05 sec -Time taken for epoch(SUBo): 284.61 sec -Time taken for epoch(OTHERo): 95.44 sec -<---------------------------------------|Epoch [52] END|---------------------------------------> - -Epoch: 53/486 (TSEC: 312) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0092]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 313/318 -128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 -Epoch 314/318 -128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 -Epoch 315/318 -128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 -Epoch 316/318 -128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 -Epoch 317/318 -128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 -Epoch 318/318 -128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4632 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.68 sec -Time taken for epoch(SUBo): 286.71 sec -Time taken for epoch(OTHERo): 93.97 sec -<---------------------------------------|Epoch [53] END|---------------------------------------> - -Epoch: 54/486 (TSEC: 318) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00914]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 319/324 -128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 -Epoch 320/324 -128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 -Epoch 321/324 -128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 -Epoch 322/324 -128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 -Epoch 323/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 -Epoch 324/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5101 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 389.45 sec -Time taken for epoch(SUBo): 287.64 sec -Time taken for epoch(OTHERo): 101.81 sec -<---------------------------------------|Epoch [54] END|---------------------------------------> - -Epoch: 55/486 (TSEC: 324) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00908]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 325/330 -128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 -Epoch 326/330 -128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 -Epoch 327/330 -128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 -Epoch 328/330 -128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 -Epoch 329/330 -128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 -Epoch 330/330 -128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3231 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.47 sec -Time taken for epoch(SUBo): 281.24 sec -Time taken for epoch(OTHERo): 99.23 sec -<---------------------------------------|Epoch [55] END|---------------------------------------> - -Epoch: 56/486 (TSEC: 330) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00902]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 331/336 -128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 -Epoch 332/336 -128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 -Epoch 333/336 -128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 -Epoch 334/336 -128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 -Epoch 335/336 -128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 -Epoch 336/336 -128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3479 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 392.43 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 103.65 sec -<---------------------------------------|Epoch [56] END|---------------------------------------> - -Epoch: 57/486 (TSEC: 336) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00896]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 337/342 -128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 -Epoch 338/342 -128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 -Epoch 339/342 -128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 -Epoch 340/342 -128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 -Epoch 341/342 -128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 -Epoch 342/342 -128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4083 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 387.98 sec -Time taken for epoch(SUBo): 285.68 sec -Time taken for epoch(OTHERo): 102.30 sec -<---------------------------------------|Epoch [57] END|---------------------------------------> - -Epoch: 58/486 (TSEC: 342) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0089]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 343/348 -128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 -Epoch 344/348 -128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 -Epoch 345/348 -128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 -Epoch 346/348 -128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 -Epoch 347/348 -128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 -Epoch 348/348 -128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3776 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 350.83 sec -Time taken for epoch(SUBo): 268.69 sec -Time taken for epoch(OTHERo): 82.14 sec -<---------------------------------------|Epoch [58] END|---------------------------------------> - -Epoch: 59/486 (TSEC: 348) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00884]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 349/354 -128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 -Epoch 350/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 -Epoch 351/354 -128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 -Epoch 352/354 -128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 -Epoch 353/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 -Epoch 354/354 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4066 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 353.60 sec -Time taken for epoch(SUBo): 258.60 sec -Time taken for epoch(OTHERo): 95.01 sec -<---------------------------------------|Epoch [59] END|---------------------------------------> - -Epoch: 60/486 (TSEC: 354) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00878]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 355/360 -128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 -Epoch 356/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 -Epoch 357/360 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 -Epoch 358/360 -128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 -Epoch 359/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 -Epoch 360/360 -128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4142 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 346.87 sec -Time taken for epoch(SUBo): 257.34 sec -Time taken for epoch(OTHERo): 89.53 sec -<---------------------------------------|Epoch [60] END|---------------------------------------> - -Epoch: 61/486 (TSEC: 360) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00872]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 361/366 -128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 -Epoch 362/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 -Epoch 363/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 -Epoch 364/366 -128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 -Epoch 365/366 -128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 -Epoch 366/366 -128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.7276 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.90 sec -Time taken for epoch(SUBo): 264.37 sec -Time taken for epoch(OTHERo): 77.53 sec -<---------------------------------------|Epoch [61] END|---------------------------------------> - -Epoch: 62/486 (TSEC: 366) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00866]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 367/372 -128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 -Epoch 368/372 -128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 -Epoch 369/372 -128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 -Epoch 370/372 -128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 -Epoch 371/372 -128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 -Epoch 372/372 -128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.2727 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 344.05 sec -Time taken for epoch(SUBo): 266.44 sec -Time taken for epoch(OTHERo): 77.61 sec -<---------------------------------------|Epoch [62] END|---------------------------------------> - -Epoch: 63/486 (TSEC: 372) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0086]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 373/378 -128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 -Epoch 374/378 -128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 -Epoch 375/378 -128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 -Epoch 376/378 -128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 -Epoch 377/378 -128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 -Epoch 378/378 -128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4211 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 334.11 sec -Time taken for epoch(SUBo): 258.37 sec -Time taken for epoch(OTHERo): 75.73 sec -<---------------------------------------|Epoch [63] END|---------------------------------------> - -Epoch: 64/486 (TSEC: 378) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 -Setting training OneCycleLr::maxlr to [0.00854]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 379/384 -128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 -Epoch 380/384 -128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 -Epoch 381/384 -128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 -Epoch 382/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 -Epoch 383/384 -128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 -Epoch 384/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3692 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 356.90 sec -Time taken for epoch(SUBo): 259.87 sec -Time taken for epoch(OTHERo): 97.03 sec -<---------------------------------------|Epoch [64] END|---------------------------------------> - -Epoch: 65/486 (TSEC: 384) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00848]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 385/390 -128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 -Epoch 386/390 -128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 -Epoch 387/390 -128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 -Epoch 388/390 -128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 -Epoch 389/390 -128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 -Epoch 390/390 -128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1823 -Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.22 sec -Time taken for epoch(SUBo): 257.30 sec -Time taken for epoch(OTHERo): 83.93 sec -<---------------------------------------|Epoch [65] END|---------------------------------------> - -Epoch: 66/486 (TSEC: 390) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00842]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 391/396 -128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 -Epoch 392/396 -128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 -Epoch 393/396 -128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 -Epoch 394/396 -128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 -Epoch 395/396 -128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 -Epoch 396/396 -128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9551 -Model Test loss: 0.2462 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 340.59 sec -Time taken for epoch(SUBo): 259.99 sec -Time taken for epoch(OTHERo): 80.59 sec -<---------------------------------------|Epoch [66] END|---------------------------------------> - -Epoch: 67/486 (TSEC: 396) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00836]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 397/402 -128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 -Epoch 398/402 -128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 -Epoch 399/402 -128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 -Epoch 400/402 -128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 -Epoch 401/402 -128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 -Epoch 402/402 -128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2736 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 348.32 sec -Time taken for epoch(SUBo): 267.55 sec -Time taken for epoch(OTHERo): 80.77 sec -<---------------------------------------|Epoch [67] END|---------------------------------------> - -Epoch: 68/486 (TSEC: 402) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0083]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 403/408 -128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 -Epoch 404/408 -128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 -Epoch 405/408 -128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 -Epoch 406/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 -Epoch 407/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 -Epoch 408/408 -128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.2983 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 374.47 sec -Time taken for epoch(SUBo): 276.39 sec -Time taken for epoch(OTHERo): 98.08 sec -<---------------------------------------|Epoch [68] END|---------------------------------------> - -Epoch: 69/486 (TSEC: 408) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00824]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 409/414 -128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 -Epoch 410/414 -128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 -Epoch 411/414 -128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 -Epoch 412/414 -128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 -Epoch 413/414 -128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 -Epoch 414/414 -128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3477 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 343.05 sec -Time taken for epoch(SUBo): 260.05 sec -Time taken for epoch(OTHERo): 83.00 sec -<---------------------------------------|Epoch [69] END|---------------------------------------> - -Epoch: 70/486 (TSEC: 414) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00818]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 415/420 -128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 -Epoch 416/420 -128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 -Epoch 417/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 -Epoch 418/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 -Epoch 419/420 -128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 -Epoch 420/420 -128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9167 -Model Test loss: 0.4541 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 342.13 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 79.85 sec -<---------------------------------------|Epoch [70] END|---------------------------------------> - -Epoch: 71/486 (TSEC: 420) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00812]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 421/426 -128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 -Epoch 422/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 -Epoch 423/426 -128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 -Epoch 424/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 -Epoch 425/426 -128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 -Epoch 426/426 -128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2110 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.68 sec -Time taken for epoch(SUBo): 260.39 sec -Time taken for epoch(OTHERo): 81.29 sec -<---------------------------------------|Epoch [71] END|---------------------------------------> - -Epoch: 72/486 (TSEC: 426) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00806]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 427/432 -128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 -Epoch 428/432 -128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 -Epoch 429/432 -128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 -Epoch 430/432 -128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 -Epoch 431/432 -128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 -Epoch 432/432 -128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... -Model Test acc: 0.9615 -Model Test loss: 0.1528 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 353.28 sec -Time taken for epoch(SUBo): 265.48 sec -Time taken for epoch(OTHERo): 87.80 sec -<---------------------------------------|Epoch [72] END|---------------------------------------> - -Epoch: 73/486 (TSEC: 432) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.008]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 433/438 -128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 -Epoch 434/438 -128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 -Epoch 435/438 -128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 -Epoch 436/438 -128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 -Epoch 437/438 -128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 -Epoch 438/438 -128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1676 -Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.79 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 111.40 sec -<---------------------------------------|Epoch [73] END|---------------------------------------> - -Epoch: 74/486 (TSEC: 438) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00794]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 439/444 -128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 -Epoch 440/444 -128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 -Epoch 441/444 -128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 -Epoch 442/444 -128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 -Epoch 443/444 -128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 -Epoch 444/444 -128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.3061 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.10 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 108.32 sec -<---------------------------------------|Epoch [74] END|---------------------------------------> - -Epoch: 75/486 (TSEC: 444) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00788]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 445/450 -128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 -Epoch 446/450 -128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 -Epoch 447/450 -128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 -Epoch 448/450 -128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 -Epoch 449/450 -128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 -Epoch 450/450 -128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.2149 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.95 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 108.55 sec -<---------------------------------------|Epoch [75] END|---------------------------------------> - -Epoch: 76/486 (TSEC: 450) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00782]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 451/456 -128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 -Epoch 452/456 -128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 -Epoch 453/456 -128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 -Epoch 454/456 -128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 -Epoch 455/456 -128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 -Epoch 456/456 -128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1664 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.35 sec -Time taken for epoch(SUBo): 290.41 sec -Time taken for epoch(OTHERo): 109.94 sec -<---------------------------------------|Epoch [76] END|---------------------------------------> - -Epoch: 77/486 (TSEC: 456) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00776]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 457/462 -128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 -Epoch 458/462 -128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 -Epoch 459/462 -128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 -Epoch 460/462 -128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 -Epoch 461/462 -128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 -Epoch 462/462 -128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1549 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 394.70 sec -Time taken for epoch(SUBo): 289.87 sec -Time taken for epoch(OTHERo): 104.83 sec -<---------------------------------------|Epoch [77] END|---------------------------------------> - -Epoch: 78/486 (TSEC: 462) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0077]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 463/468 -128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 -Epoch 464/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 -Epoch 465/468 -128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 -Epoch 466/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 -Epoch 467/468 -128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 -Epoch 468/468 -128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1365 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 398.75 sec -Time taken for epoch(SUBo): 289.42 sec -Time taken for epoch(OTHERo): 109.33 sec -<---------------------------------------|Epoch [78] END|---------------------------------------> - -Epoch: 79/486 (TSEC: 468) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00764]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 469/474 -128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 -Epoch 470/474 -128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 -Epoch 471/474 -128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 -Epoch 472/474 -128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 -Epoch 473/474 -128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 -Epoch 474/474 -128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2348 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 395.44 sec -Time taken for epoch(SUBo): 291.06 sec -Time taken for epoch(OTHERo): 104.39 sec -<---------------------------------------|Epoch [79] END|---------------------------------------> - -Epoch: 80/486 (TSEC: 474) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00758]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 475/480 -128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 -Epoch 476/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 -Epoch 477/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 -Epoch 478/480 -128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 -Epoch 479/480 -128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 -Epoch 480/480 -128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2441 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 399.87 sec -Time taken for epoch(SUBo): 293.34 sec -Time taken for epoch(OTHERo): 106.54 sec -<---------------------------------------|Epoch [80] END|---------------------------------------> - -Epoch: 81/486 (TSEC: 480) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00752]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 481/486 -128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 -Epoch 482/486 -128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 -Epoch 483/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 -Epoch 484/486 -128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 -Epoch 485/486 -128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 -Epoch 486/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3567 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.57 sec -Time taken for epoch(SUBo): 258.36 sec -Time taken for epoch(OTHERo): 102.21 sec -<---------------------------------------|Epoch [81] END|---------------------------------------> - -Epoch: 82/486 (TSEC: 486) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00746]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 487/492 -128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 -Epoch 488/492 -128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 -Epoch 489/492 -128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 -Epoch 490/492 -128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 -Epoch 491/492 -128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 -Epoch 492/492 -128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2877 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 338.30 sec -Time taken for epoch(SUBo): 256.81 sec -Time taken for epoch(OTHERo): 81.49 sec -<---------------------------------------|Epoch [82] END|---------------------------------------> - -Epoch: 83/486 (TSEC: 492) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0074]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 493/498 -128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 -Epoch 494/498 -128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 -Epoch 495/498 -128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 -Epoch 496/498 -128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 -Epoch 497/498 -128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 -Epoch 498/498 -128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3008 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 341.92 sec -Time taken for epoch(SUBo): 260.54 sec -Time taken for epoch(OTHERo): 81.38 sec -<---------------------------------------|Epoch [83] END|---------------------------------------> - -Epoch: 84/486 (TSEC: 498) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00734]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 499/504 -128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 -Epoch 500/504 -128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 -Epoch 501/504 -128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 -Epoch 502/504 -128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 -Epoch 503/504 -128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 -Epoch 504/504 -128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3369 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 401.59 sec -Time taken for epoch(SUBo): 296.36 sec -Time taken for epoch(OTHERo): 105.23 sec -<---------------------------------------|Epoch [84] END|---------------------------------------> - -Epoch: 85/486 (TSEC: 504) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00728]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 505/510 -128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 -Epoch 506/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 -Epoch 507/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 -Epoch 508/510 -128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 -Epoch 509/510 -128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 -Epoch 510/510 -128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2355 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 378.93 sec -Time taken for epoch(SUBo): 271.88 sec -Time taken for epoch(OTHERo): 107.05 sec -<---------------------------------------|Epoch [85] END|---------------------------------------> - -Epoch: 86/486 (TSEC: 510) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00722]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 511/516 -128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 -Epoch 512/516 -128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 -Epoch 513/516 -128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 -Epoch 514/516 -128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 -Epoch 515/516 -128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 -Epoch 516/516 -128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2163 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 348.35 sec -Time taken for epoch(SUBo): 264.53 sec -Time taken for epoch(OTHERo): 83.82 sec -<---------------------------------------|Epoch [86] END|---------------------------------------> - -Epoch: 87/486 (TSEC: 516) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00716]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 517/522 -128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 -Epoch 518/522 -128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 -Epoch 519/522 -128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 -Epoch 520/522 -128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 -Epoch 521/522 -128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 -Epoch 522/522 -128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.32 sec -Time taken for epoch(SUBo): 265.09 sec -Time taken for epoch(OTHERo): 84.23 sec -<---------------------------------------|Epoch [87] END|---------------------------------------> - -Epoch: 88/486 (TSEC: 522) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0071]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 523/528 -128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 -Epoch 524/528 -128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 -Epoch 525/528 -128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 -Epoch 526/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 -Epoch 527/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 -Epoch 528/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3755 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.31 sec -Time taken for epoch(SUBo): 260.67 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [88] END|---------------------------------------> - -Epoch: 89/486 (TSEC: 528) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00704]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 529/534 -128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 -Epoch 530/534 -128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 -Epoch 531/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 -Epoch 532/534 -128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 -Epoch 533/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 -Epoch 534/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1451 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 344.88 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 83.03 sec -<---------------------------------------|Epoch [89] END|---------------------------------------> - -Epoch: 90/486 (TSEC: 534) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00698]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 535/540 -128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 -Epoch 536/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 -Epoch 537/540 -128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 -Epoch 538/540 -128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 -Epoch 539/540 -128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 -Epoch 540/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2261 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 376.02 sec -Time taken for epoch(SUBo): 290.31 sec -Time taken for epoch(OTHERo): 85.71 sec -<---------------------------------------|Epoch [90] END|---------------------------------------> - -Epoch: 91/486 (TSEC: 540) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00692]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 541/546 -128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 -Epoch 542/546 -128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 -Epoch 543/546 -128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 -Epoch 544/546 -128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 -Epoch 545/546 -128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 -Epoch 546/546 -128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2810 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 409.06 sec -Time taken for epoch(SUBo): 293.27 sec -Time taken for epoch(OTHERo): 115.79 sec -<---------------------------------------|Epoch [91] END|---------------------------------------> - -Epoch: 92/486 (TSEC: 546) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00686]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 547/552 -128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 -Epoch 548/552 -128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 -Epoch 549/552 -128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 -Epoch 550/552 -128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 -Epoch 551/552 -128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 -Epoch 552/552 -128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2734 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 402.95 sec -Time taken for epoch(SUBo): 289.90 sec -Time taken for epoch(OTHERo): 113.04 sec -<---------------------------------------|Epoch [92] END|---------------------------------------> - -Epoch: 93/486 (TSEC: 552) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0068]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 553/558 -128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 -Epoch 554/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 -Epoch 555/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 -Epoch 556/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 -Epoch 557/558 -128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 -Epoch 558/558 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4314 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 343.82 sec -Time taken for epoch(SUBo): 258.19 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [93] END|---------------------------------------> - -Epoch: 94/486 (TSEC: 558) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00674]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 559/564 -128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 -Epoch 560/564 -128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 -Epoch 561/564 -128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 -Epoch 562/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 -Epoch 563/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 -Epoch 564/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2974 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.85 sec -Time taken for epoch(SUBo): 261.88 sec -Time taken for epoch(OTHERo): 85.97 sec -<---------------------------------------|Epoch [94] END|---------------------------------------> - -Epoch: 95/486 (TSEC: 564) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00668]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 565/570 -128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 -Epoch 566/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 -Epoch 567/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 -Epoch 568/570 -128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 -Epoch 569/570 -128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 -Epoch 570/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.4469 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.57 sec -Time taken for epoch(SUBo): 260.42 sec -Time taken for epoch(OTHERo): 89.15 sec -<---------------------------------------|Epoch [95] END|---------------------------------------> - -Epoch: 96/486 (TSEC: 570) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 -Setting training OneCycleLr::maxlr to [0.00662]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 571/576 -128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 -Epoch 572/576 -128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 -Epoch 573/576 -128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 -Epoch 574/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 -Epoch 575/576 -128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 -Epoch 576/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3027 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.90 sec -Time taken for epoch(SUBo): 261.28 sec -Time taken for epoch(OTHERo): 99.62 sec -<---------------------------------------|Epoch [96] END|---------------------------------------> - -Epoch: 97/486 (TSEC: 576) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00656]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 577/582 -128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 -Epoch 578/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 -Epoch 579/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 -Epoch 580/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 -Epoch 581/582 -128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 -Epoch 582/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3618 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.27 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 84.42 sec -<---------------------------------------|Epoch [97] END|---------------------------------------> - -Epoch: 98/486 (TSEC: 582) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0065]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 583/588 -128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 -Epoch 584/588 -128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 -Epoch 585/588 -128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 -Epoch 586/588 -128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 -Epoch 587/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 -Epoch 588/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3557 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.51 sec -Time taken for epoch(SUBo): 262.33 sec -Time taken for epoch(OTHERo): 83.18 sec -<---------------------------------------|Epoch [98] END|---------------------------------------> - -Epoch: 99/486 (TSEC: 588) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00644]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 589/594 -128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 -Epoch 590/594 -128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 -Epoch 591/594 -128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 -Epoch 592/594 -128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 -Epoch 593/594 -128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 -Epoch 594/594 -128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3592 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.37 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 85.09 sec -<---------------------------------------|Epoch [99] END|---------------------------------------> - -Epoch: 100/486 (TSEC: 594) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00638]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 595/600 -128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 -Epoch 596/600 -128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 -Epoch 597/600 -128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 -Epoch 598/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 -Epoch 599/600 -128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 -Epoch 600/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.3042 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.54 sec -Time taken for epoch(SUBo): 263.28 sec -Time taken for epoch(OTHERo): 82.26 sec -<---------------------------------------|Epoch [100] END|---------------------------------------> - -Epoch: 101/486 (TSEC: 600) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00632]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 601/606 -128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 -Epoch 602/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 -Epoch 603/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 -Epoch 604/606 -128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 605/606 -128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 -Epoch 606/606 -128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1757 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.57 sec -Time taken for epoch(SUBo): 261.73 sec -Time taken for epoch(OTHERo): 85.84 sec -<---------------------------------------|Epoch [101] END|---------------------------------------> - -Epoch: 102/486 (TSEC: 606) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00626]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 607/612 -128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 -Epoch 608/612 -128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 -Epoch 609/612 -128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 -Epoch 610/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 -Epoch 611/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 -Epoch 612/612 -128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.4659 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 350.48 sec -Time taken for epoch(SUBo): 263.62 sec -Time taken for epoch(OTHERo): 86.85 sec -<---------------------------------------|Epoch [102] END|---------------------------------------> - -Epoch: 103/486 (TSEC: 612) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0062]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 613/618 -128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 -Epoch 614/618 -128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 -Epoch 615/618 -128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 -Epoch 616/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 -Epoch 617/618 -128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 -Epoch 618/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3278 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 356.91 sec -Time taken for epoch(SUBo): 264.67 sec -Time taken for epoch(OTHERo): 92.23 sec -<---------------------------------------|Epoch [103] END|---------------------------------------> - -Epoch: 104/486 (TSEC: 618) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00614]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 619/624 -128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 -Epoch 620/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 -Epoch 621/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 -Epoch 622/624 -128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 -Epoch 623/624 -128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 -Epoch 624/624 -128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.4053 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.90 sec -Time taken for epoch(SUBo): 260.79 sec -Time taken for epoch(OTHERo): 86.11 sec -<---------------------------------------|Epoch [104] END|---------------------------------------> - -Epoch: 105/486 (TSEC: 624) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00608]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 625/630 -128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 -Epoch 626/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 -Epoch 627/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 -Epoch 628/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 -Epoch 629/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 -Epoch 630/630 -128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2148 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 353.04 sec -Time taken for epoch(SUBo): 264.40 sec -Time taken for epoch(OTHERo): 88.64 sec -<---------------------------------------|Epoch [105] END|---------------------------------------> - -Epoch: 106/486 (TSEC: 630) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00602]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 631/636 -128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 -Epoch 632/636 -128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 -Epoch 633/636 -128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 -Epoch 634/636 -128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 -Epoch 635/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 -Epoch 636/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1188 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 356.65 sec -Time taken for epoch(SUBo): 263.16 sec -Time taken for epoch(OTHERo): 93.49 sec -<---------------------------------------|Epoch [106] END|---------------------------------------> - -Epoch: 107/486 (TSEC: 636) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00596]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 637/642 -128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 -Epoch 638/642 -128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 -Epoch 639/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 -Epoch 640/642 -128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 -Epoch 641/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 -Epoch 642/642 -128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2388 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 353.97 sec -Time taken for epoch(SUBo): 260.86 sec -Time taken for epoch(OTHERo): 93.11 sec -<---------------------------------------|Epoch [107] END|---------------------------------------> - -Epoch: 108/486 (TSEC: 642) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0059]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 643/648 -128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 -Epoch 644/648 -128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 -Epoch 645/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 -Epoch 646/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 -Epoch 647/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 -Epoch 648/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2612 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 351.69 sec -Time taken for epoch(SUBo): 260.60 sec -Time taken for epoch(OTHERo): 91.10 sec -<---------------------------------------|Epoch [108] END|---------------------------------------> - -Epoch: 109/486 (TSEC: 648) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00584]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 649/654 -128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 -Epoch 650/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 -Epoch 651/654 -128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 -Epoch 652/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 -Epoch 653/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 -Epoch 654/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3569 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 346.32 sec -Time taken for epoch(SUBo): 259.69 sec -Time taken for epoch(OTHERo): 86.63 sec -<---------------------------------------|Epoch [109] END|---------------------------------------> - -Epoch: 110/486 (TSEC: 654) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00578]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 655/660 -128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 -Epoch 656/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 -Epoch 657/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 -Epoch 658/660 -128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 -Epoch 659/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 -Epoch 660/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3959 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 347.66 sec -Time taken for epoch(SUBo): 261.10 sec -Time taken for epoch(OTHERo): 86.56 sec -<---------------------------------------|Epoch [110] END|---------------------------------------> - -Epoch: 111/486 (TSEC: 660) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00572]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 661/666 -128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 -Epoch 662/666 -128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 -Epoch 663/666 -128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 -Epoch 664/666 -128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 -Epoch 665/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 -Epoch 666/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2614 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 374.77 sec -Time taken for epoch(SUBo): 287.07 sec -Time taken for epoch(OTHERo): 87.70 sec -<---------------------------------------|Epoch [111] END|---------------------------------------> - -Epoch: 112/486 (TSEC: 666) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00566]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 667/672 -128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 -Epoch 668/672 -128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 -Epoch 669/672 -128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 -Epoch 670/672 -128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 -Epoch 671/672 -128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 -Epoch 672/672 -128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2773 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.29 sec -Time taken for epoch(SUBo): 294.69 sec -Time taken for epoch(OTHERo): 108.60 sec -<---------------------------------------|Epoch [112] END|---------------------------------------> - -Epoch: 113/486 (TSEC: 672) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0056]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 673/678 -128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 -Epoch 674/678 -128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 -Epoch 675/678 -128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 676/678 -128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 -Epoch 677/678 -128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 -Epoch 678/678 -128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3138 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 405.22 sec -Time taken for epoch(SUBo): 290.78 sec -Time taken for epoch(OTHERo): 114.43 sec -<---------------------------------------|Epoch [113] END|---------------------------------------> - -Epoch: 114/486 (TSEC: 678) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00554]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 679/684 -128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 -Epoch 680/684 -128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 -Epoch 681/684 -128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 -Epoch 682/684 -128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 -Epoch 683/684 -128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 -Epoch 684/684 -128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.12 sec -Time taken for epoch(SUBo): 294.80 sec -Time taken for epoch(OTHERo): 117.31 sec -<---------------------------------------|Epoch [114] END|---------------------------------------> - -Epoch: 115/486 (TSEC: 684) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00548]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 685/690 -128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 -Epoch 686/690 -128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 -Epoch 687/690 -128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 -Epoch 688/690 -128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 -Epoch 689/690 -128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 -Epoch 690/690 -128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4240 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.16 sec -Time taken for epoch(SUBo): 291.71 sec -Time taken for epoch(OTHERo): 110.46 sec -<---------------------------------------|Epoch [115] END|---------------------------------------> - -Epoch: 116/486 (TSEC: 690) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00542]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 691/696 -128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 -Epoch 692/696 -128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 -Epoch 693/696 -128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 -Epoch 694/696 -128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 -Epoch 695/696 -128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 -Epoch 696/696 -128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3796 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.58 sec -Time taken for epoch(SUBo): 295.23 sec -Time taken for epoch(OTHERo): 113.35 sec -<---------------------------------------|Epoch [116] END|---------------------------------------> - -Epoch: 117/486 (TSEC: 696) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00536]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 697/702 -128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 -Epoch 698/702 -128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 -Epoch 699/702 -128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 -Epoch 700/702 -128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 -Epoch 701/702 -128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 -Epoch 702/702 -128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3449 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.03 sec -Time taken for epoch(SUBo): 295.99 sec -Time taken for epoch(OTHERo): 115.05 sec -<---------------------------------------|Epoch [117] END|---------------------------------------> - -Epoch: 118/486 (TSEC: 702) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0053]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 703/708 -128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 -Epoch 704/708 -128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 -Epoch 705/708 -128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 -Epoch 706/708 -128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 -Epoch 707/708 -128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 -Epoch 708/708 -128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2279 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.63 sec -Time taken for epoch(SUBo): 296.34 sec -Time taken for epoch(OTHERo): 117.29 sec -<---------------------------------------|Epoch [118] END|---------------------------------------> - -Epoch: 119/486 (TSEC: 708) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00524]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 709/714 -128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 -Epoch 710/714 -128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 -Epoch 711/714 -128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 -Epoch 712/714 -128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 -Epoch 713/714 -128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 -Epoch 714/714 -128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3194 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.60 sec -Time taken for epoch(SUBo): 294.03 sec -Time taken for epoch(OTHERo): 114.57 sec -<---------------------------------------|Epoch [119] END|---------------------------------------> - -Epoch: 120/486 (TSEC: 714) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00518]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 715/720 -128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 -Epoch 716/720 -128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 -Epoch 717/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 -Epoch 718/720 -128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 -Epoch 719/720 -128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 -Epoch 720/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.5143 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.26 sec -Time taken for epoch(SUBo): 295.66 sec -Time taken for epoch(OTHERo): 112.60 sec -<---------------------------------------|Epoch [120] END|---------------------------------------> - -Epoch: 121/486 (TSEC: 720) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00512]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 721/726 -128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 -Epoch 722/726 -128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 -Epoch 723/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 -Epoch 724/726 -128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 -Epoch 725/726 -128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 -Epoch 726/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4177 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.06 sec -Time taken for epoch(SUBo): 295.42 sec -Time taken for epoch(OTHERo): 118.64 sec -<---------------------------------------|Epoch [121] END|---------------------------------------> - -Epoch: 122/486 (TSEC: 726) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00506]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 727/732 -128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 -Epoch 728/732 -128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 -Epoch 729/732 -128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 -Epoch 730/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 -Epoch 731/732 -128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 -Epoch 732/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4436 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.37 sec -Time taken for epoch(SUBo): 293.85 sec -Time taken for epoch(OTHERo): 117.52 sec -<---------------------------------------|Epoch [122] END|---------------------------------------> - -Epoch: 123/486 (TSEC: 732) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.005]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 733/738 -128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 -Epoch 734/738 -128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 -Epoch 735/738 -128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 -Epoch 736/738 -128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 -Epoch 737/738 -128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 -Epoch 738/738 -128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4303 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.96 sec -Time taken for epoch(SUBo): 294.39 sec -Time taken for epoch(OTHERo): 118.57 sec -<---------------------------------------|Epoch [123] END|---------------------------------------> - -Epoch: 124/486 (TSEC: 738) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00494]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 739/744 -128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 -Epoch 740/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 -Epoch 741/744 -128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 -Epoch 742/744 -128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 -Epoch 743/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 -Epoch 744/744 -128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4234 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.23 sec -Time taken for epoch(SUBo): 298.41 sec -Time taken for epoch(OTHERo): 114.83 sec -<---------------------------------------|Epoch [124] END|---------------------------------------> - -Epoch: 125/486 (TSEC: 744) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00488]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 745/750 -128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 -Epoch 746/750 -128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 -Epoch 747/750 -128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 -Epoch 748/750 -128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 -Epoch 749/750 -128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 -Epoch 750/750 -128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3677 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.29 sec -Time taken for epoch(SUBo): 293.29 sec -Time taken for epoch(OTHERo): 119.99 sec -<---------------------------------------|Epoch [125] END|---------------------------------------> - -Epoch: 126/486 (TSEC: 750) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00482]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 751/756 -128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 -Epoch 752/756 -128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 -Epoch 753/756 -128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 -Epoch 754/756 -128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 -Epoch 755/756 -128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 -Epoch 756/756 -128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3409 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.45 sec -Time taken for epoch(SUBo): 293.26 sec -Time taken for epoch(OTHERo): 110.19 sec -<---------------------------------------|Epoch [126] END|---------------------------------------> - -Epoch: 127/486 (TSEC: 756) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00476]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 757/762 -128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 -Epoch 758/762 -128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 -Epoch 759/762 -128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 -Epoch 760/762 -128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 -Epoch 761/762 -128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 -Epoch 762/762 -128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3632 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.89 sec -Time taken for epoch(SUBo): 291.93 sec -Time taken for epoch(OTHERo): 111.96 sec -<---------------------------------------|Epoch [127] END|---------------------------------------> - -Epoch: 128/486 (TSEC: 762) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 -Setting training OneCycleLr::maxlr to [0.0047]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 763/768 -128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 -Epoch 764/768 -128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 -Epoch 765/768 -128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 -Epoch 766/768 -128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 -Epoch 767/768 -128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 -Epoch 768/768 -128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3065 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 425.95 sec -Time taken for epoch(SUBo): 291.59 sec -Time taken for epoch(OTHERo): 134.36 sec -<---------------------------------------|Epoch [128] END|---------------------------------------> - -Epoch: 129/486 (TSEC: 768) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00464]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 769/774 -128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 -Epoch 770/774 -128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 -Epoch 771/774 -128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 -Epoch 772/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 -Epoch 773/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 -Epoch 774/774 -128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2221 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.23 sec -Time taken for epoch(SUBo): 293.03 sec -Time taken for epoch(OTHERo): 109.20 sec -<---------------------------------------|Epoch [129] END|---------------------------------------> - -Epoch: 130/486 (TSEC: 774) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00458]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 775/780 -128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 -Epoch 776/780 -128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 -Epoch 777/780 -128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 -Epoch 778/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 -Epoch 779/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 -Epoch 780/780 -128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3315 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.71 sec -Time taken for epoch(SUBo): 293.90 sec -Time taken for epoch(OTHERo): 108.81 sec -<---------------------------------------|Epoch [130] END|---------------------------------------> - -Epoch: 131/486 (TSEC: 780) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00452]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 781/786 -128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 -Epoch 782/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 -Epoch 783/786 -128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 -Epoch 784/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 -Epoch 785/786 -128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 -Epoch 786/786 -128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.5019 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 424.42 sec -Time taken for epoch(SUBo): 300.59 sec -Time taken for epoch(OTHERo): 123.83 sec -<---------------------------------------|Epoch [131] END|---------------------------------------> - -Epoch: 132/486 (TSEC: 786) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00446]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 787/792 -128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 -Epoch 788/792 -128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 -Epoch 789/792 -128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 -Epoch 790/792 -128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 -Epoch 791/792 -128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 -Epoch 792/792 -128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9215 -Model Test loss: 0.5337 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.90 sec -Time taken for epoch(SUBo): 299.61 sec -Time taken for epoch(OTHERo): 121.28 sec -<---------------------------------------|Epoch [132] END|---------------------------------------> - -Epoch: 133/486 (TSEC: 792) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0044]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 793/798 -128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 -Epoch 794/798 -128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 -Epoch 795/798 -128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 -Epoch 796/798 -128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 -Epoch 797/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 -Epoch 798/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.5005 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.52 sec -Time taken for epoch(SUBo): 296.92 sec -Time taken for epoch(OTHERo): 120.59 sec -<---------------------------------------|Epoch [133] END|---------------------------------------> - -Epoch: 134/486 (TSEC: 798) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00434]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 799/804 -128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 -Epoch 800/804 -128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 -Epoch 801/804 -128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 -Epoch 802/804 -128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 -Epoch 803/804 -128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 -Epoch 804/804 -128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9151 -Model Test loss: 0.5460 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.80 sec -Time taken for epoch(SUBo): 297.98 sec -Time taken for epoch(OTHERo): 122.82 sec -<---------------------------------------|Epoch [134] END|---------------------------------------> - -Epoch: 135/486 (TSEC: 804) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00428]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 805/810 -128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 -Epoch 806/810 -128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 -Epoch 807/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 -Epoch 808/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 -Epoch 809/810 -128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 -Epoch 810/810 -128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9103 -Model Test loss: 0.5751 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.70 sec -Time taken for epoch(SUBo): 297.58 sec -Time taken for epoch(OTHERo): 117.13 sec -<---------------------------------------|Epoch [135] END|---------------------------------------> - -Epoch: 136/486 (TSEC: 810) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00422]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 811/816 -128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 -Epoch 812/816 -128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 -Epoch 813/816 -128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 -Epoch 814/816 -128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 -Epoch 815/816 -128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 -Epoch 816/816 -128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3819 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.58 sec -Time taken for epoch(SUBo): 300.30 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [136] END|---------------------------------------> - -Epoch: 137/486 (TSEC: 816) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00416]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 817/822 -128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 -Epoch 818/822 -128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 -Epoch 819/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 -Epoch 820/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 -Epoch 821/822 -128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 -Epoch 822/822 -128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4434 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.54 sec -Time taken for epoch(SUBo): 297.62 sec -Time taken for epoch(OTHERo): 118.92 sec -<---------------------------------------|Epoch [137] END|---------------------------------------> - -Epoch: 138/486 (TSEC: 822) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0041]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 823/828 -128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 -Epoch 824/828 -128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 -Epoch 825/828 -128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 -Epoch 826/828 -128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 -Epoch 827/828 -128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 -Epoch 828/828 -128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4551 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.46 sec -Time taken for epoch(SUBo): 297.55 sec -Time taken for epoch(OTHERo): 117.91 sec -<---------------------------------------|Epoch [138] END|---------------------------------------> - -Epoch: 139/486 (TSEC: 828) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00404]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 829/834 -128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 -Epoch 830/834 -128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 -Epoch 831/834 -128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 -Epoch 832/834 -128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 -Epoch 833/834 -128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 -Epoch 834/834 -128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.5237 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 418.66 sec -Time taken for epoch(SUBo): 295.90 sec -Time taken for epoch(OTHERo): 122.76 sec -<---------------------------------------|Epoch [139] END|---------------------------------------> - -Epoch: 140/486 (TSEC: 834) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00398]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 835/840 -128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 -Epoch 836/840 -128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 -Epoch 837/840 -128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 -Epoch 838/840 -128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 -Epoch 839/840 -128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 -Epoch 840/840 -128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5710 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.43 sec -Time taken for epoch(SUBo): 298.12 sec -Time taken for epoch(OTHERo): 122.31 sec -<---------------------------------------|Epoch [140] END|---------------------------------------> - -Epoch: 141/486 (TSEC: 840) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00392]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 841/846 -128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 -Epoch 842/846 -128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 -Epoch 843/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 -Epoch 844/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 -Epoch 845/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 -Epoch 846/846 -128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.5128 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 423.15 sec -Time taken for epoch(SUBo): 298.17 sec -Time taken for epoch(OTHERo): 124.98 sec -<---------------------------------------|Epoch [141] END|---------------------------------------> - -Epoch: 142/486 (TSEC: 846) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00386]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 847/852 -128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 -Epoch 848/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 -Epoch 849/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 -Epoch 850/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 -Epoch 851/852 -128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 -Epoch 852/852 -128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5103 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.49 sec -Time taken for epoch(SUBo): 301.87 sec -Time taken for epoch(OTHERo): 114.61 sec -<---------------------------------------|Epoch [142] END|---------------------------------------> - -Epoch: 143/486 (TSEC: 852) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0038]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 853/858 -128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 -Epoch 854/858 -128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 -Epoch 855/858 -128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 -Epoch 856/858 -128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 -Epoch 857/858 -128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 -Epoch 858/858 -128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.7814 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.74 sec -Time taken for epoch(SUBo): 295.81 sec -Time taken for epoch(OTHERo): 113.94 sec -<---------------------------------------|Epoch [143] END|---------------------------------------> - -Epoch: 144/486 (TSEC: 858) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00374]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 859/864 -128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 -Epoch 860/864 -128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 -Epoch 861/864 -128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 -Epoch 862/864 -128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 -Epoch 863/864 -128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 -Epoch 864/864 -128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3838 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.79 sec -Time taken for epoch(SUBo): 295.17 sec -Time taken for epoch(OTHERo): 118.61 sec -<---------------------------------------|Epoch [144] END|---------------------------------------> - -Epoch: 145/486 (TSEC: 864) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00368]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 865/870 -128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 -Epoch 866/870 -128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 -Epoch 867/870 -128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 -Epoch 868/870 -128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 -Epoch 869/870 -128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 -Epoch 870/870 -128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3789 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.68 sec -Time taken for epoch(SUBo): 295.62 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [145] END|---------------------------------------> - -Epoch: 146/486 (TSEC: 870) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00362]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 871/876 -128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 -Epoch 872/876 -128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 -Epoch 873/876 -128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 -Epoch 874/876 -128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 -Epoch 875/876 -128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 -Epoch 876/876 -128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.5834 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.30 sec -Time taken for epoch(SUBo): 298.45 sec -Time taken for epoch(OTHERo): 116.86 sec -<---------------------------------------|Epoch [146] END|---------------------------------------> - -Epoch: 147/486 (TSEC: 876) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00356]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 877/882 -128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 -Epoch 878/882 -128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 -Epoch 879/882 -128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 -Epoch 880/882 -128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 -Epoch 881/882 -128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 -Epoch 882/882 -128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3896 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.42 sec -Time taken for epoch(SUBo): 297.44 sec -Time taken for epoch(OTHERo): 119.98 sec -<---------------------------------------|Epoch [147] END|---------------------------------------> - -Epoch: 148/486 (TSEC: 882) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0035]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 883/888 -128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 -Epoch 884/888 -128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 -Epoch 885/888 -128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 -Epoch 886/888 -128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 -Epoch 887/888 -128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 -Epoch 888/888 -128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5339 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.09 sec -Time taken for epoch(SUBo): 293.02 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [148] END|---------------------------------------> - -Epoch: 149/486 (TSEC: 888) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00344]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 889/894 -128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 -Epoch 890/894 -128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 -Epoch 891/894 -128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 -Epoch 892/894 -128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 -Epoch 893/894 -128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 -Epoch 894/894 -128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.4329 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.69 sec -Time taken for epoch(SUBo): 298.62 sec -Time taken for epoch(OTHERo): 117.07 sec -<---------------------------------------|Epoch [149] END|---------------------------------------> - -Epoch: 150/486 (TSEC: 894) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00338]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 895/900 -128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 -Epoch 896/900 -128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 -Epoch 897/900 -128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 -Epoch 898/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 -Epoch 899/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 -Epoch 900/900 -128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.6034 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.43 sec -Time taken for epoch(SUBo): 292.15 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [150] END|---------------------------------------> - -Epoch: 151/486 (TSEC: 900) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00332]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 901/906 -128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 -Epoch 902/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 -Epoch 903/906 -128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 -Epoch 904/906 -128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 -Epoch 905/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 -Epoch 906/906 -128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3160 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.64 sec -Time taken for epoch(SUBo): 296.21 sec -Time taken for epoch(OTHERo): 120.43 sec -<---------------------------------------|Epoch [151] END|---------------------------------------> - -Epoch: 152/486 (TSEC: 906) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00326]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 907/912 -128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 -Epoch 908/912 -128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 -Epoch 909/912 -128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 -Epoch 910/912 -128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 -Epoch 911/912 - 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 -KeyboardInterrupt. -Training done. - +Training the model... + +Setup Verbose: +Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... +Use_extended_tensorboard [False]. +Debug_OUTPUT_DPS [True]. +OneCycleLr_UFTS [False]. +Setup Verbose END. + +Epoch: 1/486 (TSEC: 0) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Fitting ImageDataGenerator... +- ImageDataGenerator fit done. +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 +Epoch 2/6 +128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 +Epoch 3/6 +128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 +Epoch 4/6 +128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 +Epoch 5/6 +128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 +Epoch 6/6 +128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... +Model Test acc: 0.8974 +Model Test loss: 2.4019 +Improved model accuracy from 0 to 0.8974359035491943. Saving model. +Saving full model H5 format... +Improved model loss from inf to 2.4019267559051514. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 676.74 sec +Time taken for epoch(SUBo): 271.12 sec +Time taken for epoch(OTHERo): 405.62 sec +<---------------------------------------|Epoch [1] END|---------------------------------------> + +Epoch: 2/486 (TSEC: 6) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 7/12 +128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 +Epoch 8/12 +128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 +Epoch 9/12 +128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 +Epoch 10/12 +128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 +Epoch 11/12 +128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 +Epoch 12/12 +128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... +Model Test acc: 0.9407 +Model Test loss: 0.5406 +Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. +Saving full model H5 format... +Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.91 sec +Time taken for epoch(SUBo): 257.59 sec +Time taken for epoch(OTHERo): 68.33 sec +<---------------------------------------|Epoch [2] END|---------------------------------------> + +Epoch: 3/486 (TSEC: 12) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 13/18 +128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 +Epoch 14/18 +128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 +Epoch 15/18 +128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 +Epoch 16/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 +Epoch 17/18 +128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 +Epoch 18/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... +Model Test acc: 0.9423 +Model Test loss: 0.4656 +Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. +Saving full model H5 format... +Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 324.58 sec +Time taken for epoch(SUBo): 255.82 sec +Time taken for epoch(OTHERo): 68.76 sec +<---------------------------------------|Epoch [3] END|---------------------------------------> + +Epoch: 4/486 (TSEC: 18) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 19/24 +128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 +Epoch 20/24 +128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 +Epoch 21/24 +128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 +Epoch 22/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 +Epoch 23/24 +128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 +Epoch 24/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... +Model Test acc: 0.9343 +Model Test loss: 0.4089 +Model accuracy did not improve from 0.942307710647583. Not saving model. +Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 323.62 sec +Time taken for epoch(SUBo): 256.60 sec +Time taken for epoch(OTHERo): 67.02 sec +<---------------------------------------|Epoch [4] END|---------------------------------------> + +Epoch: 5/486 (TSEC: 24) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 25/30 +128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 +Epoch 26/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 +Epoch 27/30 +128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 +Epoch 28/30 +128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 +Epoch 29/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 +Epoch 30/30 +128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.2338 +Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. +Saving full model H5 format... +Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.89 sec +Time taken for epoch(SUBo): 258.52 sec +Time taken for epoch(OTHERo): 67.37 sec +<---------------------------------------|Epoch [5] END|---------------------------------------> + +Epoch: 6/486 (TSEC: 30) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 31/36 +128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 +Epoch 32/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 +Epoch 33/36 +128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 +Epoch 34/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 +Epoch 35/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 +Epoch 36/36 +128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2253 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 321.73 sec +Time taken for epoch(SUBo): 256.17 sec +Time taken for epoch(OTHERo): 65.57 sec +<---------------------------------------|Epoch [6] END|---------------------------------------> + +Epoch: 7/486 (TSEC: 36) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 37/42 +128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 +Epoch 38/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 +Epoch 39/42 +128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 +Epoch 40/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 +Epoch 41/42 +128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 +Epoch 42/42 +128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2070 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 326.03 sec +Time taken for epoch(SUBo): 259.27 sec +Time taken for epoch(OTHERo): 66.76 sec +<---------------------------------------|Epoch [7] END|---------------------------------------> + +Epoch: 8/486 (TSEC: 42) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 43/48 +128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 +Epoch 44/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 +Epoch 45/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 +Epoch 46/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 +Epoch 47/48 +128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 +Epoch 48/48 +128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2199 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Model loss did not improve from 0.20697814226150513. Not saving model. +Time taken for epoch(FULL): 322.67 sec +Time taken for epoch(SUBo): 256.59 sec +Time taken for epoch(OTHERo): 66.08 sec +<---------------------------------------|Epoch [8] END|---------------------------------------> + +Epoch: 9/486 (TSEC: 48) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 49/54 +128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 +Epoch 50/54 +128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 +Epoch 51/54 +128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 +Epoch 52/54 +128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 +Epoch 53/54 +128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 +Epoch 54/54 +128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.1662 +Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. +Saving full model H5 format... +Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 327.90 sec +Time taken for epoch(SUBo): 257.53 sec +Time taken for epoch(OTHERo): 70.37 sec +<---------------------------------------|Epoch [9] END|---------------------------------------> + +Epoch: 10/486 (TSEC: 54) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 55/60 +128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 +Epoch 56/60 +128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 +Epoch 57/60 +128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 +Epoch 58/60 +128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 +Epoch 59/60 +128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 +Epoch 60/60 +128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2059 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 327.86 sec +Time taken for epoch(SUBo): 259.66 sec +Time taken for epoch(OTHERo): 68.20 sec +<---------------------------------------|Epoch [10] END|---------------------------------------> + +Epoch: 11/486 (TSEC: 60) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 61/66 +128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 +Epoch 62/66 +128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 +Epoch 63/66 +128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 +Epoch 64/66 +128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 +Epoch 65/66 +128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 +Epoch 66/66 +128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2938 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 257.49 sec +Time taken for epoch(OTHERo): 69.06 sec +<---------------------------------------|Epoch [11] END|---------------------------------------> + +Epoch: 12/486 (TSEC: 66) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 67/72 +128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 +Epoch 68/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 +Epoch 69/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 +Epoch 70/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 +Epoch 71/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 +Epoch 72/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... +Model Test acc: 0.9631 +Model Test loss: 0.2115 +Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 324.68 sec +Time taken for epoch(SUBo): 253.65 sec +Time taken for epoch(OTHERo): 71.03 sec +<---------------------------------------|Epoch [12] END|---------------------------------------> + +Epoch: 13/486 (TSEC: 72) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 73/78 +128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 +Epoch 74/78 +128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 +Epoch 75/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 +Epoch 76/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 +Epoch 77/78 +128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 +Epoch 78/78 +128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.1545 +Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. +Saving full model H5 format... +Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.97 sec +Time taken for epoch(SUBo): 251.55 sec +Time taken for epoch(OTHERo): 74.42 sec +<---------------------------------------|Epoch [13] END|---------------------------------------> + +Epoch: 14/486 (TSEC: 78) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 79/84 +128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 +Epoch 80/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 +Epoch 81/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 +Epoch 82/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 +Epoch 83/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 +Epoch 84/84 +128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1780 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 71.59 sec +<---------------------------------------|Epoch [14] END|---------------------------------------> + +Epoch: 15/486 (TSEC: 84) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 85/90 +128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 +Epoch 86/90 +128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 +Epoch 87/90 +128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 +Epoch 88/90 +128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 +Epoch 89/90 +128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 +Epoch 90/90 +128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... +Model Test acc: 0.9615 +Model Test loss: 0.2233 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.17 sec +Time taken for epoch(SUBo): 252.81 sec +Time taken for epoch(OTHERo): 70.36 sec +<---------------------------------------|Epoch [15] END|---------------------------------------> + +Epoch: 16/486 (TSEC: 90) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 91/96 +128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 +Epoch 92/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 +Epoch 93/96 +128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 +Epoch 94/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 +Epoch 95/96 +128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 +Epoch 96/96 +128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1648 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 319.96 sec +Time taken for epoch(SUBo): 249.52 sec +Time taken for epoch(OTHERo): 70.43 sec +<---------------------------------------|Epoch [16] END|---------------------------------------> + +Epoch: 17/486 (TSEC: 96) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 97/102 +128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 +Epoch 98/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 +Epoch 99/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 +Epoch 100/102 +128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 +Epoch 101/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 +Epoch 102/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1764 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.46 sec +Time taken for epoch(SUBo): 253.14 sec +Time taken for epoch(OTHERo): 67.31 sec +<---------------------------------------|Epoch [17] END|---------------------------------------> + +Epoch: 18/486 (TSEC: 102) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 103/108 +128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 +Epoch 104/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 +Epoch 105/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 +Epoch 106/108 +128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 +Epoch 107/108 +128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 +Epoch 108/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2677 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.64 sec +Time taken for epoch(SUBo): 253.71 sec +Time taken for epoch(OTHERo): 70.93 sec +<---------------------------------------|Epoch [18] END|---------------------------------------> + +Epoch: 19/486 (TSEC: 108) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 109/114 +128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 +Epoch 110/114 +128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 +Epoch 111/114 +128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 +Epoch 112/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 +Epoch 113/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 +Epoch 114/114 +128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2055 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.02 sec +Time taken for epoch(SUBo): 253.02 sec +Time taken for epoch(OTHERo): 69.00 sec +<---------------------------------------|Epoch [19] END|---------------------------------------> + +Epoch: 20/486 (TSEC: 114) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 115/120 +128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 +Epoch 116/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 +Epoch 117/120 +128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 +Epoch 118/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 +Epoch 119/120 +128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 +Epoch 120/120 +128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... +Model Test acc: 0.9439 +Model Test loss: 0.3367 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 252.69 sec +Time taken for epoch(OTHERo): 70.57 sec +<---------------------------------------|Epoch [20] END|---------------------------------------> + +Epoch: 21/486 (TSEC: 120) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 121/126 +128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 +Epoch 122/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 +Epoch 123/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 +Epoch 124/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 +Epoch 125/126 +128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 +Epoch 126/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... +Model Test acc: 0.9583 +Model Test loss: 0.2373 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.10 sec +Time taken for epoch(SUBo): 252.57 sec +Time taken for epoch(OTHERo): 68.53 sec +<---------------------------------------|Epoch [21] END|---------------------------------------> + +Epoch: 22/486 (TSEC: 126) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 127/132 +128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 +Epoch 128/132 +128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 +Epoch 129/132 +128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 +Epoch 130/132 +128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 +Epoch 131/132 +128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 +Epoch 132/132 +128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2035 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.92 sec +Time taken for epoch(SUBo): 252.61 sec +Time taken for epoch(OTHERo): 69.31 sec +<---------------------------------------|Epoch [22] END|---------------------------------------> + +Epoch: 23/486 (TSEC: 132) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 133/138 +128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 +Epoch 134/138 +128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 +Epoch 135/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 +Epoch 136/138 +128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 +Epoch 137/138 +128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 +Epoch 138/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1983 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 251.30 sec +Time taken for epoch(OTHERo): 71.96 sec +<---------------------------------------|Epoch [23] END|---------------------------------------> + +Epoch: 24/486 (TSEC: 138) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01094]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 139/144 +128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 +Epoch 140/144 +128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 +Epoch 141/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 +Epoch 142/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 +Epoch 143/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 144/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... +Model Test acc: 0.9567 +Model Test loss: 0.2415 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.37 sec +Time taken for epoch(SUBo): 251.97 sec +Time taken for epoch(OTHERo): 72.40 sec +<---------------------------------------|Epoch [24] END|---------------------------------------> + +Epoch: 25/486 (TSEC: 144) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01088]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 145/150 +128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 +Epoch 146/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 +Epoch 147/150 +128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 +Epoch 148/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 +Epoch 149/150 +128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 +Epoch 150/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2961 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.24 sec +Time taken for epoch(SUBo): 250.77 sec +Time taken for epoch(OTHERo): 69.47 sec +<---------------------------------------|Epoch [25] END|---------------------------------------> + +Epoch: 26/486 (TSEC: 150) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01082]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 151/156 +128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 +Epoch 152/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 +Epoch 153/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 +Epoch 154/156 +128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 +Epoch 155/156 +128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 +Epoch 156/156 +128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2054 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.50 sec +Time taken for epoch(SUBo): 253.89 sec +Time taken for epoch(OTHERo): 68.61 sec +<---------------------------------------|Epoch [26] END|---------------------------------------> + +Epoch: 27/486 (TSEC: 156) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01076]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 157/162 +128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 +Epoch 158/162 +128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 +Epoch 159/162 +128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 +Epoch 160/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 +Epoch 161/162 +128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 +Epoch 162/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2812 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.25 sec +Time taken for epoch(SUBo): 253.57 sec +Time taken for epoch(OTHERo): 69.69 sec +<---------------------------------------|Epoch [27] END|---------------------------------------> + +Epoch: 28/486 (TSEC: 162) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0107]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 163/168 +128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 +Epoch 164/168 +128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 165/168 +128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 +Epoch 166/168 +128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 +Epoch 167/168 +128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 +Epoch 168/168 +128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3527 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.79 sec +Time taken for epoch(SUBo): 252.26 sec +Time taken for epoch(OTHERo): 68.52 sec +<---------------------------------------|Epoch [28] END|---------------------------------------> + +Epoch: 29/486 (TSEC: 168) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01064]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 169/174 +128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 +Epoch 170/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 +Epoch 171/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 +Epoch 172/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 +Epoch 173/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 +Epoch 174/174 +128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3280 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.85 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 70.35 sec +<---------------------------------------|Epoch [29] END|---------------------------------------> + +Epoch: 30/486 (TSEC: 174) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01058]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 175/180 +128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 +Epoch 176/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 +Epoch 177/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 +Epoch 178/180 +128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 +Epoch 179/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 +Epoch 180/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2430 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.08 sec +Time taken for epoch(SUBo): 252.22 sec +Time taken for epoch(OTHERo): 69.87 sec +<---------------------------------------|Epoch [30] END|---------------------------------------> + +Epoch: 31/486 (TSEC: 180) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01052]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 181/186 +128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 +Epoch 182/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 +Epoch 183/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 +Epoch 184/186 +128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 +Epoch 185/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 +Epoch 186/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9022 +Model Test loss: 0.5451 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.75 sec +Time taken for epoch(SUBo): 253.25 sec +Time taken for epoch(OTHERo): 69.50 sec +<---------------------------------------|Epoch [31] END|---------------------------------------> + +Epoch: 32/486 (TSEC: 186) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 +Setting training OneCycleLr::maxlr to [0.01046]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 187/192 +128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 +Epoch 188/192 +128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 +Epoch 189/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 +Epoch 190/192 +128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 +Epoch 191/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 +Epoch 192/192 +128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.2548 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 338.08 sec +Time taken for epoch(SUBo): 252.96 sec +Time taken for epoch(OTHERo): 85.12 sec +<---------------------------------------|Epoch [32] END|---------------------------------------> + +Epoch: 33/486 (TSEC: 192) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0104]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 193/198 +128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 +Epoch 194/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 +Epoch 195/198 +128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 +Epoch 196/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 +Epoch 197/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 +Epoch 198/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... +Model Test acc: 0.9663 +Model Test loss: 0.1816 +Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 328.41 sec +Time taken for epoch(SUBo): 253.11 sec +Time taken for epoch(OTHERo): 75.30 sec +<---------------------------------------|Epoch [33] END|---------------------------------------> + +Epoch: 34/486 (TSEC: 198) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01034]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 199/204 +128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 +Epoch 200/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 +Epoch 201/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 +Epoch 202/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 +Epoch 203/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 +Epoch 204/204 +128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2118 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 327.41 sec +Time taken for epoch(SUBo): 254.14 sec +Time taken for epoch(OTHERo): 73.27 sec +<---------------------------------------|Epoch [34] END|---------------------------------------> + +Epoch: 35/486 (TSEC: 204) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01028]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 205/210 +128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 +Epoch 206/210 +128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 +Epoch 207/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 +Epoch 208/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 +Epoch 209/210 +128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 +Epoch 210/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... +Model Test acc: 0.9551 +Model Test loss: 0.1544 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 329.96 sec +Time taken for epoch(SUBo): 252.88 sec +Time taken for epoch(OTHERo): 77.08 sec +<---------------------------------------|Epoch [35] END|---------------------------------------> + +Epoch: 36/486 (TSEC: 210) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01022]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 211/216 +128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 +Epoch 212/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 +Epoch 213/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 +Epoch 214/216 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 +Epoch 215/216 +128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 +Epoch 216/216 +128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3144 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 328.83 sec +Time taken for epoch(SUBo): 253.49 sec +Time taken for epoch(OTHERo): 75.34 sec +<---------------------------------------|Epoch [36] END|---------------------------------------> + +Epoch: 37/486 (TSEC: 216) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01016]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 217/222 +128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 +Epoch 218/222 +128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 +Epoch 219/222 +128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 +Epoch 220/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 +Epoch 221/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 +Epoch 222/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.4185 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.53 sec +Time taken for epoch(SUBo): 253.85 sec +Time taken for epoch(OTHERo): 73.68 sec +<---------------------------------------|Epoch [37] END|---------------------------------------> + +Epoch: 38/486 (TSEC: 222) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0101]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 223/228 +128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 +Epoch 224/228 +128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 +Epoch 225/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 +Epoch 226/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 +Epoch 227/228 +128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 +Epoch 228/228 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2645 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 252.83 sec +Time taken for epoch(OTHERo): 72.28 sec +<---------------------------------------|Epoch [38] END|---------------------------------------> + +Epoch: 39/486 (TSEC: 228) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01004]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 229/234 +128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 +Epoch 230/234 +128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 +Epoch 231/234 +128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 +Epoch 232/234 +128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 +Epoch 233/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 +Epoch 234/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.3982 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.39 sec +Time taken for epoch(SUBo): 254.95 sec +Time taken for epoch(OTHERo): 71.43 sec +<---------------------------------------|Epoch [39] END|---------------------------------------> + +Epoch: 40/486 (TSEC: 234) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00998]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 235/240 +128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 +Epoch 236/240 +128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 +Epoch 237/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 +Epoch 238/240 +128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 +Epoch 239/240 +128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 +Epoch 240/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2006 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.76 sec +Time taken for epoch(SUBo): 253.96 sec +Time taken for epoch(OTHERo): 71.80 sec +<---------------------------------------|Epoch [40] END|---------------------------------------> + +Epoch: 41/486 (TSEC: 240) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00992]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 241/246 +128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 +Epoch 242/246 +128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 +Epoch 243/246 +128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 +Epoch 244/246 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 +Epoch 245/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 +Epoch 246/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2011 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.07 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.68 sec +<---------------------------------------|Epoch [41] END|---------------------------------------> + +Epoch: 42/486 (TSEC: 246) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00986]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 247/252 +128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 +Epoch 248/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 +Epoch 249/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 +Epoch 250/252 +128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 +Epoch 251/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 +Epoch 252/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2136 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 253.59 sec +Time taken for epoch(OTHERo): 72.54 sec +<---------------------------------------|Epoch [42] END|---------------------------------------> + +Epoch: 43/486 (TSEC: 252) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0098]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 253/258 +128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 +Epoch 254/258 +128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 +Epoch 255/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 +Epoch 256/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 +Epoch 257/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 +Epoch 258/258 +128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2042 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.11 sec +Time taken for epoch(SUBo): 254.27 sec +Time taken for epoch(OTHERo): 72.84 sec +<---------------------------------------|Epoch [43] END|---------------------------------------> + +Epoch: 44/486 (TSEC: 258) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00974]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 259/264 +128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 +Epoch 260/264 +128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 +Epoch 261/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 +Epoch 262/264 +128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 +Epoch 263/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 +Epoch 264/264 +128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.3485 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 252.93 sec +Time taken for epoch(OTHERo): 73.19 sec +<---------------------------------------|Epoch [44] END|---------------------------------------> + +Epoch: 45/486 (TSEC: 264) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00968]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 265/270 +128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 +Epoch 266/270 +128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 +Epoch 267/270 +128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 +Epoch 268/270 +128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 +Epoch 269/270 +128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 +Epoch 270/270 +128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2324 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.17 sec +<---------------------------------------|Epoch [45] END|---------------------------------------> + +Epoch: 46/486 (TSEC: 270) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00962]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 271/276 +128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 +Epoch 272/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 +Epoch 273/276 +128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 +Epoch 274/276 +128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 +Epoch 275/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 +Epoch 276/276 +128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3108 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.76 sec +Time taken for epoch(SUBo): 254.60 sec +Time taken for epoch(OTHERo): 72.16 sec +<---------------------------------------|Epoch [46] END|---------------------------------------> + +Epoch: 47/486 (TSEC: 276) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00956]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 277/282 +128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 +Epoch 278/282 +128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 +Epoch 279/282 +128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 +Epoch 280/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 +Epoch 281/282 +128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 +Epoch 282/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3641 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.51 sec +Time taken for epoch(SUBo): 253.63 sec +Time taken for epoch(OTHERo): 72.88 sec +<---------------------------------------|Epoch [47] END|---------------------------------------> + +Epoch: 48/486 (TSEC: 282) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0095]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 283/288 +128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 +Epoch 284/288 +128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 +Epoch 285/288 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 +Epoch 286/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 +Epoch 287/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 +Epoch 288/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8958 +Model Test loss: 0.7104 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.17 sec +Time taken for epoch(SUBo): 255.62 sec +Time taken for epoch(OTHERo): 74.55 sec +<---------------------------------------|Epoch [48] END|---------------------------------------> + +Epoch: 49/486 (TSEC: 288) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00944]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 289/294 +128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 +Epoch 290/294 +128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 +Epoch 291/294 +128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 +Epoch 292/294 +128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 +Epoch 293/294 +128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 +Epoch 294/294 +128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9183 +Model Test loss: 0.5959 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.11 sec +Time taken for epoch(SUBo): 254.80 sec +Time taken for epoch(OTHERo): 75.32 sec +<---------------------------------------|Epoch [49] END|---------------------------------------> + +Epoch: 50/486 (TSEC: 294) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00938]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 295/300 +128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 +Epoch 296/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 +Epoch 297/300 +128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 +Epoch 298/300 +128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 +Epoch 299/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 +Epoch 300/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4308 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 331.10 sec +Time taken for epoch(SUBo): 255.03 sec +Time taken for epoch(OTHERo): 76.07 sec +<---------------------------------------|Epoch [50] END|---------------------------------------> + +Epoch: 51/486 (TSEC: 300) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00932]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 301/306 +128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 +Epoch 302/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 +Epoch 303/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 +Epoch 304/306 +128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 +Epoch 305/306 +128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 +Epoch 306/306 +128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8974 +Model Test loss: 0.7052 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 362.92 sec +Time taken for epoch(SUBo): 286.09 sec +Time taken for epoch(OTHERo): 76.83 sec +<---------------------------------------|Epoch [51] END|---------------------------------------> + +Epoch: 52/486 (TSEC: 306) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00926]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 307/312 +128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 +Epoch 308/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 +Epoch 309/312 +128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 +Epoch 310/312 +128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 +Epoch 311/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 +Epoch 312/312 +128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9135 +Model Test loss: 0.5680 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.05 sec +Time taken for epoch(SUBo): 284.61 sec +Time taken for epoch(OTHERo): 95.44 sec +<---------------------------------------|Epoch [52] END|---------------------------------------> + +Epoch: 53/486 (TSEC: 312) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0092]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 313/318 +128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 +Epoch 314/318 +128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 +Epoch 315/318 +128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 +Epoch 316/318 +128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 +Epoch 317/318 +128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 +Epoch 318/318 +128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4632 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.68 sec +Time taken for epoch(SUBo): 286.71 sec +Time taken for epoch(OTHERo): 93.97 sec +<---------------------------------------|Epoch [53] END|---------------------------------------> + +Epoch: 54/486 (TSEC: 318) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00914]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 319/324 +128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 +Epoch 320/324 +128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 +Epoch 321/324 +128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 +Epoch 322/324 +128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 +Epoch 323/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 +Epoch 324/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5101 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 389.45 sec +Time taken for epoch(SUBo): 287.64 sec +Time taken for epoch(OTHERo): 101.81 sec +<---------------------------------------|Epoch [54] END|---------------------------------------> + +Epoch: 55/486 (TSEC: 324) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00908]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 325/330 +128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 +Epoch 326/330 +128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 +Epoch 327/330 +128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 +Epoch 328/330 +128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 +Epoch 329/330 +128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 +Epoch 330/330 +128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3231 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.47 sec +Time taken for epoch(SUBo): 281.24 sec +Time taken for epoch(OTHERo): 99.23 sec +<---------------------------------------|Epoch [55] END|---------------------------------------> + +Epoch: 56/486 (TSEC: 330) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00902]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 331/336 +128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 +Epoch 332/336 +128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 +Epoch 333/336 +128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 +Epoch 334/336 +128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 +Epoch 335/336 +128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 +Epoch 336/336 +128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3479 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 392.43 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 103.65 sec +<---------------------------------------|Epoch [56] END|---------------------------------------> + +Epoch: 57/486 (TSEC: 336) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00896]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 337/342 +128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 +Epoch 338/342 +128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 +Epoch 339/342 +128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 +Epoch 340/342 +128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 +Epoch 341/342 +128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 +Epoch 342/342 +128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4083 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 387.98 sec +Time taken for epoch(SUBo): 285.68 sec +Time taken for epoch(OTHERo): 102.30 sec +<---------------------------------------|Epoch [57] END|---------------------------------------> + +Epoch: 58/486 (TSEC: 342) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0089]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 343/348 +128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 +Epoch 344/348 +128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 +Epoch 345/348 +128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 +Epoch 346/348 +128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 +Epoch 347/348 +128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 +Epoch 348/348 +128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3776 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 350.83 sec +Time taken for epoch(SUBo): 268.69 sec +Time taken for epoch(OTHERo): 82.14 sec +<---------------------------------------|Epoch [58] END|---------------------------------------> + +Epoch: 59/486 (TSEC: 348) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00884]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 349/354 +128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 +Epoch 350/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 +Epoch 351/354 +128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 +Epoch 352/354 +128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 +Epoch 353/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 +Epoch 354/354 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4066 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 353.60 sec +Time taken for epoch(SUBo): 258.60 sec +Time taken for epoch(OTHERo): 95.01 sec +<---------------------------------------|Epoch [59] END|---------------------------------------> + +Epoch: 60/486 (TSEC: 354) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00878]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 355/360 +128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 +Epoch 356/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 +Epoch 357/360 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 +Epoch 358/360 +128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 +Epoch 359/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 +Epoch 360/360 +128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4142 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 346.87 sec +Time taken for epoch(SUBo): 257.34 sec +Time taken for epoch(OTHERo): 89.53 sec +<---------------------------------------|Epoch [60] END|---------------------------------------> + +Epoch: 61/486 (TSEC: 360) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00872]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 361/366 +128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 +Epoch 362/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 +Epoch 363/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 +Epoch 364/366 +128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 +Epoch 365/366 +128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 +Epoch 366/366 +128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.7276 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.90 sec +Time taken for epoch(SUBo): 264.37 sec +Time taken for epoch(OTHERo): 77.53 sec +<---------------------------------------|Epoch [61] END|---------------------------------------> + +Epoch: 62/486 (TSEC: 366) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00866]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 367/372 +128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 +Epoch 368/372 +128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 +Epoch 369/372 +128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 +Epoch 370/372 +128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 +Epoch 371/372 +128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 +Epoch 372/372 +128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.2727 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 344.05 sec +Time taken for epoch(SUBo): 266.44 sec +Time taken for epoch(OTHERo): 77.61 sec +<---------------------------------------|Epoch [62] END|---------------------------------------> + +Epoch: 63/486 (TSEC: 372) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0086]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 373/378 +128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 +Epoch 374/378 +128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 +Epoch 375/378 +128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 +Epoch 376/378 +128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 +Epoch 377/378 +128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 +Epoch 378/378 +128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4211 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 334.11 sec +Time taken for epoch(SUBo): 258.37 sec +Time taken for epoch(OTHERo): 75.73 sec +<---------------------------------------|Epoch [63] END|---------------------------------------> + +Epoch: 64/486 (TSEC: 378) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 +Setting training OneCycleLr::maxlr to [0.00854]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 379/384 +128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 +Epoch 380/384 +128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 +Epoch 381/384 +128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 +Epoch 382/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 +Epoch 383/384 +128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 +Epoch 384/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3692 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 356.90 sec +Time taken for epoch(SUBo): 259.87 sec +Time taken for epoch(OTHERo): 97.03 sec +<---------------------------------------|Epoch [64] END|---------------------------------------> + +Epoch: 65/486 (TSEC: 384) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00848]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 385/390 +128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 +Epoch 386/390 +128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 +Epoch 387/390 +128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 +Epoch 388/390 +128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 +Epoch 389/390 +128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 +Epoch 390/390 +128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1823 +Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.22 sec +Time taken for epoch(SUBo): 257.30 sec +Time taken for epoch(OTHERo): 83.93 sec +<---------------------------------------|Epoch [65] END|---------------------------------------> + +Epoch: 66/486 (TSEC: 390) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00842]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 391/396 +128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 +Epoch 392/396 +128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 +Epoch 393/396 +128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 +Epoch 394/396 +128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 +Epoch 395/396 +128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 +Epoch 396/396 +128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9551 +Model Test loss: 0.2462 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 340.59 sec +Time taken for epoch(SUBo): 259.99 sec +Time taken for epoch(OTHERo): 80.59 sec +<---------------------------------------|Epoch [66] END|---------------------------------------> + +Epoch: 67/486 (TSEC: 396) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00836]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 397/402 +128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 +Epoch 398/402 +128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 +Epoch 399/402 +128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 +Epoch 400/402 +128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 +Epoch 401/402 +128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 +Epoch 402/402 +128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2736 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 348.32 sec +Time taken for epoch(SUBo): 267.55 sec +Time taken for epoch(OTHERo): 80.77 sec +<---------------------------------------|Epoch [67] END|---------------------------------------> + +Epoch: 68/486 (TSEC: 402) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0083]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 403/408 +128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 +Epoch 404/408 +128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 +Epoch 405/408 +128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 +Epoch 406/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 +Epoch 407/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 +Epoch 408/408 +128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.2983 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 374.47 sec +Time taken for epoch(SUBo): 276.39 sec +Time taken for epoch(OTHERo): 98.08 sec +<---------------------------------------|Epoch [68] END|---------------------------------------> + +Epoch: 69/486 (TSEC: 408) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00824]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 409/414 +128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 +Epoch 410/414 +128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 +Epoch 411/414 +128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 +Epoch 412/414 +128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 +Epoch 413/414 +128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 +Epoch 414/414 +128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3477 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 343.05 sec +Time taken for epoch(SUBo): 260.05 sec +Time taken for epoch(OTHERo): 83.00 sec +<---------------------------------------|Epoch [69] END|---------------------------------------> + +Epoch: 70/486 (TSEC: 414) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00818]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 415/420 +128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 +Epoch 416/420 +128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 +Epoch 417/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 +Epoch 418/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 +Epoch 419/420 +128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 +Epoch 420/420 +128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9167 +Model Test loss: 0.4541 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 342.13 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 79.85 sec +<---------------------------------------|Epoch [70] END|---------------------------------------> + +Epoch: 71/486 (TSEC: 420) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00812]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 421/426 +128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 +Epoch 422/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 +Epoch 423/426 +128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 +Epoch 424/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 +Epoch 425/426 +128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 +Epoch 426/426 +128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2110 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.68 sec +Time taken for epoch(SUBo): 260.39 sec +Time taken for epoch(OTHERo): 81.29 sec +<---------------------------------------|Epoch [71] END|---------------------------------------> + +Epoch: 72/486 (TSEC: 426) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00806]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 427/432 +128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 +Epoch 428/432 +128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 +Epoch 429/432 +128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 +Epoch 430/432 +128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 +Epoch 431/432 +128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 +Epoch 432/432 +128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... +Model Test acc: 0.9615 +Model Test loss: 0.1528 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 353.28 sec +Time taken for epoch(SUBo): 265.48 sec +Time taken for epoch(OTHERo): 87.80 sec +<---------------------------------------|Epoch [72] END|---------------------------------------> + +Epoch: 73/486 (TSEC: 432) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.008]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 433/438 +128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 +Epoch 434/438 +128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 +Epoch 435/438 +128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 +Epoch 436/438 +128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 +Epoch 437/438 +128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 +Epoch 438/438 +128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1676 +Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.79 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 111.40 sec +<---------------------------------------|Epoch [73] END|---------------------------------------> + +Epoch: 74/486 (TSEC: 438) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00794]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 439/444 +128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 +Epoch 440/444 +128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 +Epoch 441/444 +128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 +Epoch 442/444 +128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 +Epoch 443/444 +128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 +Epoch 444/444 +128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.3061 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.10 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 108.32 sec +<---------------------------------------|Epoch [74] END|---------------------------------------> + +Epoch: 75/486 (TSEC: 444) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00788]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 445/450 +128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 +Epoch 446/450 +128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 +Epoch 447/450 +128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 +Epoch 448/450 +128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 +Epoch 449/450 +128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 +Epoch 450/450 +128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.2149 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.95 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 108.55 sec +<---------------------------------------|Epoch [75] END|---------------------------------------> + +Epoch: 76/486 (TSEC: 450) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00782]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 451/456 +128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 +Epoch 452/456 +128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 +Epoch 453/456 +128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 +Epoch 454/456 +128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 +Epoch 455/456 +128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 +Epoch 456/456 +128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1664 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.35 sec +Time taken for epoch(SUBo): 290.41 sec +Time taken for epoch(OTHERo): 109.94 sec +<---------------------------------------|Epoch [76] END|---------------------------------------> + +Epoch: 77/486 (TSEC: 456) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00776]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 457/462 +128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 +Epoch 458/462 +128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 +Epoch 459/462 +128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 +Epoch 460/462 +128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 +Epoch 461/462 +128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 +Epoch 462/462 +128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1549 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 394.70 sec +Time taken for epoch(SUBo): 289.87 sec +Time taken for epoch(OTHERo): 104.83 sec +<---------------------------------------|Epoch [77] END|---------------------------------------> + +Epoch: 78/486 (TSEC: 462) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0077]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 463/468 +128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 +Epoch 464/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 +Epoch 465/468 +128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 +Epoch 466/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 +Epoch 467/468 +128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 +Epoch 468/468 +128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1365 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 398.75 sec +Time taken for epoch(SUBo): 289.42 sec +Time taken for epoch(OTHERo): 109.33 sec +<---------------------------------------|Epoch [78] END|---------------------------------------> + +Epoch: 79/486 (TSEC: 468) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00764]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 469/474 +128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 +Epoch 470/474 +128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 +Epoch 471/474 +128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 +Epoch 472/474 +128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 +Epoch 473/474 +128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 +Epoch 474/474 +128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2348 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 395.44 sec +Time taken for epoch(SUBo): 291.06 sec +Time taken for epoch(OTHERo): 104.39 sec +<---------------------------------------|Epoch [79] END|---------------------------------------> + +Epoch: 80/486 (TSEC: 474) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00758]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 475/480 +128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 +Epoch 476/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 +Epoch 477/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 +Epoch 478/480 +128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 +Epoch 479/480 +128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 +Epoch 480/480 +128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2441 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 399.87 sec +Time taken for epoch(SUBo): 293.34 sec +Time taken for epoch(OTHERo): 106.54 sec +<---------------------------------------|Epoch [80] END|---------------------------------------> + +Epoch: 81/486 (TSEC: 480) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00752]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 481/486 +128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 +Epoch 482/486 +128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 +Epoch 483/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 +Epoch 484/486 +128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 +Epoch 485/486 +128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 +Epoch 486/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3567 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.57 sec +Time taken for epoch(SUBo): 258.36 sec +Time taken for epoch(OTHERo): 102.21 sec +<---------------------------------------|Epoch [81] END|---------------------------------------> + +Epoch: 82/486 (TSEC: 486) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00746]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 487/492 +128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 +Epoch 488/492 +128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 +Epoch 489/492 +128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 +Epoch 490/492 +128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 +Epoch 491/492 +128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 +Epoch 492/492 +128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2877 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 338.30 sec +Time taken for epoch(SUBo): 256.81 sec +Time taken for epoch(OTHERo): 81.49 sec +<---------------------------------------|Epoch [82] END|---------------------------------------> + +Epoch: 83/486 (TSEC: 492) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0074]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 493/498 +128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 +Epoch 494/498 +128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 +Epoch 495/498 +128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 +Epoch 496/498 +128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 +Epoch 497/498 +128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 +Epoch 498/498 +128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3008 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 341.92 sec +Time taken for epoch(SUBo): 260.54 sec +Time taken for epoch(OTHERo): 81.38 sec +<---------------------------------------|Epoch [83] END|---------------------------------------> + +Epoch: 84/486 (TSEC: 498) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00734]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 499/504 +128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 +Epoch 500/504 +128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 +Epoch 501/504 +128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 +Epoch 502/504 +128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 +Epoch 503/504 +128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 +Epoch 504/504 +128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3369 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 401.59 sec +Time taken for epoch(SUBo): 296.36 sec +Time taken for epoch(OTHERo): 105.23 sec +<---------------------------------------|Epoch [84] END|---------------------------------------> + +Epoch: 85/486 (TSEC: 504) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00728]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 505/510 +128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 +Epoch 506/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 +Epoch 507/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 +Epoch 508/510 +128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 +Epoch 509/510 +128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 +Epoch 510/510 +128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2355 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 378.93 sec +Time taken for epoch(SUBo): 271.88 sec +Time taken for epoch(OTHERo): 107.05 sec +<---------------------------------------|Epoch [85] END|---------------------------------------> + +Epoch: 86/486 (TSEC: 510) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00722]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 511/516 +128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 +Epoch 512/516 +128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 +Epoch 513/516 +128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 +Epoch 514/516 +128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 +Epoch 515/516 +128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 +Epoch 516/516 +128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2163 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 348.35 sec +Time taken for epoch(SUBo): 264.53 sec +Time taken for epoch(OTHERo): 83.82 sec +<---------------------------------------|Epoch [86] END|---------------------------------------> + +Epoch: 87/486 (TSEC: 516) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00716]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 517/522 +128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 +Epoch 518/522 +128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 +Epoch 519/522 +128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 +Epoch 520/522 +128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 +Epoch 521/522 +128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 +Epoch 522/522 +128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.32 sec +Time taken for epoch(SUBo): 265.09 sec +Time taken for epoch(OTHERo): 84.23 sec +<---------------------------------------|Epoch [87] END|---------------------------------------> + +Epoch: 88/486 (TSEC: 522) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0071]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 523/528 +128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 +Epoch 524/528 +128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 +Epoch 525/528 +128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 +Epoch 526/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 +Epoch 527/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 +Epoch 528/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3755 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.31 sec +Time taken for epoch(SUBo): 260.67 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [88] END|---------------------------------------> + +Epoch: 89/486 (TSEC: 528) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00704]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 529/534 +128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 +Epoch 530/534 +128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 +Epoch 531/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 +Epoch 532/534 +128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 +Epoch 533/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 +Epoch 534/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1451 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 344.88 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 83.03 sec +<---------------------------------------|Epoch [89] END|---------------------------------------> + +Epoch: 90/486 (TSEC: 534) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00698]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 535/540 +128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 +Epoch 536/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 +Epoch 537/540 +128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 +Epoch 538/540 +128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 +Epoch 539/540 +128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 +Epoch 540/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2261 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 376.02 sec +Time taken for epoch(SUBo): 290.31 sec +Time taken for epoch(OTHERo): 85.71 sec +<---------------------------------------|Epoch [90] END|---------------------------------------> + +Epoch: 91/486 (TSEC: 540) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00692]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 541/546 +128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 +Epoch 542/546 +128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 +Epoch 543/546 +128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 +Epoch 544/546 +128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 +Epoch 545/546 +128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 +Epoch 546/546 +128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2810 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 409.06 sec +Time taken for epoch(SUBo): 293.27 sec +Time taken for epoch(OTHERo): 115.79 sec +<---------------------------------------|Epoch [91] END|---------------------------------------> + +Epoch: 92/486 (TSEC: 546) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00686]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 547/552 +128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 +Epoch 548/552 +128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 +Epoch 549/552 +128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 +Epoch 550/552 +128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 +Epoch 551/552 +128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 +Epoch 552/552 +128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2734 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 402.95 sec +Time taken for epoch(SUBo): 289.90 sec +Time taken for epoch(OTHERo): 113.04 sec +<---------------------------------------|Epoch [92] END|---------------------------------------> + +Epoch: 93/486 (TSEC: 552) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0068]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 553/558 +128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 +Epoch 554/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 +Epoch 555/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 +Epoch 556/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 +Epoch 557/558 +128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 +Epoch 558/558 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4314 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 343.82 sec +Time taken for epoch(SUBo): 258.19 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [93] END|---------------------------------------> + +Epoch: 94/486 (TSEC: 558) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00674]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 559/564 +128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 +Epoch 560/564 +128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 +Epoch 561/564 +128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 +Epoch 562/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 +Epoch 563/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 +Epoch 564/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2974 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.85 sec +Time taken for epoch(SUBo): 261.88 sec +Time taken for epoch(OTHERo): 85.97 sec +<---------------------------------------|Epoch [94] END|---------------------------------------> + +Epoch: 95/486 (TSEC: 564) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00668]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 565/570 +128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 +Epoch 566/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 +Epoch 567/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 +Epoch 568/570 +128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 +Epoch 569/570 +128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 +Epoch 570/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.4469 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.57 sec +Time taken for epoch(SUBo): 260.42 sec +Time taken for epoch(OTHERo): 89.15 sec +<---------------------------------------|Epoch [95] END|---------------------------------------> + +Epoch: 96/486 (TSEC: 570) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 +Setting training OneCycleLr::maxlr to [0.00662]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 571/576 +128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 +Epoch 572/576 +128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 +Epoch 573/576 +128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 +Epoch 574/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 +Epoch 575/576 +128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 +Epoch 576/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3027 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.90 sec +Time taken for epoch(SUBo): 261.28 sec +Time taken for epoch(OTHERo): 99.62 sec +<---------------------------------------|Epoch [96] END|---------------------------------------> + +Epoch: 97/486 (TSEC: 576) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00656]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 577/582 +128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 +Epoch 578/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 +Epoch 579/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 +Epoch 580/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 +Epoch 581/582 +128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 +Epoch 582/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3618 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.27 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 84.42 sec +<---------------------------------------|Epoch [97] END|---------------------------------------> + +Epoch: 98/486 (TSEC: 582) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0065]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 583/588 +128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 +Epoch 584/588 +128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 +Epoch 585/588 +128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 +Epoch 586/588 +128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 +Epoch 587/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 +Epoch 588/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3557 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.51 sec +Time taken for epoch(SUBo): 262.33 sec +Time taken for epoch(OTHERo): 83.18 sec +<---------------------------------------|Epoch [98] END|---------------------------------------> + +Epoch: 99/486 (TSEC: 588) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00644]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 589/594 +128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 +Epoch 590/594 +128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 +Epoch 591/594 +128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 +Epoch 592/594 +128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 +Epoch 593/594 +128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 +Epoch 594/594 +128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3592 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.37 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 85.09 sec +<---------------------------------------|Epoch [99] END|---------------------------------------> + +Epoch: 100/486 (TSEC: 594) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00638]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 595/600 +128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 +Epoch 596/600 +128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 +Epoch 597/600 +128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 +Epoch 598/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 +Epoch 599/600 +128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 +Epoch 600/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.3042 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.54 sec +Time taken for epoch(SUBo): 263.28 sec +Time taken for epoch(OTHERo): 82.26 sec +<---------------------------------------|Epoch [100] END|---------------------------------------> + +Epoch: 101/486 (TSEC: 600) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00632]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 601/606 +128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 +Epoch 602/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 +Epoch 603/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 +Epoch 604/606 +128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 605/606 +128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 +Epoch 606/606 +128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1757 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.57 sec +Time taken for epoch(SUBo): 261.73 sec +Time taken for epoch(OTHERo): 85.84 sec +<---------------------------------------|Epoch [101] END|---------------------------------------> + +Epoch: 102/486 (TSEC: 606) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00626]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 607/612 +128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 +Epoch 608/612 +128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 +Epoch 609/612 +128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 +Epoch 610/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 +Epoch 611/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 +Epoch 612/612 +128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.4659 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 350.48 sec +Time taken for epoch(SUBo): 263.62 sec +Time taken for epoch(OTHERo): 86.85 sec +<---------------------------------------|Epoch [102] END|---------------------------------------> + +Epoch: 103/486 (TSEC: 612) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0062]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 613/618 +128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 +Epoch 614/618 +128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 +Epoch 615/618 +128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 +Epoch 616/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 +Epoch 617/618 +128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 +Epoch 618/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3278 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 356.91 sec +Time taken for epoch(SUBo): 264.67 sec +Time taken for epoch(OTHERo): 92.23 sec +<---------------------------------------|Epoch [103] END|---------------------------------------> + +Epoch: 104/486 (TSEC: 618) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00614]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 619/624 +128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 +Epoch 620/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 +Epoch 621/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 +Epoch 622/624 +128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 +Epoch 623/624 +128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 +Epoch 624/624 +128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.4053 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.90 sec +Time taken for epoch(SUBo): 260.79 sec +Time taken for epoch(OTHERo): 86.11 sec +<---------------------------------------|Epoch [104] END|---------------------------------------> + +Epoch: 105/486 (TSEC: 624) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00608]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 625/630 +128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 +Epoch 626/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 +Epoch 627/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 +Epoch 628/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 +Epoch 629/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 +Epoch 630/630 +128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2148 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 353.04 sec +Time taken for epoch(SUBo): 264.40 sec +Time taken for epoch(OTHERo): 88.64 sec +<---------------------------------------|Epoch [105] END|---------------------------------------> + +Epoch: 106/486 (TSEC: 630) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00602]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 631/636 +128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 +Epoch 632/636 +128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 +Epoch 633/636 +128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 +Epoch 634/636 +128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 +Epoch 635/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 +Epoch 636/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1188 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 356.65 sec +Time taken for epoch(SUBo): 263.16 sec +Time taken for epoch(OTHERo): 93.49 sec +<---------------------------------------|Epoch [106] END|---------------------------------------> + +Epoch: 107/486 (TSEC: 636) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00596]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 637/642 +128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 +Epoch 638/642 +128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 +Epoch 639/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 +Epoch 640/642 +128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 +Epoch 641/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 +Epoch 642/642 +128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2388 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 353.97 sec +Time taken for epoch(SUBo): 260.86 sec +Time taken for epoch(OTHERo): 93.11 sec +<---------------------------------------|Epoch [107] END|---------------------------------------> + +Epoch: 108/486 (TSEC: 642) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0059]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 643/648 +128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 +Epoch 644/648 +128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 +Epoch 645/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 +Epoch 646/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 +Epoch 647/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 +Epoch 648/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2612 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 351.69 sec +Time taken for epoch(SUBo): 260.60 sec +Time taken for epoch(OTHERo): 91.10 sec +<---------------------------------------|Epoch [108] END|---------------------------------------> + +Epoch: 109/486 (TSEC: 648) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00584]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 649/654 +128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 +Epoch 650/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 +Epoch 651/654 +128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 +Epoch 652/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 +Epoch 653/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 +Epoch 654/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3569 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 346.32 sec +Time taken for epoch(SUBo): 259.69 sec +Time taken for epoch(OTHERo): 86.63 sec +<---------------------------------------|Epoch [109] END|---------------------------------------> + +Epoch: 110/486 (TSEC: 654) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00578]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 655/660 +128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 +Epoch 656/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 +Epoch 657/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 +Epoch 658/660 +128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 +Epoch 659/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 +Epoch 660/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3959 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 347.66 sec +Time taken for epoch(SUBo): 261.10 sec +Time taken for epoch(OTHERo): 86.56 sec +<---------------------------------------|Epoch [110] END|---------------------------------------> + +Epoch: 111/486 (TSEC: 660) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00572]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 661/666 +128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 +Epoch 662/666 +128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 +Epoch 663/666 +128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 +Epoch 664/666 +128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 +Epoch 665/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 +Epoch 666/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2614 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 374.77 sec +Time taken for epoch(SUBo): 287.07 sec +Time taken for epoch(OTHERo): 87.70 sec +<---------------------------------------|Epoch [111] END|---------------------------------------> + +Epoch: 112/486 (TSEC: 666) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00566]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 667/672 +128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 +Epoch 668/672 +128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 +Epoch 669/672 +128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 +Epoch 670/672 +128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 +Epoch 671/672 +128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 +Epoch 672/672 +128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2773 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.29 sec +Time taken for epoch(SUBo): 294.69 sec +Time taken for epoch(OTHERo): 108.60 sec +<---------------------------------------|Epoch [112] END|---------------------------------------> + +Epoch: 113/486 (TSEC: 672) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0056]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 673/678 +128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 +Epoch 674/678 +128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 +Epoch 675/678 +128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 676/678 +128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 +Epoch 677/678 +128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 +Epoch 678/678 +128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3138 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 405.22 sec +Time taken for epoch(SUBo): 290.78 sec +Time taken for epoch(OTHERo): 114.43 sec +<---------------------------------------|Epoch [113] END|---------------------------------------> + +Epoch: 114/486 (TSEC: 678) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00554]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 679/684 +128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 +Epoch 680/684 +128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 +Epoch 681/684 +128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 +Epoch 682/684 +128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 +Epoch 683/684 +128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 +Epoch 684/684 +128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.12 sec +Time taken for epoch(SUBo): 294.80 sec +Time taken for epoch(OTHERo): 117.31 sec +<---------------------------------------|Epoch [114] END|---------------------------------------> + +Epoch: 115/486 (TSEC: 684) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00548]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 685/690 +128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 +Epoch 686/690 +128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 +Epoch 687/690 +128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 +Epoch 688/690 +128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 +Epoch 689/690 +128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 +Epoch 690/690 +128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4240 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.16 sec +Time taken for epoch(SUBo): 291.71 sec +Time taken for epoch(OTHERo): 110.46 sec +<---------------------------------------|Epoch [115] END|---------------------------------------> + +Epoch: 116/486 (TSEC: 690) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00542]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 691/696 +128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 +Epoch 692/696 +128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 +Epoch 693/696 +128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 +Epoch 694/696 +128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 +Epoch 695/696 +128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 +Epoch 696/696 +128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3796 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.58 sec +Time taken for epoch(SUBo): 295.23 sec +Time taken for epoch(OTHERo): 113.35 sec +<---------------------------------------|Epoch [116] END|---------------------------------------> + +Epoch: 117/486 (TSEC: 696) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00536]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 697/702 +128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 +Epoch 698/702 +128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 +Epoch 699/702 +128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 +Epoch 700/702 +128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 +Epoch 701/702 +128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 +Epoch 702/702 +128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3449 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.03 sec +Time taken for epoch(SUBo): 295.99 sec +Time taken for epoch(OTHERo): 115.05 sec +<---------------------------------------|Epoch [117] END|---------------------------------------> + +Epoch: 118/486 (TSEC: 702) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0053]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 703/708 +128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 +Epoch 704/708 +128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 +Epoch 705/708 +128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 +Epoch 706/708 +128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 +Epoch 707/708 +128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 +Epoch 708/708 +128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2279 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.63 sec +Time taken for epoch(SUBo): 296.34 sec +Time taken for epoch(OTHERo): 117.29 sec +<---------------------------------------|Epoch [118] END|---------------------------------------> + +Epoch: 119/486 (TSEC: 708) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00524]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 709/714 +128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 +Epoch 710/714 +128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 +Epoch 711/714 +128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 +Epoch 712/714 +128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 +Epoch 713/714 +128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 +Epoch 714/714 +128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3194 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.60 sec +Time taken for epoch(SUBo): 294.03 sec +Time taken for epoch(OTHERo): 114.57 sec +<---------------------------------------|Epoch [119] END|---------------------------------------> + +Epoch: 120/486 (TSEC: 714) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00518]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 715/720 +128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 +Epoch 716/720 +128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 +Epoch 717/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 +Epoch 718/720 +128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 +Epoch 719/720 +128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 +Epoch 720/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.5143 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.26 sec +Time taken for epoch(SUBo): 295.66 sec +Time taken for epoch(OTHERo): 112.60 sec +<---------------------------------------|Epoch [120] END|---------------------------------------> + +Epoch: 121/486 (TSEC: 720) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00512]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 721/726 +128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 +Epoch 722/726 +128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 +Epoch 723/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 +Epoch 724/726 +128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 +Epoch 725/726 +128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 +Epoch 726/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4177 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.06 sec +Time taken for epoch(SUBo): 295.42 sec +Time taken for epoch(OTHERo): 118.64 sec +<---------------------------------------|Epoch [121] END|---------------------------------------> + +Epoch: 122/486 (TSEC: 726) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00506]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 727/732 +128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 +Epoch 728/732 +128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 +Epoch 729/732 +128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 +Epoch 730/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 +Epoch 731/732 +128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 +Epoch 732/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4436 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.37 sec +Time taken for epoch(SUBo): 293.85 sec +Time taken for epoch(OTHERo): 117.52 sec +<---------------------------------------|Epoch [122] END|---------------------------------------> + +Epoch: 123/486 (TSEC: 732) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.005]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 733/738 +128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 +Epoch 734/738 +128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 +Epoch 735/738 +128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 +Epoch 736/738 +128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 +Epoch 737/738 +128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 +Epoch 738/738 +128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4303 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.96 sec +Time taken for epoch(SUBo): 294.39 sec +Time taken for epoch(OTHERo): 118.57 sec +<---------------------------------------|Epoch [123] END|---------------------------------------> + +Epoch: 124/486 (TSEC: 738) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00494]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 739/744 +128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 +Epoch 740/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 +Epoch 741/744 +128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 +Epoch 742/744 +128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 +Epoch 743/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 +Epoch 744/744 +128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4234 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.23 sec +Time taken for epoch(SUBo): 298.41 sec +Time taken for epoch(OTHERo): 114.83 sec +<---------------------------------------|Epoch [124] END|---------------------------------------> + +Epoch: 125/486 (TSEC: 744) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00488]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 745/750 +128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 +Epoch 746/750 +128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 +Epoch 747/750 +128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 +Epoch 748/750 +128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 +Epoch 749/750 +128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 +Epoch 750/750 +128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3677 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.29 sec +Time taken for epoch(SUBo): 293.29 sec +Time taken for epoch(OTHERo): 119.99 sec +<---------------------------------------|Epoch [125] END|---------------------------------------> + +Epoch: 126/486 (TSEC: 750) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00482]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 751/756 +128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 +Epoch 752/756 +128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 +Epoch 753/756 +128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 +Epoch 754/756 +128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 +Epoch 755/756 +128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 +Epoch 756/756 +128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3409 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.45 sec +Time taken for epoch(SUBo): 293.26 sec +Time taken for epoch(OTHERo): 110.19 sec +<---------------------------------------|Epoch [126] END|---------------------------------------> + +Epoch: 127/486 (TSEC: 756) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00476]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 757/762 +128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 +Epoch 758/762 +128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 +Epoch 759/762 +128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 +Epoch 760/762 +128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 +Epoch 761/762 +128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 +Epoch 762/762 +128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3632 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.89 sec +Time taken for epoch(SUBo): 291.93 sec +Time taken for epoch(OTHERo): 111.96 sec +<---------------------------------------|Epoch [127] END|---------------------------------------> + +Epoch: 128/486 (TSEC: 762) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 +Setting training OneCycleLr::maxlr to [0.0047]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 763/768 +128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 +Epoch 764/768 +128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 +Epoch 765/768 +128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 +Epoch 766/768 +128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 +Epoch 767/768 +128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 +Epoch 768/768 +128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3065 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 425.95 sec +Time taken for epoch(SUBo): 291.59 sec +Time taken for epoch(OTHERo): 134.36 sec +<---------------------------------------|Epoch [128] END|---------------------------------------> + +Epoch: 129/486 (TSEC: 768) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00464]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 769/774 +128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 +Epoch 770/774 +128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 +Epoch 771/774 +128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 +Epoch 772/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 +Epoch 773/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 +Epoch 774/774 +128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2221 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.23 sec +Time taken for epoch(SUBo): 293.03 sec +Time taken for epoch(OTHERo): 109.20 sec +<---------------------------------------|Epoch [129] END|---------------------------------------> + +Epoch: 130/486 (TSEC: 774) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00458]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 775/780 +128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 +Epoch 776/780 +128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 +Epoch 777/780 +128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 +Epoch 778/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 +Epoch 779/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 +Epoch 780/780 +128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3315 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.71 sec +Time taken for epoch(SUBo): 293.90 sec +Time taken for epoch(OTHERo): 108.81 sec +<---------------------------------------|Epoch [130] END|---------------------------------------> + +Epoch: 131/486 (TSEC: 780) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00452]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 781/786 +128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 +Epoch 782/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 +Epoch 783/786 +128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 +Epoch 784/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 +Epoch 785/786 +128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 +Epoch 786/786 +128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.5019 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 424.42 sec +Time taken for epoch(SUBo): 300.59 sec +Time taken for epoch(OTHERo): 123.83 sec +<---------------------------------------|Epoch [131] END|---------------------------------------> + +Epoch: 132/486 (TSEC: 786) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00446]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 787/792 +128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 +Epoch 788/792 +128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 +Epoch 789/792 +128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 +Epoch 790/792 +128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 +Epoch 791/792 +128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 +Epoch 792/792 +128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9215 +Model Test loss: 0.5337 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.90 sec +Time taken for epoch(SUBo): 299.61 sec +Time taken for epoch(OTHERo): 121.28 sec +<---------------------------------------|Epoch [132] END|---------------------------------------> + +Epoch: 133/486 (TSEC: 792) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0044]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 793/798 +128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 +Epoch 794/798 +128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 +Epoch 795/798 +128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 +Epoch 796/798 +128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 +Epoch 797/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 +Epoch 798/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.5005 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.52 sec +Time taken for epoch(SUBo): 296.92 sec +Time taken for epoch(OTHERo): 120.59 sec +<---------------------------------------|Epoch [133] END|---------------------------------------> + +Epoch: 134/486 (TSEC: 798) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00434]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 799/804 +128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 +Epoch 800/804 +128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 +Epoch 801/804 +128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 +Epoch 802/804 +128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 +Epoch 803/804 +128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 +Epoch 804/804 +128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9151 +Model Test loss: 0.5460 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.80 sec +Time taken for epoch(SUBo): 297.98 sec +Time taken for epoch(OTHERo): 122.82 sec +<---------------------------------------|Epoch [134] END|---------------------------------------> + +Epoch: 135/486 (TSEC: 804) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00428]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 805/810 +128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 +Epoch 806/810 +128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 +Epoch 807/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 +Epoch 808/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 +Epoch 809/810 +128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 +Epoch 810/810 +128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9103 +Model Test loss: 0.5751 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.70 sec +Time taken for epoch(SUBo): 297.58 sec +Time taken for epoch(OTHERo): 117.13 sec +<---------------------------------------|Epoch [135] END|---------------------------------------> + +Epoch: 136/486 (TSEC: 810) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00422]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 811/816 +128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 +Epoch 812/816 +128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 +Epoch 813/816 +128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 +Epoch 814/816 +128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 +Epoch 815/816 +128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 +Epoch 816/816 +128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3819 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.58 sec +Time taken for epoch(SUBo): 300.30 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [136] END|---------------------------------------> + +Epoch: 137/486 (TSEC: 816) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00416]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 817/822 +128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 +Epoch 818/822 +128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 +Epoch 819/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 +Epoch 820/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 +Epoch 821/822 +128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 +Epoch 822/822 +128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4434 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.54 sec +Time taken for epoch(SUBo): 297.62 sec +Time taken for epoch(OTHERo): 118.92 sec +<---------------------------------------|Epoch [137] END|---------------------------------------> + +Epoch: 138/486 (TSEC: 822) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0041]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 823/828 +128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 +Epoch 824/828 +128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 +Epoch 825/828 +128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 +Epoch 826/828 +128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 +Epoch 827/828 +128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 +Epoch 828/828 +128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4551 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.46 sec +Time taken for epoch(SUBo): 297.55 sec +Time taken for epoch(OTHERo): 117.91 sec +<---------------------------------------|Epoch [138] END|---------------------------------------> + +Epoch: 139/486 (TSEC: 828) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00404]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 829/834 +128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 +Epoch 830/834 +128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 +Epoch 831/834 +128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 +Epoch 832/834 +128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 +Epoch 833/834 +128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 +Epoch 834/834 +128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.5237 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 418.66 sec +Time taken for epoch(SUBo): 295.90 sec +Time taken for epoch(OTHERo): 122.76 sec +<---------------------------------------|Epoch [139] END|---------------------------------------> + +Epoch: 140/486 (TSEC: 834) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00398]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 835/840 +128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 +Epoch 836/840 +128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 +Epoch 837/840 +128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 +Epoch 838/840 +128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 +Epoch 839/840 +128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 +Epoch 840/840 +128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5710 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.43 sec +Time taken for epoch(SUBo): 298.12 sec +Time taken for epoch(OTHERo): 122.31 sec +<---------------------------------------|Epoch [140] END|---------------------------------------> + +Epoch: 141/486 (TSEC: 840) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00392]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 841/846 +128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 +Epoch 842/846 +128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 +Epoch 843/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 +Epoch 844/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 +Epoch 845/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 +Epoch 846/846 +128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.5128 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 423.15 sec +Time taken for epoch(SUBo): 298.17 sec +Time taken for epoch(OTHERo): 124.98 sec +<---------------------------------------|Epoch [141] END|---------------------------------------> + +Epoch: 142/486 (TSEC: 846) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00386]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 847/852 +128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 +Epoch 848/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 +Epoch 849/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 +Epoch 850/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 +Epoch 851/852 +128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 +Epoch 852/852 +128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5103 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.49 sec +Time taken for epoch(SUBo): 301.87 sec +Time taken for epoch(OTHERo): 114.61 sec +<---------------------------------------|Epoch [142] END|---------------------------------------> + +Epoch: 143/486 (TSEC: 852) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0038]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 853/858 +128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 +Epoch 854/858 +128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 +Epoch 855/858 +128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 +Epoch 856/858 +128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 +Epoch 857/858 +128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 +Epoch 858/858 +128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.7814 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.74 sec +Time taken for epoch(SUBo): 295.81 sec +Time taken for epoch(OTHERo): 113.94 sec +<---------------------------------------|Epoch [143] END|---------------------------------------> + +Epoch: 144/486 (TSEC: 858) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00374]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 859/864 +128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 +Epoch 860/864 +128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 +Epoch 861/864 +128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 +Epoch 862/864 +128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 +Epoch 863/864 +128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 +Epoch 864/864 +128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3838 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.79 sec +Time taken for epoch(SUBo): 295.17 sec +Time taken for epoch(OTHERo): 118.61 sec +<---------------------------------------|Epoch [144] END|---------------------------------------> + +Epoch: 145/486 (TSEC: 864) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00368]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 865/870 +128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 +Epoch 866/870 +128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 +Epoch 867/870 +128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 +Epoch 868/870 +128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 +Epoch 869/870 +128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 +Epoch 870/870 +128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3789 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.68 sec +Time taken for epoch(SUBo): 295.62 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [145] END|---------------------------------------> + +Epoch: 146/486 (TSEC: 870) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00362]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 871/876 +128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 +Epoch 872/876 +128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 +Epoch 873/876 +128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 +Epoch 874/876 +128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 +Epoch 875/876 +128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 +Epoch 876/876 +128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.5834 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.30 sec +Time taken for epoch(SUBo): 298.45 sec +Time taken for epoch(OTHERo): 116.86 sec +<---------------------------------------|Epoch [146] END|---------------------------------------> + +Epoch: 147/486 (TSEC: 876) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00356]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 877/882 +128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 +Epoch 878/882 +128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 +Epoch 879/882 +128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 +Epoch 880/882 +128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 +Epoch 881/882 +128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 +Epoch 882/882 +128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3896 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.42 sec +Time taken for epoch(SUBo): 297.44 sec +Time taken for epoch(OTHERo): 119.98 sec +<---------------------------------------|Epoch [147] END|---------------------------------------> + +Epoch: 148/486 (TSEC: 882) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0035]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 883/888 +128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 +Epoch 884/888 +128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 +Epoch 885/888 +128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 +Epoch 886/888 +128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 +Epoch 887/888 +128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 +Epoch 888/888 +128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5339 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.09 sec +Time taken for epoch(SUBo): 293.02 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [148] END|---------------------------------------> + +Epoch: 149/486 (TSEC: 888) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00344]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 889/894 +128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 +Epoch 890/894 +128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 +Epoch 891/894 +128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 +Epoch 892/894 +128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 +Epoch 893/894 +128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 +Epoch 894/894 +128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.4329 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.69 sec +Time taken for epoch(SUBo): 298.62 sec +Time taken for epoch(OTHERo): 117.07 sec +<---------------------------------------|Epoch [149] END|---------------------------------------> + +Epoch: 150/486 (TSEC: 894) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00338]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 895/900 +128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 +Epoch 896/900 +128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 +Epoch 897/900 +128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 +Epoch 898/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 +Epoch 899/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 +Epoch 900/900 +128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.6034 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.43 sec +Time taken for epoch(SUBo): 292.15 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [150] END|---------------------------------------> + +Epoch: 151/486 (TSEC: 900) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00332]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 901/906 +128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 +Epoch 902/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 +Epoch 903/906 +128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 +Epoch 904/906 +128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 +Epoch 905/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 +Epoch 906/906 +128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3160 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.64 sec +Time taken for epoch(SUBo): 296.21 sec +Time taken for epoch(OTHERo): 120.43 sec +<---------------------------------------|Epoch [151] END|---------------------------------------> + +Epoch: 152/486 (TSEC: 906) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00326]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 907/912 +128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 +Epoch 908/912 +128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 +Epoch 909/912 +128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 +Epoch 910/912 +128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 +Epoch 911/912 + 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 +KeyboardInterrupt. +Training done. + diff --git a/backup/V5/TRAIN_LOG_ANSI_r.txt b/backup/V5/TRAIN_LOG_ANSI_r.txt index 2182925..1c2fbe6 100644 --- a/backup/V5/TRAIN_LOG_ANSI_r.txt +++ b/backup/V5/TRAIN_LOG_ANSI_r.txt @@ -1,4779 +1,4779 @@ -Training the model... - -Setup Verbose: -Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... -Use_extended_tensorboard [False]. -Debug_OUTPUT_DPS [True]. -OneCycleLr_UFTS [False]. -Setup Verbose END. - -Epoch: 1/486 (TSEC: 0) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Fitting ImageDataGenerator... -- ImageDataGenerator fit done. -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 1/6 -128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 -Epoch 2/6 -128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 -Epoch 3/6 -128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 -Epoch 4/6 -128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 -Epoch 5/6 -128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 -Epoch 6/6 -128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... -Model Test acc: 0.8974 -Model Test loss: 2.4019 -Improved model accuracy from 0 to 0.8974359035491943. Saving model. -Saving full model H5 format... -Improved model loss from inf to 2.4019267559051514. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 676.74 sec -Time taken for epoch(SUBo): 271.12 sec -Time taken for epoch(OTHERo): 405.62 sec -<---------------------------------------|Epoch [1] END|---------------------------------------> - -Epoch: 2/486 (TSEC: 6) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 7/12 -128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 -Epoch 8/12 -128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 -Epoch 9/12 -128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 -Epoch 10/12 -128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 -Epoch 11/12 -128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 -Epoch 12/12 -128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... -Model Test acc: 0.9407 -Model Test loss: 0.5406 -Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. -Saving full model H5 format... -Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.91 sec -Time taken for epoch(SUBo): 257.59 sec -Time taken for epoch(OTHERo): 68.33 sec -<---------------------------------------|Epoch [2] END|---------------------------------------> - -Epoch: 3/486 (TSEC: 12) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 13/18 -128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 -Epoch 14/18 -128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 -Epoch 15/18 -128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 -Epoch 16/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 -Epoch 17/18 -128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 -Epoch 18/18 -128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... -Model Test acc: 0.9423 -Model Test loss: 0.4656 -Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. -Saving full model H5 format... -Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 324.58 sec -Time taken for epoch(SUBo): 255.82 sec -Time taken for epoch(OTHERo): 68.76 sec -<---------------------------------------|Epoch [3] END|---------------------------------------> - -Epoch: 4/486 (TSEC: 18) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 19/24 -128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 -Epoch 20/24 -128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 -Epoch 21/24 -128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 -Epoch 22/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 -Epoch 23/24 -128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 -Epoch 24/24 -128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... -Model Test acc: 0.9343 -Model Test loss: 0.4089 -Model accuracy did not improve from 0.942307710647583. Not saving model. -Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 323.62 sec -Time taken for epoch(SUBo): 256.60 sec -Time taken for epoch(OTHERo): 67.02 sec -<---------------------------------------|Epoch [4] END|---------------------------------------> - -Epoch: 5/486 (TSEC: 24) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 25/30 -128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 -Epoch 26/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 -Epoch 27/30 -128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 -Epoch 28/30 -128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 -Epoch 29/30 -128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 -Epoch 30/30 -128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.2338 -Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. -Saving full model H5 format... -Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.89 sec -Time taken for epoch(SUBo): 258.52 sec -Time taken for epoch(OTHERo): 67.37 sec -<---------------------------------------|Epoch [5] END|---------------------------------------> - -Epoch: 6/486 (TSEC: 30) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 31/36 -128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 -Epoch 32/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 -Epoch 33/36 -128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 -Epoch 34/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 -Epoch 35/36 -128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 -Epoch 36/36 -128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2253 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 321.73 sec -Time taken for epoch(SUBo): 256.17 sec -Time taken for epoch(OTHERo): 65.57 sec -<---------------------------------------|Epoch [6] END|---------------------------------------> - -Epoch: 7/486 (TSEC: 36) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 37/42 -128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 -Epoch 38/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 -Epoch 39/42 -128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 -Epoch 40/42 -128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 -Epoch 41/42 -128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 -Epoch 42/42 -128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2070 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 326.03 sec -Time taken for epoch(SUBo): 259.27 sec -Time taken for epoch(OTHERo): 66.76 sec -<---------------------------------------|Epoch [7] END|---------------------------------------> - -Epoch: 8/486 (TSEC: 42) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 43/48 -128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 -Epoch 44/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 -Epoch 45/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 -Epoch 46/48 -128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 -Epoch 47/48 -128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 -Epoch 48/48 -128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... -Model Test acc: 0.9503 -Model Test loss: 0.2199 -Model accuracy did not improve from 0.9519230723381042. Not saving model. -Model loss did not improve from 0.20697814226150513. Not saving model. -Time taken for epoch(FULL): 322.67 sec -Time taken for epoch(SUBo): 256.59 sec -Time taken for epoch(OTHERo): 66.08 sec -<---------------------------------------|Epoch [8] END|---------------------------------------> - -Epoch: 9/486 (TSEC: 48) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 49/54 -128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 -Epoch 50/54 -128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 -Epoch 51/54 -128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 -Epoch 52/54 -128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 -Epoch 53/54 -128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 -Epoch 54/54 -128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.1662 -Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. -Saving full model H5 format... -Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 327.90 sec -Time taken for epoch(SUBo): 257.53 sec -Time taken for epoch(OTHERo): 70.37 sec -<---------------------------------------|Epoch [9] END|---------------------------------------> - -Epoch: 10/486 (TSEC: 54) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 55/60 -128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 -Epoch 56/60 -128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 -Epoch 57/60 -128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 -Epoch 58/60 -128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 -Epoch 59/60 -128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 -Epoch 60/60 -128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2059 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 327.86 sec -Time taken for epoch(SUBo): 259.66 sec -Time taken for epoch(OTHERo): 68.20 sec -<---------------------------------------|Epoch [10] END|---------------------------------------> - -Epoch: 11/486 (TSEC: 60) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 61/66 -128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 -Epoch 62/66 -128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 -Epoch 63/66 -128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 -Epoch 64/66 -128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 -Epoch 65/66 -128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 -Epoch 66/66 -128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... -Model Test acc: 0.9487 -Model Test loss: 0.2938 -Model accuracy did not improve from 0.9535256624221802. Not saving model. -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 257.49 sec -Time taken for epoch(OTHERo): 69.06 sec -<---------------------------------------|Epoch [11] END|---------------------------------------> - -Epoch: 12/486 (TSEC: 66) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 67/72 -128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 -Epoch 68/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 -Epoch 69/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 -Epoch 70/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 -Epoch 71/72 -128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 -Epoch 72/72 -128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... -Model Test acc: 0.9631 -Model Test loss: 0.2115 -Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.16622641682624817. Not saving model. -Time taken for epoch(FULL): 324.68 sec -Time taken for epoch(SUBo): 253.65 sec -Time taken for epoch(OTHERo): 71.03 sec -<---------------------------------------|Epoch [12] END|---------------------------------------> - -Epoch: 13/486 (TSEC: 72) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 73/78 -128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 -Epoch 74/78 -128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 -Epoch 75/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 -Epoch 76/78 -128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 -Epoch 77/78 -128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 -Epoch 78/78 -128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.1545 -Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. -Saving full model H5 format... -Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 325.97 sec -Time taken for epoch(SUBo): 251.55 sec -Time taken for epoch(OTHERo): 74.42 sec -<---------------------------------------|Epoch [13] END|---------------------------------------> - -Epoch: 14/486 (TSEC: 78) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 79/84 -128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 -Epoch 80/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 -Epoch 81/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 -Epoch 82/84 -128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 -Epoch 83/84 -128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 -Epoch 84/84 -128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1780 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 71.59 sec -<---------------------------------------|Epoch [14] END|---------------------------------------> - -Epoch: 15/486 (TSEC: 84) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 85/90 -128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 -Epoch 86/90 -128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 -Epoch 87/90 -128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 -Epoch 88/90 -128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 -Epoch 89/90 -128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 -Epoch 90/90 -128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... -Model Test acc: 0.9615 -Model Test loss: 0.2233 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.17 sec -Time taken for epoch(SUBo): 252.81 sec -Time taken for epoch(OTHERo): 70.36 sec -<---------------------------------------|Epoch [15] END|---------------------------------------> - -Epoch: 16/486 (TSEC: 90) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 91/96 -128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 -Epoch 92/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 -Epoch 93/96 -128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 -Epoch 94/96 -128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 -Epoch 95/96 -128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 -Epoch 96/96 -128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... -Model Test acc: 0.9599 -Model Test loss: 0.1648 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 319.96 sec -Time taken for epoch(SUBo): 249.52 sec -Time taken for epoch(OTHERo): 70.43 sec -<---------------------------------------|Epoch [16] END|---------------------------------------> - -Epoch: 17/486 (TSEC: 96) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 97/102 -128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 -Epoch 98/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 -Epoch 99/102 -128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 -Epoch 100/102 -128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 -Epoch 101/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 -Epoch 102/102 -128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1764 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.46 sec -Time taken for epoch(SUBo): 253.14 sec -Time taken for epoch(OTHERo): 67.31 sec -<---------------------------------------|Epoch [17] END|---------------------------------------> - -Epoch: 18/486 (TSEC: 102) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 103/108 -128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 -Epoch 104/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 -Epoch 105/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 -Epoch 106/108 -128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 -Epoch 107/108 -128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 -Epoch 108/108 -128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2677 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.64 sec -Time taken for epoch(SUBo): 253.71 sec -Time taken for epoch(OTHERo): 70.93 sec -<---------------------------------------|Epoch [18] END|---------------------------------------> - -Epoch: 19/486 (TSEC: 108) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 109/114 -128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 -Epoch 110/114 -128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 -Epoch 111/114 -128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 -Epoch 112/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 -Epoch 113/114 -128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 -Epoch 114/114 -128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2055 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.02 sec -Time taken for epoch(SUBo): 253.02 sec -Time taken for epoch(OTHERo): 69.00 sec -<---------------------------------------|Epoch [19] END|---------------------------------------> - -Epoch: 20/486 (TSEC: 114) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 115/120 -128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 -Epoch 116/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 -Epoch 117/120 -128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 -Epoch 118/120 -128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 -Epoch 119/120 -128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 -Epoch 120/120 -128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... -Model Test acc: 0.9439 -Model Test loss: 0.3367 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 252.69 sec -Time taken for epoch(OTHERo): 70.57 sec -<---------------------------------------|Epoch [20] END|---------------------------------------> - -Epoch: 21/486 (TSEC: 120) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 121/126 -128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 -Epoch 122/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 -Epoch 123/126 -128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 -Epoch 124/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 -Epoch 125/126 -128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 -Epoch 126/126 -128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... -Model Test acc: 0.9583 -Model Test loss: 0.2373 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.10 sec -Time taken for epoch(SUBo): 252.57 sec -Time taken for epoch(OTHERo): 68.53 sec -<---------------------------------------|Epoch [21] END|---------------------------------------> - -Epoch: 22/486 (TSEC: 126) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 127/132 -128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 -Epoch 128/132 -128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 -Epoch 129/132 -128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 -Epoch 130/132 -128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 -Epoch 131/132 -128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 -Epoch 132/132 -128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... -Model Test acc: 0.9535 -Model Test loss: 0.2035 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 321.92 sec -Time taken for epoch(SUBo): 252.61 sec -Time taken for epoch(OTHERo): 69.31 sec -<---------------------------------------|Epoch [22] END|---------------------------------------> - -Epoch: 23/486 (TSEC: 132) | [Learning the patterns] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.011]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 133/138 -128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 -Epoch 134/138 -128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 -Epoch 135/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 -Epoch 136/138 -128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 -Epoch 137/138 -128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 -Epoch 138/138 -128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... -Model Test acc: 0.9519 -Model Test loss: 0.1983 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.26 sec -Time taken for epoch(SUBo): 251.30 sec -Time taken for epoch(OTHERo): 71.96 sec -<---------------------------------------|Epoch [23] END|---------------------------------------> - -Epoch: 24/486 (TSEC: 138) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01094]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 139/144 -128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 -Epoch 140/144 -128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 -Epoch 141/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 -Epoch 142/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 -Epoch 143/144 -128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 144/144 -128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... -Model Test acc: 0.9567 -Model Test loss: 0.2415 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 324.37 sec -Time taken for epoch(SUBo): 251.97 sec -Time taken for epoch(OTHERo): 72.40 sec -<---------------------------------------|Epoch [24] END|---------------------------------------> - -Epoch: 25/486 (TSEC: 144) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01088]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 145/150 -128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 -Epoch 146/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 -Epoch 147/150 -128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 -Epoch 148/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 -Epoch 149/150 -128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 -Epoch 150/150 -128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2961 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.24 sec -Time taken for epoch(SUBo): 250.77 sec -Time taken for epoch(OTHERo): 69.47 sec -<---------------------------------------|Epoch [25] END|---------------------------------------> - -Epoch: 26/486 (TSEC: 150) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01082]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 151/156 -128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 -Epoch 152/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 -Epoch 153/156 -128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 -Epoch 154/156 -128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 -Epoch 155/156 -128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 -Epoch 156/156 -128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2054 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.50 sec -Time taken for epoch(SUBo): 253.89 sec -Time taken for epoch(OTHERo): 68.61 sec -<---------------------------------------|Epoch [26] END|---------------------------------------> - -Epoch: 27/486 (TSEC: 156) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01076]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 157/162 -128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 -Epoch 158/162 -128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 -Epoch 159/162 -128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 -Epoch 160/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 -Epoch 161/162 -128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 -Epoch 162/162 -128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2812 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.25 sec -Time taken for epoch(SUBo): 253.57 sec -Time taken for epoch(OTHERo): 69.69 sec -<---------------------------------------|Epoch [27] END|---------------------------------------> - -Epoch: 28/486 (TSEC: 162) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0107]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 163/168 -128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 -Epoch 164/168 -128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 -Epoch 165/168 -128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 -Epoch 166/168 -128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 -Epoch 167/168 -128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 -Epoch 168/168 -128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3527 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 320.79 sec -Time taken for epoch(SUBo): 252.26 sec -Time taken for epoch(OTHERo): 68.52 sec -<---------------------------------------|Epoch [28] END|---------------------------------------> - -Epoch: 29/486 (TSEC: 168) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01064]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 169/174 -128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 -Epoch 170/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 -Epoch 171/174 -128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 -Epoch 172/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 -Epoch 173/174 -128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 -Epoch 174/174 -128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3280 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 323.85 sec -Time taken for epoch(SUBo): 253.51 sec -Time taken for epoch(OTHERo): 70.35 sec -<---------------------------------------|Epoch [29] END|---------------------------------------> - -Epoch: 30/486 (TSEC: 174) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01058]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 175/180 -128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 -Epoch 176/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 -Epoch 177/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 -Epoch 178/180 -128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 -Epoch 179/180 -128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 -Epoch 180/180 -128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2430 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.08 sec -Time taken for epoch(SUBo): 252.22 sec -Time taken for epoch(OTHERo): 69.87 sec -<---------------------------------------|Epoch [30] END|---------------------------------------> - -Epoch: 31/486 (TSEC: 180) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01052]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 181/186 -128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 -Epoch 182/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 -Epoch 183/186 -128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 -Epoch 184/186 -128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 -Epoch 185/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 -Epoch 186/186 -128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9022 -Model Test loss: 0.5451 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 322.75 sec -Time taken for epoch(SUBo): 253.25 sec -Time taken for epoch(OTHERo): 69.50 sec -<---------------------------------------|Epoch [31] END|---------------------------------------> - -Epoch: 32/486 (TSEC: 186) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 -Setting training OneCycleLr::maxlr to [0.01046]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 187/192 -128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 -Epoch 188/192 -128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 -Epoch 189/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 -Epoch 190/192 -128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 -Epoch 191/192 -128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 -Epoch 192/192 -128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.2548 -Model accuracy did not improve from 0.9647436141967773. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 338.08 sec -Time taken for epoch(SUBo): 252.96 sec -Time taken for epoch(OTHERo): 85.12 sec -<---------------------------------------|Epoch [32] END|---------------------------------------> - -Epoch: 33/486 (TSEC: 192) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0104]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 193/198 -128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 -Epoch 194/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 -Epoch 195/198 -128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 -Epoch 196/198 -128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 -Epoch 197/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 -Epoch 198/198 -128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... -Model Test acc: 0.9663 -Model Test loss: 0.1816 -Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 328.41 sec -Time taken for epoch(SUBo): 253.11 sec -Time taken for epoch(OTHERo): 75.30 sec -<---------------------------------------|Epoch [33] END|---------------------------------------> - -Epoch: 34/486 (TSEC: 198) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01034]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 199/204 -128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 -Epoch 200/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 -Epoch 201/204 -128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 -Epoch 202/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 -Epoch 203/204 -128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 -Epoch 204/204 -128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2118 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.1544923484325409. Not saving model. -Time taken for epoch(FULL): 327.41 sec -Time taken for epoch(SUBo): 254.14 sec -Time taken for epoch(OTHERo): 73.27 sec -<---------------------------------------|Epoch [34] END|---------------------------------------> - -Epoch: 35/486 (TSEC: 204) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01028]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 205/210 -128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 -Epoch 206/210 -128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 -Epoch 207/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 -Epoch 208/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 -Epoch 209/210 -128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 -Epoch 210/210 -128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... -Model Test acc: 0.9551 -Model Test loss: 0.1544 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 329.96 sec -Time taken for epoch(SUBo): 252.88 sec -Time taken for epoch(OTHERo): 77.08 sec -<---------------------------------------|Epoch [35] END|---------------------------------------> - -Epoch: 36/486 (TSEC: 210) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01022]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 211/216 -128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 -Epoch 212/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 -Epoch 213/216 -128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 -Epoch 214/216 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 -Epoch 215/216 -128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 -Epoch 216/216 -128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3144 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 328.83 sec -Time taken for epoch(SUBo): 253.49 sec -Time taken for epoch(OTHERo): 75.34 sec -<---------------------------------------|Epoch [36] END|---------------------------------------> - -Epoch: 37/486 (TSEC: 216) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01016]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 217/222 -128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 -Epoch 218/222 -128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 -Epoch 219/222 -128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 -Epoch 220/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 -Epoch 221/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 -Epoch 222/222 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.4185 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.53 sec -Time taken for epoch(SUBo): 253.85 sec -Time taken for epoch(OTHERo): 73.68 sec -<---------------------------------------|Epoch [37] END|---------------------------------------> - -Epoch: 38/486 (TSEC: 222) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0101]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 223/228 -128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 -Epoch 224/228 -128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 -Epoch 225/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 -Epoch 226/228 -128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 -Epoch 227/228 -128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 -Epoch 228/228 -128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2645 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.10 sec -Time taken for epoch(SUBo): 252.83 sec -Time taken for epoch(OTHERo): 72.28 sec -<---------------------------------------|Epoch [38] END|---------------------------------------> - -Epoch: 39/486 (TSEC: 228) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.01004]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 229/234 -128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 -Epoch 230/234 -128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 -Epoch 231/234 -128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 -Epoch 232/234 -128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 -Epoch 233/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 -Epoch 234/234 -128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.3982 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.39 sec -Time taken for epoch(SUBo): 254.95 sec -Time taken for epoch(OTHERo): 71.43 sec -<---------------------------------------|Epoch [39] END|---------------------------------------> - -Epoch: 40/486 (TSEC: 234) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00998]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 235/240 -128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 -Epoch 236/240 -128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 -Epoch 237/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 -Epoch 238/240 -128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 -Epoch 239/240 -128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 -Epoch 240/240 -128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2006 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 325.76 sec -Time taken for epoch(SUBo): 253.96 sec -Time taken for epoch(OTHERo): 71.80 sec -<---------------------------------------|Epoch [40] END|---------------------------------------> - -Epoch: 41/486 (TSEC: 240) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00992]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 241/246 -128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 -Epoch 242/246 -128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 -Epoch 243/246 -128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 -Epoch 244/246 -128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 -Epoch 245/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 -Epoch 246/246 -128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2011 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.07 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.68 sec -<---------------------------------------|Epoch [41] END|---------------------------------------> - -Epoch: 42/486 (TSEC: 246) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00986]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 247/252 -128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 -Epoch 248/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 -Epoch 249/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 -Epoch 250/252 -128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 -Epoch 251/252 -128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 -Epoch 252/252 -128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2136 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 253.59 sec -Time taken for epoch(OTHERo): 72.54 sec -<---------------------------------------|Epoch [42] END|---------------------------------------> - -Epoch: 43/486 (TSEC: 252) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0098]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 253/258 -128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 -Epoch 254/258 -128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 -Epoch 255/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 -Epoch 256/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 -Epoch 257/258 -128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 -Epoch 258/258 -128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2042 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 327.11 sec -Time taken for epoch(SUBo): 254.27 sec -Time taken for epoch(OTHERo): 72.84 sec -<---------------------------------------|Epoch [43] END|---------------------------------------> - -Epoch: 44/486 (TSEC: 258) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00974]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 259/264 -128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 -Epoch 260/264 -128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 -Epoch 261/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 -Epoch 262/264 -128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 -Epoch 263/264 -128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 -Epoch 264/264 -128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.3485 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.12 sec -Time taken for epoch(SUBo): 252.93 sec -Time taken for epoch(OTHERo): 73.19 sec -<---------------------------------------|Epoch [44] END|---------------------------------------> - -Epoch: 45/486 (TSEC: 264) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00968]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 265/270 -128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 -Epoch 266/270 -128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 -Epoch 267/270 -128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 -Epoch 268/270 -128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 -Epoch 269/270 -128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 -Epoch 270/270 -128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9535 -Model Test loss: 0.2324 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.56 sec -Time taken for epoch(SUBo): 254.39 sec -Time taken for epoch(OTHERo): 72.17 sec -<---------------------------------------|Epoch [45] END|---------------------------------------> - -Epoch: 46/486 (TSEC: 270) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00962]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 271/276 -128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 -Epoch 272/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 -Epoch 273/276 -128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 -Epoch 274/276 -128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 -Epoch 275/276 -128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 -Epoch 276/276 -128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3108 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.76 sec -Time taken for epoch(SUBo): 254.60 sec -Time taken for epoch(OTHERo): 72.16 sec -<---------------------------------------|Epoch [46] END|---------------------------------------> - -Epoch: 47/486 (TSEC: 276) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00956]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 277/282 -128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 -Epoch 278/282 -128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 -Epoch 279/282 -128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 -Epoch 280/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 -Epoch 281/282 -128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 -Epoch 282/282 -128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3641 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 326.51 sec -Time taken for epoch(SUBo): 253.63 sec -Time taken for epoch(OTHERo): 72.88 sec -<---------------------------------------|Epoch [47] END|---------------------------------------> - -Epoch: 48/486 (TSEC: 282) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0095]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 283/288 -128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 -Epoch 284/288 -128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 -Epoch 285/288 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 -Epoch 286/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 -Epoch 287/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 -Epoch 288/288 -128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8958 -Model Test loss: 0.7104 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.17 sec -Time taken for epoch(SUBo): 255.62 sec -Time taken for epoch(OTHERo): 74.55 sec -<---------------------------------------|Epoch [48] END|---------------------------------------> - -Epoch: 49/486 (TSEC: 288) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00944]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 289/294 -128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 -Epoch 290/294 -128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 -Epoch 291/294 -128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 -Epoch 292/294 -128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 -Epoch 293/294 -128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 -Epoch 294/294 -128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9183 -Model Test loss: 0.5959 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 330.11 sec -Time taken for epoch(SUBo): 254.80 sec -Time taken for epoch(OTHERo): 75.32 sec -<---------------------------------------|Epoch [49] END|---------------------------------------> - -Epoch: 50/486 (TSEC: 294) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00938]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 295/300 -128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 -Epoch 296/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 -Epoch 297/300 -128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 -Epoch 298/300 -128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 -Epoch 299/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 -Epoch 300/300 -128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4308 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 331.10 sec -Time taken for epoch(SUBo): 255.03 sec -Time taken for epoch(OTHERo): 76.07 sec -<---------------------------------------|Epoch [50] END|---------------------------------------> - -Epoch: 51/486 (TSEC: 300) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00932]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 301/306 -128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 -Epoch 302/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 -Epoch 303/306 -128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 -Epoch 304/306 -128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 -Epoch 305/306 -128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 -Epoch 306/306 -128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.8974 -Model Test loss: 0.7052 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 362.92 sec -Time taken for epoch(SUBo): 286.09 sec -Time taken for epoch(OTHERo): 76.83 sec -<---------------------------------------|Epoch [51] END|---------------------------------------> - -Epoch: 52/486 (TSEC: 306) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00926]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 307/312 -128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 -Epoch 308/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 -Epoch 309/312 -128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 -Epoch 310/312 -128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 -Epoch 311/312 -128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 -Epoch 312/312 -128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9135 -Model Test loss: 0.5680 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.05 sec -Time taken for epoch(SUBo): 284.61 sec -Time taken for epoch(OTHERo): 95.44 sec -<---------------------------------------|Epoch [52] END|---------------------------------------> - -Epoch: 53/486 (TSEC: 312) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0092]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 313/318 -128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 -Epoch 314/318 -128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 -Epoch 315/318 -128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 -Epoch 316/318 -128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 -Epoch 317/318 -128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 -Epoch 318/318 -128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4632 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.68 sec -Time taken for epoch(SUBo): 286.71 sec -Time taken for epoch(OTHERo): 93.97 sec -<---------------------------------------|Epoch [53] END|---------------------------------------> - -Epoch: 54/486 (TSEC: 318) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00914]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 319/324 -128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 -Epoch 320/324 -128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 -Epoch 321/324 -128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 -Epoch 322/324 -128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 -Epoch 323/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 -Epoch 324/324 -128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5101 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 389.45 sec -Time taken for epoch(SUBo): 287.64 sec -Time taken for epoch(OTHERo): 101.81 sec -<---------------------------------------|Epoch [54] END|---------------------------------------> - -Epoch: 55/486 (TSEC: 324) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00908]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 325/330 -128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 -Epoch 326/330 -128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 -Epoch 327/330 -128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 -Epoch 328/330 -128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 -Epoch 329/330 -128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 -Epoch 330/330 -128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3231 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 380.47 sec -Time taken for epoch(SUBo): 281.24 sec -Time taken for epoch(OTHERo): 99.23 sec -<---------------------------------------|Epoch [55] END|---------------------------------------> - -Epoch: 56/486 (TSEC: 330) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00902]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 331/336 -128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 -Epoch 332/336 -128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 -Epoch 333/336 -128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 -Epoch 334/336 -128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 -Epoch 335/336 -128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 -Epoch 336/336 -128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3479 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 392.43 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 103.65 sec -<---------------------------------------|Epoch [56] END|---------------------------------------> - -Epoch: 57/486 (TSEC: 336) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00896]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 337/342 -128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 -Epoch 338/342 -128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 -Epoch 339/342 -128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 -Epoch 340/342 -128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 -Epoch 341/342 -128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 -Epoch 342/342 -128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.4083 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 387.98 sec -Time taken for epoch(SUBo): 285.68 sec -Time taken for epoch(OTHERo): 102.30 sec -<---------------------------------------|Epoch [57] END|---------------------------------------> - -Epoch: 58/486 (TSEC: 342) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0089]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 343/348 -128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 -Epoch 344/348 -128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 -Epoch 345/348 -128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 -Epoch 346/348 -128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 -Epoch 347/348 -128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 -Epoch 348/348 -128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3776 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 350.83 sec -Time taken for epoch(SUBo): 268.69 sec -Time taken for epoch(OTHERo): 82.14 sec -<---------------------------------------|Epoch [58] END|---------------------------------------> - -Epoch: 59/486 (TSEC: 348) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00884]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 349/354 -128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 -Epoch 350/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 -Epoch 351/354 -128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 -Epoch 352/354 -128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 -Epoch 353/354 -128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 -Epoch 354/354 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4066 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 353.60 sec -Time taken for epoch(SUBo): 258.60 sec -Time taken for epoch(OTHERo): 95.01 sec -<---------------------------------------|Epoch [59] END|---------------------------------------> - -Epoch: 60/486 (TSEC: 354) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00878]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 355/360 -128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 -Epoch 356/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 -Epoch 357/360 -128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 -Epoch 358/360 -128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 -Epoch 359/360 -128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 -Epoch 360/360 -128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4142 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 346.87 sec -Time taken for epoch(SUBo): 257.34 sec -Time taken for epoch(OTHERo): 89.53 sec -<---------------------------------------|Epoch [60] END|---------------------------------------> - -Epoch: 61/486 (TSEC: 360) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00872]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 361/366 -128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 -Epoch 362/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 -Epoch 363/366 -128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 -Epoch 364/366 -128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 -Epoch 365/366 -128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 -Epoch 366/366 -128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.7276 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.90 sec -Time taken for epoch(SUBo): 264.37 sec -Time taken for epoch(OTHERo): 77.53 sec -<---------------------------------------|Epoch [61] END|---------------------------------------> - -Epoch: 62/486 (TSEC: 366) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00866]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 367/372 -128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 -Epoch 368/372 -128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 -Epoch 369/372 -128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 -Epoch 370/372 -128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 -Epoch 371/372 -128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 -Epoch 372/372 -128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.2727 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 344.05 sec -Time taken for epoch(SUBo): 266.44 sec -Time taken for epoch(OTHERo): 77.61 sec -<---------------------------------------|Epoch [62] END|---------------------------------------> - -Epoch: 63/486 (TSEC: 372) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0086]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 373/378 -128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 -Epoch 374/378 -128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 -Epoch 375/378 -128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 -Epoch 376/378 -128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 -Epoch 377/378 -128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 -Epoch 378/378 -128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4211 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 334.11 sec -Time taken for epoch(SUBo): 258.37 sec -Time taken for epoch(OTHERo): 75.73 sec -<---------------------------------------|Epoch [63] END|---------------------------------------> - -Epoch: 64/486 (TSEC: 378) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 -Setting training OneCycleLr::maxlr to [0.00854]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 379/384 -128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 -Epoch 380/384 -128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 -Epoch 381/384 -128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 -Epoch 382/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 -Epoch 383/384 -128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 -Epoch 384/384 -128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3692 -Model accuracy did not improve from 0.9663461446762085. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 356.90 sec -Time taken for epoch(SUBo): 259.87 sec -Time taken for epoch(OTHERo): 97.03 sec -<---------------------------------------|Epoch [64] END|---------------------------------------> - -Epoch: 65/486 (TSEC: 384) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00848]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 385/390 -128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 -Epoch 386/390 -128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 -Epoch 387/390 -128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 -Epoch 388/390 -128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 -Epoch 389/390 -128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 -Epoch 390/390 -128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1823 -Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.22 sec -Time taken for epoch(SUBo): 257.30 sec -Time taken for epoch(OTHERo): 83.93 sec -<---------------------------------------|Epoch [65] END|---------------------------------------> - -Epoch: 66/486 (TSEC: 390) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00842]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 391/396 -128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 -Epoch 392/396 -128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 -Epoch 393/396 -128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 -Epoch 394/396 -128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 -Epoch 395/396 -128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 -Epoch 396/396 -128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9551 -Model Test loss: 0.2462 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 340.59 sec -Time taken for epoch(SUBo): 259.99 sec -Time taken for epoch(OTHERo): 80.59 sec -<---------------------------------------|Epoch [66] END|---------------------------------------> - -Epoch: 67/486 (TSEC: 396) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00836]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 397/402 -128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 -Epoch 398/402 -128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 -Epoch 399/402 -128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 -Epoch 400/402 -128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 -Epoch 401/402 -128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 -Epoch 402/402 -128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2736 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 348.32 sec -Time taken for epoch(SUBo): 267.55 sec -Time taken for epoch(OTHERo): 80.77 sec -<---------------------------------------|Epoch [67] END|---------------------------------------> - -Epoch: 68/486 (TSEC: 402) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0083]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 403/408 -128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 -Epoch 404/408 -128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 -Epoch 405/408 -128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 -Epoch 406/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 -Epoch 407/408 -128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 -Epoch 408/408 -128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.2983 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 374.47 sec -Time taken for epoch(SUBo): 276.39 sec -Time taken for epoch(OTHERo): 98.08 sec -<---------------------------------------|Epoch [68] END|---------------------------------------> - -Epoch: 69/486 (TSEC: 408) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00824]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 409/414 -128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 -Epoch 410/414 -128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 -Epoch 411/414 -128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 -Epoch 412/414 -128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 -Epoch 413/414 -128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 -Epoch 414/414 -128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3477 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 343.05 sec -Time taken for epoch(SUBo): 260.05 sec -Time taken for epoch(OTHERo): 83.00 sec -<---------------------------------------|Epoch [69] END|---------------------------------------> - -Epoch: 70/486 (TSEC: 414) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00818]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 415/420 -128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 -Epoch 416/420 -128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 -Epoch 417/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 -Epoch 418/420 -128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 -Epoch 419/420 -128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 -Epoch 420/420 -128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9167 -Model Test loss: 0.4541 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 342.13 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 79.85 sec -<---------------------------------------|Epoch [70] END|---------------------------------------> - -Epoch: 71/486 (TSEC: 420) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00812]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 421/426 -128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 -Epoch 422/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 -Epoch 423/426 -128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 -Epoch 424/426 -128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 -Epoch 425/426 -128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 -Epoch 426/426 -128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2110 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Model loss did not improve from 0.15437141060829163. Not saving model. -Time taken for epoch(FULL): 341.68 sec -Time taken for epoch(SUBo): 260.39 sec -Time taken for epoch(OTHERo): 81.29 sec -<---------------------------------------|Epoch [71] END|---------------------------------------> - -Epoch: 72/486 (TSEC: 426) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00806]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 427/432 -128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 -Epoch 428/432 -128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 -Epoch 429/432 -128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 -Epoch 430/432 -128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 -Epoch 431/432 -128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 -Epoch 432/432 -128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... -Model Test acc: 0.9615 -Model Test loss: 0.1528 -Model accuracy did not improve from 0.9679487347602844. Not saving model. -Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 353.28 sec -Time taken for epoch(SUBo): 265.48 sec -Time taken for epoch(OTHERo): 87.80 sec -<---------------------------------------|Epoch [72] END|---------------------------------------> - -Epoch: 73/486 (TSEC: 432) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.008]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 433/438 -128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 -Epoch 434/438 -128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 -Epoch 435/438 -128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 -Epoch 436/438 -128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 -Epoch 437/438 -128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 -Epoch 438/438 -128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1676 -Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. -Saving full model H5 format... -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.79 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 111.40 sec -<---------------------------------------|Epoch [73] END|---------------------------------------> - -Epoch: 74/486 (TSEC: 438) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00794]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 439/444 -128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 -Epoch 440/444 -128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 -Epoch 441/444 -128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 -Epoch 442/444 -128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 -Epoch 443/444 -128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 -Epoch 444/444 -128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.3061 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.10 sec -Time taken for epoch(SUBo): 288.78 sec -Time taken for epoch(OTHERo): 108.32 sec -<---------------------------------------|Epoch [74] END|---------------------------------------> - -Epoch: 75/486 (TSEC: 444) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00788]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 445/450 -128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 -Epoch 446/450 -128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 -Epoch 447/450 -128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 -Epoch 448/450 -128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 -Epoch 449/450 -128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 -Epoch 450/450 -128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... -Model Test acc: 0.9647 -Model Test loss: 0.2149 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 397.95 sec -Time taken for epoch(SUBo): 289.40 sec -Time taken for epoch(OTHERo): 108.55 sec -<---------------------------------------|Epoch [75] END|---------------------------------------> - -Epoch: 76/486 (TSEC: 450) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00782]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 451/456 -128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 -Epoch 452/456 -128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 -Epoch 453/456 -128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 -Epoch 454/456 -128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 -Epoch 455/456 -128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 -Epoch 456/456 -128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1664 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 400.35 sec -Time taken for epoch(SUBo): 290.41 sec -Time taken for epoch(OTHERo): 109.94 sec -<---------------------------------------|Epoch [76] END|---------------------------------------> - -Epoch: 77/486 (TSEC: 456) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00776]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 457/462 -128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 -Epoch 458/462 -128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 -Epoch 459/462 -128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 -Epoch 460/462 -128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 -Epoch 461/462 -128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 -Epoch 462/462 -128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... -Model Test acc: 0.9696 -Model Test loss: 0.1549 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.15280155837535858. Not saving model. -Time taken for epoch(FULL): 394.70 sec -Time taken for epoch(SUBo): 289.87 sec -Time taken for epoch(OTHERo): 104.83 sec -<---------------------------------------|Epoch [77] END|---------------------------------------> - -Epoch: 78/486 (TSEC: 462) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0077]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 463/468 -128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 -Epoch 464/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 -Epoch 465/468 -128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 -Epoch 466/468 -128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 -Epoch 467/468 -128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 -Epoch 468/468 -128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1365 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 398.75 sec -Time taken for epoch(SUBo): 289.42 sec -Time taken for epoch(OTHERo): 109.33 sec -<---------------------------------------|Epoch [78] END|---------------------------------------> - -Epoch: 79/486 (TSEC: 468) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00764]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 469/474 -128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 -Epoch 470/474 -128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 -Epoch 471/474 -128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 -Epoch 472/474 -128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 -Epoch 473/474 -128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 -Epoch 474/474 -128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2348 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 395.44 sec -Time taken for epoch(SUBo): 291.06 sec -Time taken for epoch(OTHERo): 104.39 sec -<---------------------------------------|Epoch [79] END|---------------------------------------> - -Epoch: 80/486 (TSEC: 474) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00758]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 475/480 -128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 -Epoch 476/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 -Epoch 477/480 -128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 -Epoch 478/480 -128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 -Epoch 479/480 -128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 -Epoch 480/480 -128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9583 -Model Test loss: 0.2441 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 399.87 sec -Time taken for epoch(SUBo): 293.34 sec -Time taken for epoch(OTHERo): 106.54 sec -<---------------------------------------|Epoch [80] END|---------------------------------------> - -Epoch: 81/486 (TSEC: 480) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00752]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 481/486 -128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 -Epoch 482/486 -128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 -Epoch 483/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 -Epoch 484/486 -128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 -Epoch 485/486 -128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 -Epoch 486/486 -128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3567 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.57 sec -Time taken for epoch(SUBo): 258.36 sec -Time taken for epoch(OTHERo): 102.21 sec -<---------------------------------------|Epoch [81] END|---------------------------------------> - -Epoch: 82/486 (TSEC: 486) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00746]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 487/492 -128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 -Epoch 488/492 -128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 -Epoch 489/492 -128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 -Epoch 490/492 -128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 -Epoch 491/492 -128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 -Epoch 492/492 -128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2877 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 338.30 sec -Time taken for epoch(SUBo): 256.81 sec -Time taken for epoch(OTHERo): 81.49 sec -<---------------------------------------|Epoch [82] END|---------------------------------------> - -Epoch: 83/486 (TSEC: 492) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0074]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 493/498 -128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 -Epoch 494/498 -128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 -Epoch 495/498 -128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 -Epoch 496/498 -128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 -Epoch 497/498 -128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 -Epoch 498/498 -128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3008 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 341.92 sec -Time taken for epoch(SUBo): 260.54 sec -Time taken for epoch(OTHERo): 81.38 sec -<---------------------------------------|Epoch [83] END|---------------------------------------> - -Epoch: 84/486 (TSEC: 498) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00734]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 499/504 -128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 -Epoch 500/504 -128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 -Epoch 501/504 -128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 -Epoch 502/504 -128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 -Epoch 503/504 -128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 -Epoch 504/504 -128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3369 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 401.59 sec -Time taken for epoch(SUBo): 296.36 sec -Time taken for epoch(OTHERo): 105.23 sec -<---------------------------------------|Epoch [84] END|---------------------------------------> - -Epoch: 85/486 (TSEC: 504) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00728]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 505/510 -128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 -Epoch 506/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 -Epoch 507/510 -128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 -Epoch 508/510 -128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 -Epoch 509/510 -128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 -Epoch 510/510 -128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.2355 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 378.93 sec -Time taken for epoch(SUBo): 271.88 sec -Time taken for epoch(OTHERo): 107.05 sec -<---------------------------------------|Epoch [85] END|---------------------------------------> - -Epoch: 86/486 (TSEC: 510) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00722]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 511/516 -128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 -Epoch 512/516 -128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 -Epoch 513/516 -128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 -Epoch 514/516 -128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 -Epoch 515/516 -128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 -Epoch 516/516 -128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9567 -Model Test loss: 0.2163 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 348.35 sec -Time taken for epoch(SUBo): 264.53 sec -Time taken for epoch(OTHERo): 83.82 sec -<---------------------------------------|Epoch [86] END|---------------------------------------> - -Epoch: 87/486 (TSEC: 516) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00716]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 517/522 -128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 -Epoch 518/522 -128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 -Epoch 519/522 -128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 -Epoch 520/522 -128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 -Epoch 521/522 -128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 -Epoch 522/522 -128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9519 -Model Test loss: 0.3130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.32 sec -Time taken for epoch(SUBo): 265.09 sec -Time taken for epoch(OTHERo): 84.23 sec -<---------------------------------------|Epoch [87] END|---------------------------------------> - -Epoch: 88/486 (TSEC: 522) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0071]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 523/528 -128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 -Epoch 524/528 -128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 -Epoch 525/528 -128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 -Epoch 526/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 -Epoch 527/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 -Epoch 528/528 -128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3755 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.31 sec -Time taken for epoch(SUBo): 260.67 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [88] END|---------------------------------------> - -Epoch: 89/486 (TSEC: 528) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00704]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 529/534 -128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 -Epoch 530/534 -128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 -Epoch 531/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 -Epoch 532/534 -128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 -Epoch 533/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 -Epoch 534/534 -128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1451 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 344.88 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 83.03 sec -<---------------------------------------|Epoch [89] END|---------------------------------------> - -Epoch: 90/486 (TSEC: 534) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00698]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 535/540 -128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 -Epoch 536/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 -Epoch 537/540 -128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 -Epoch 538/540 -128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 -Epoch 539/540 -128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 -Epoch 540/540 -128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2261 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 376.02 sec -Time taken for epoch(SUBo): 290.31 sec -Time taken for epoch(OTHERo): 85.71 sec -<---------------------------------------|Epoch [90] END|---------------------------------------> - -Epoch: 91/486 (TSEC: 540) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00692]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 541/546 -128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 -Epoch 542/546 -128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 -Epoch 543/546 -128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 -Epoch 544/546 -128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 -Epoch 545/546 -128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 -Epoch 546/546 -128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.2810 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 409.06 sec -Time taken for epoch(SUBo): 293.27 sec -Time taken for epoch(OTHERo): 115.79 sec -<---------------------------------------|Epoch [91] END|---------------------------------------> - -Epoch: 92/486 (TSEC: 546) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00686]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 547/552 -128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 -Epoch 548/552 -128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 -Epoch 549/552 -128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 -Epoch 550/552 -128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 -Epoch 551/552 -128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 -Epoch 552/552 -128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9487 -Model Test loss: 0.2734 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 402.95 sec -Time taken for epoch(SUBo): 289.90 sec -Time taken for epoch(OTHERo): 113.04 sec -<---------------------------------------|Epoch [92] END|---------------------------------------> - -Epoch: 93/486 (TSEC: 552) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0068]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 553/558 -128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 -Epoch 554/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 -Epoch 555/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 -Epoch 556/558 -128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 -Epoch 557/558 -128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 -Epoch 558/558 -128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4314 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 343.82 sec -Time taken for epoch(SUBo): 258.19 sec -Time taken for epoch(OTHERo): 85.63 sec -<---------------------------------------|Epoch [93] END|---------------------------------------> - -Epoch: 94/486 (TSEC: 558) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00674]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 559/564 -128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 -Epoch 560/564 -128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 -Epoch 561/564 -128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 -Epoch 562/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 -Epoch 563/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 -Epoch 564/564 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2974 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.85 sec -Time taken for epoch(SUBo): 261.88 sec -Time taken for epoch(OTHERo): 85.97 sec -<---------------------------------------|Epoch [94] END|---------------------------------------> - -Epoch: 95/486 (TSEC: 564) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00668]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 565/570 -128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 -Epoch 566/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 -Epoch 567/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 -Epoch 568/570 -128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 -Epoch 569/570 -128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 -Epoch 570/570 -128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.4469 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 349.57 sec -Time taken for epoch(SUBo): 260.42 sec -Time taken for epoch(OTHERo): 89.15 sec -<---------------------------------------|Epoch [95] END|---------------------------------------> - -Epoch: 96/486 (TSEC: 570) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 -Setting training OneCycleLr::maxlr to [0.00662]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 571/576 -128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 -Epoch 572/576 -128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 -Epoch 573/576 -128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 -Epoch 574/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 -Epoch 575/576 -128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 -Epoch 576/576 -128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3027 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 360.90 sec -Time taken for epoch(SUBo): 261.28 sec -Time taken for epoch(OTHERo): 99.62 sec -<---------------------------------------|Epoch [96] END|---------------------------------------> - -Epoch: 97/486 (TSEC: 576) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00656]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 577/582 -128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 -Epoch 578/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 -Epoch 579/582 -128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 -Epoch 580/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 -Epoch 581/582 -128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 -Epoch 582/582 -128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3618 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.27 sec -Time taken for epoch(SUBo): 261.85 sec -Time taken for epoch(OTHERo): 84.42 sec -<---------------------------------------|Epoch [97] END|---------------------------------------> - -Epoch: 98/486 (TSEC: 582) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0065]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 583/588 -128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 -Epoch 584/588 -128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 -Epoch 585/588 -128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 -Epoch 586/588 -128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 -Epoch 587/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 -Epoch 588/588 -128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.3557 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.51 sec -Time taken for epoch(SUBo): 262.33 sec -Time taken for epoch(OTHERo): 83.18 sec -<---------------------------------------|Epoch [98] END|---------------------------------------> - -Epoch: 99/486 (TSEC: 588) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00644]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 589/594 -128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 -Epoch 590/594 -128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 -Epoch 591/594 -128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 -Epoch 592/594 -128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 -Epoch 593/594 -128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 -Epoch 594/594 -128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9391 -Model Test loss: 0.3592 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.37 sec -Time taken for epoch(SUBo): 262.28 sec -Time taken for epoch(OTHERo): 85.09 sec -<---------------------------------------|Epoch [99] END|---------------------------------------> - -Epoch: 100/486 (TSEC: 594) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00638]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 595/600 -128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 -Epoch 596/600 -128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 -Epoch 597/600 -128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 -Epoch 598/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 -Epoch 599/600 -128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 -Epoch 600/600 -128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9471 -Model Test loss: 0.3042 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 345.54 sec -Time taken for epoch(SUBo): 263.28 sec -Time taken for epoch(OTHERo): 82.26 sec -<---------------------------------------|Epoch [100] END|---------------------------------------> - -Epoch: 101/486 (TSEC: 600) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00632]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 601/606 -128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 -Epoch 602/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 -Epoch 603/606 -128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 -Epoch 604/606 -128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 605/606 -128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 -Epoch 606/606 -128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.1757 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 347.57 sec -Time taken for epoch(SUBo): 261.73 sec -Time taken for epoch(OTHERo): 85.84 sec -<---------------------------------------|Epoch [101] END|---------------------------------------> - -Epoch: 102/486 (TSEC: 606) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00626]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 607/612 -128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 -Epoch 608/612 -128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 -Epoch 609/612 -128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 -Epoch 610/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 -Epoch 611/612 -128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 -Epoch 612/612 -128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.4659 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 350.48 sec -Time taken for epoch(SUBo): 263.62 sec -Time taken for epoch(OTHERo): 86.85 sec -<---------------------------------------|Epoch [102] END|---------------------------------------> - -Epoch: 103/486 (TSEC: 612) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0062]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 613/618 -128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 -Epoch 614/618 -128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 -Epoch 615/618 -128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 -Epoch 616/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 -Epoch 617/618 -128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 -Epoch 618/618 -128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3278 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 356.91 sec -Time taken for epoch(SUBo): 264.67 sec -Time taken for epoch(OTHERo): 92.23 sec -<---------------------------------------|Epoch [103] END|---------------------------------------> - -Epoch: 104/486 (TSEC: 618) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00614]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 619/624 -128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 -Epoch 620/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 -Epoch 621/624 -128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 -Epoch 622/624 -128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 -Epoch 623/624 -128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 -Epoch 624/624 -128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.4053 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 346.90 sec -Time taken for epoch(SUBo): 260.79 sec -Time taken for epoch(OTHERo): 86.11 sec -<---------------------------------------|Epoch [104] END|---------------------------------------> - -Epoch: 105/486 (TSEC: 624) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00608]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 625/630 -128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 -Epoch 626/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 -Epoch 627/630 -128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 -Epoch 628/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 -Epoch 629/630 -128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 -Epoch 630/630 -128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9631 -Model Test loss: 0.2148 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.13646124303340912. Not saving model. -Time taken for epoch(FULL): 353.04 sec -Time taken for epoch(SUBo): 264.40 sec -Time taken for epoch(OTHERo): 88.64 sec -<---------------------------------------|Epoch [105] END|---------------------------------------> - -Epoch: 106/486 (TSEC: 630) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00602]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 631/636 -128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 -Epoch 632/636 -128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 -Epoch 633/636 -128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 -Epoch 634/636 -128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 -Epoch 635/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 -Epoch 636/636 -128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 -Subset training done. -Loading the best weights... -Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... -Model Test acc: 0.9679 -Model Test loss: 0.1188 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. -Saving full model H5 format... -Time taken for epoch(FULL): 356.65 sec -Time taken for epoch(SUBo): 263.16 sec -Time taken for epoch(OTHERo): 93.49 sec -<---------------------------------------|Epoch [106] END|---------------------------------------> - -Epoch: 107/486 (TSEC: 636) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00596]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 637/642 -128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 -Epoch 638/642 -128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 -Epoch 639/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 -Epoch 640/642 -128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 -Epoch 641/642 -128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 -Epoch 642/642 -128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2388 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 353.97 sec -Time taken for epoch(SUBo): 260.86 sec -Time taken for epoch(OTHERo): 93.11 sec -<---------------------------------------|Epoch [107] END|---------------------------------------> - -Epoch: 108/486 (TSEC: 642) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0059]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 643/648 -128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 -Epoch 644/648 -128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 -Epoch 645/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 -Epoch 646/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 -Epoch 647/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 -Epoch 648/648 -128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9455 -Model Test loss: 0.2612 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 351.69 sec -Time taken for epoch(SUBo): 260.60 sec -Time taken for epoch(OTHERo): 91.10 sec -<---------------------------------------|Epoch [108] END|---------------------------------------> - -Epoch: 109/486 (TSEC: 648) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00584]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 649/654 -128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 -Epoch 650/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 -Epoch 651/654 -128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 -Epoch 652/654 -128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 -Epoch 653/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 -Epoch 654/654 -128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.3569 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 346.32 sec -Time taken for epoch(SUBo): 259.69 sec -Time taken for epoch(OTHERo): 86.63 sec -<---------------------------------------|Epoch [109] END|---------------------------------------> - -Epoch: 110/486 (TSEC: 654) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00578]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 655/660 -128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 -Epoch 656/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 -Epoch 657/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 -Epoch 658/660 -128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 -Epoch 659/660 -128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 -Epoch 660/660 -128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3959 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 347.66 sec -Time taken for epoch(SUBo): 261.10 sec -Time taken for epoch(OTHERo): 86.56 sec -<---------------------------------------|Epoch [110] END|---------------------------------------> - -Epoch: 111/486 (TSEC: 660) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00572]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 661/666 -128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 -Epoch 662/666 -128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 -Epoch 663/666 -128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 -Epoch 664/666 -128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 -Epoch 665/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 -Epoch 666/666 -128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2614 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 374.77 sec -Time taken for epoch(SUBo): 287.07 sec -Time taken for epoch(OTHERo): 87.70 sec -<---------------------------------------|Epoch [111] END|---------------------------------------> - -Epoch: 112/486 (TSEC: 666) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00566]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 667/672 -128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 -Epoch 668/672 -128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 -Epoch 669/672 -128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 -Epoch 670/672 -128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 -Epoch 671/672 -128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 -Epoch 672/672 -128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.2773 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.29 sec -Time taken for epoch(SUBo): 294.69 sec -Time taken for epoch(OTHERo): 108.60 sec -<---------------------------------------|Epoch [112] END|---------------------------------------> - -Epoch: 113/486 (TSEC: 672) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0056]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 673/678 -128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 -Epoch 674/678 -128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 -Epoch 675/678 -128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 -Epoch 676/678 -128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 -Epoch 677/678 -128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 -Epoch 678/678 -128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3138 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 405.22 sec -Time taken for epoch(SUBo): 290.78 sec -Time taken for epoch(OTHERo): 114.43 sec -<---------------------------------------|Epoch [113] END|---------------------------------------> - -Epoch: 114/486 (TSEC: 678) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00554]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 679/684 -128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 -Epoch 680/684 -128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 -Epoch 681/684 -128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 -Epoch 682/684 -128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 -Epoch 683/684 -128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 -Epoch 684/684 -128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9615 -Model Test loss: 0.2130 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.12 sec -Time taken for epoch(SUBo): 294.80 sec -Time taken for epoch(OTHERo): 117.31 sec -<---------------------------------------|Epoch [114] END|---------------------------------------> - -Epoch: 115/486 (TSEC: 684) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00548]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 685/690 -128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 -Epoch 686/690 -128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 -Epoch 687/690 -128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 -Epoch 688/690 -128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 -Epoch 689/690 -128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 -Epoch 690/690 -128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.4240 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.16 sec -Time taken for epoch(SUBo): 291.71 sec -Time taken for epoch(OTHERo): 110.46 sec -<---------------------------------------|Epoch [115] END|---------------------------------------> - -Epoch: 116/486 (TSEC: 690) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00542]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 691/696 -128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 -Epoch 692/696 -128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 -Epoch 693/696 -128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 -Epoch 694/696 -128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 -Epoch 695/696 -128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 -Epoch 696/696 -128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3796 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.58 sec -Time taken for epoch(SUBo): 295.23 sec -Time taken for epoch(OTHERo): 113.35 sec -<---------------------------------------|Epoch [116] END|---------------------------------------> - -Epoch: 117/486 (TSEC: 696) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00536]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 697/702 -128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 -Epoch 698/702 -128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 -Epoch 699/702 -128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 -Epoch 700/702 -128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 -Epoch 701/702 -128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 -Epoch 702/702 -128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3449 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.03 sec -Time taken for epoch(SUBo): 295.99 sec -Time taken for epoch(OTHERo): 115.05 sec -<---------------------------------------|Epoch [117] END|---------------------------------------> - -Epoch: 118/486 (TSEC: 702) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0053]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 703/708 -128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 -Epoch 704/708 -128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 -Epoch 705/708 -128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 -Epoch 706/708 -128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 -Epoch 707/708 -128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 -Epoch 708/708 -128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9503 -Model Test loss: 0.2279 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.63 sec -Time taken for epoch(SUBo): 296.34 sec -Time taken for epoch(OTHERo): 117.29 sec -<---------------------------------------|Epoch [118] END|---------------------------------------> - -Epoch: 119/486 (TSEC: 708) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00524]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 709/714 -128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 -Epoch 710/714 -128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 -Epoch 711/714 -128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 -Epoch 712/714 -128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 -Epoch 713/714 -128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 -Epoch 714/714 -128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3194 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.60 sec -Time taken for epoch(SUBo): 294.03 sec -Time taken for epoch(OTHERo): 114.57 sec -<---------------------------------------|Epoch [119] END|---------------------------------------> - -Epoch: 120/486 (TSEC: 714) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00518]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 715/720 -128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 -Epoch 716/720 -128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 -Epoch 717/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 -Epoch 718/720 -128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 -Epoch 719/720 -128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 -Epoch 720/720 -128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.5143 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 408.26 sec -Time taken for epoch(SUBo): 295.66 sec -Time taken for epoch(OTHERo): 112.60 sec -<---------------------------------------|Epoch [120] END|---------------------------------------> - -Epoch: 121/486 (TSEC: 720) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00512]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 721/726 -128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 -Epoch 722/726 -128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 -Epoch 723/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 -Epoch 724/726 -128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 -Epoch 725/726 -128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 -Epoch 726/726 -128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9231 -Model Test loss: 0.4177 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.06 sec -Time taken for epoch(SUBo): 295.42 sec -Time taken for epoch(OTHERo): 118.64 sec -<---------------------------------------|Epoch [121] END|---------------------------------------> - -Epoch: 122/486 (TSEC: 726) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00506]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 727/732 -128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 -Epoch 728/732 -128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 -Epoch 729/732 -128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 -Epoch 730/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 -Epoch 731/732 -128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 -Epoch 732/732 -128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9327 -Model Test loss: 0.4436 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.37 sec -Time taken for epoch(SUBo): 293.85 sec -Time taken for epoch(OTHERo): 117.52 sec -<---------------------------------------|Epoch [122] END|---------------------------------------> - -Epoch: 123/486 (TSEC: 732) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.005]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 733/738 -128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 -Epoch 734/738 -128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 -Epoch 735/738 -128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 -Epoch 736/738 -128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 -Epoch 737/738 -128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 -Epoch 738/738 -128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4303 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 412.96 sec -Time taken for epoch(SUBo): 294.39 sec -Time taken for epoch(OTHERo): 118.57 sec -<---------------------------------------|Epoch [123] END|---------------------------------------> - -Epoch: 124/486 (TSEC: 738) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00494]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 739/744 -128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 -Epoch 740/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 -Epoch 741/744 -128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 -Epoch 742/744 -128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 -Epoch 743/744 -128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 -Epoch 744/744 -128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4234 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.23 sec -Time taken for epoch(SUBo): 298.41 sec -Time taken for epoch(OTHERo): 114.83 sec -<---------------------------------------|Epoch [124] END|---------------------------------------> - -Epoch: 125/486 (TSEC: 744) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00488]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 745/750 -128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 -Epoch 746/750 -128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 -Epoch 747/750 -128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 -Epoch 748/750 -128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 -Epoch 749/750 -128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 -Epoch 750/750 -128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9423 -Model Test loss: 0.3677 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.29 sec -Time taken for epoch(SUBo): 293.29 sec -Time taken for epoch(OTHERo): 119.99 sec -<---------------------------------------|Epoch [125] END|---------------------------------------> - -Epoch: 126/486 (TSEC: 750) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00482]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 751/756 -128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 -Epoch 752/756 -128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 -Epoch 753/756 -128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 -Epoch 754/756 -128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 -Epoch 755/756 -128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 -Epoch 756/756 -128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3409 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.45 sec -Time taken for epoch(SUBo): 293.26 sec -Time taken for epoch(OTHERo): 110.19 sec -<---------------------------------------|Epoch [126] END|---------------------------------------> - -Epoch: 127/486 (TSEC: 756) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00476]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 757/762 -128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 -Epoch 758/762 -128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 -Epoch 759/762 -128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 -Epoch 760/762 -128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 -Epoch 761/762 -128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 -Epoch 762/762 -128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9359 -Model Test loss: 0.3632 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 403.89 sec -Time taken for epoch(SUBo): 291.93 sec -Time taken for epoch(OTHERo): 111.96 sec -<---------------------------------------|Epoch [127] END|---------------------------------------> - -Epoch: 128/486 (TSEC: 762) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -└───Shuffling data... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 -Setting training OneCycleLr::maxlr to [0.0047]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 763/768 -128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 -Epoch 764/768 -128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 -Epoch 765/768 -128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 -Epoch 766/768 -128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 -Epoch 767/768 -128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 -Epoch 768/768 -128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9343 -Model Test loss: 0.3065 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 425.95 sec -Time taken for epoch(SUBo): 291.59 sec -Time taken for epoch(OTHERo): 134.36 sec -<---------------------------------------|Epoch [128] END|---------------------------------------> - -Epoch: 129/486 (TSEC: 768) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00464]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 769/774 -128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 -Epoch 770/774 -128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 -Epoch 771/774 -128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 -Epoch 772/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 -Epoch 773/774 -128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 -Epoch 774/774 -128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9599 -Model Test loss: 0.2221 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.23 sec -Time taken for epoch(SUBo): 293.03 sec -Time taken for epoch(OTHERo): 109.20 sec -<---------------------------------------|Epoch [129] END|---------------------------------------> - -Epoch: 130/486 (TSEC: 774) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00458]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 775/780 -128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 -Epoch 776/780 -128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 -Epoch 777/780 -128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 -Epoch 778/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 -Epoch 779/780 -128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 -Epoch 780/780 -128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9439 -Model Test loss: 0.3315 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 402.71 sec -Time taken for epoch(SUBo): 293.90 sec -Time taken for epoch(OTHERo): 108.81 sec -<---------------------------------------|Epoch [130] END|---------------------------------------> - -Epoch: 131/486 (TSEC: 780) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00452]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 781/786 -128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 -Epoch 782/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 -Epoch 783/786 -128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 -Epoch 784/786 -128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 -Epoch 785/786 -128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 -Epoch 786/786 -128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9263 -Model Test loss: 0.5019 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 424.42 sec -Time taken for epoch(SUBo): 300.59 sec -Time taken for epoch(OTHERo): 123.83 sec -<---------------------------------------|Epoch [131] END|---------------------------------------> - -Epoch: 132/486 (TSEC: 786) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00446]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 787/792 -128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 -Epoch 788/792 -128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 -Epoch 789/792 -128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 -Epoch 790/792 -128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 -Epoch 791/792 -128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 -Epoch 792/792 -128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9215 -Model Test loss: 0.5337 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.90 sec -Time taken for epoch(SUBo): 299.61 sec -Time taken for epoch(OTHERo): 121.28 sec -<---------------------------------------|Epoch [132] END|---------------------------------------> - -Epoch: 133/486 (TSEC: 792) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0044]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 793/798 -128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 -Epoch 794/798 -128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 -Epoch 795/798 -128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 -Epoch 796/798 -128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 -Epoch 797/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 -Epoch 798/798 -128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9311 -Model Test loss: 0.5005 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.52 sec -Time taken for epoch(SUBo): 296.92 sec -Time taken for epoch(OTHERo): 120.59 sec -<---------------------------------------|Epoch [133] END|---------------------------------------> - -Epoch: 134/486 (TSEC: 798) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00434]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 799/804 -128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 -Epoch 800/804 -128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 -Epoch 801/804 -128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 -Epoch 802/804 -128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 -Epoch 803/804 -128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 -Epoch 804/804 -128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9151 -Model Test loss: 0.5460 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.80 sec -Time taken for epoch(SUBo): 297.98 sec -Time taken for epoch(OTHERo): 122.82 sec -<---------------------------------------|Epoch [134] END|---------------------------------------> - -Epoch: 135/486 (TSEC: 804) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00428]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 805/810 -128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 -Epoch 806/810 -128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 -Epoch 807/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 -Epoch 808/810 -128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 -Epoch 809/810 -128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 -Epoch 810/810 -128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9103 -Model Test loss: 0.5751 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 414.70 sec -Time taken for epoch(SUBo): 297.58 sec -Time taken for epoch(OTHERo): 117.13 sec -<---------------------------------------|Epoch [135] END|---------------------------------------> - -Epoch: 136/486 (TSEC: 810) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00422]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 811/816 -128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 -Epoch 812/816 -128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 -Epoch 813/816 -128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 -Epoch 814/816 -128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 -Epoch 815/816 -128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 -Epoch 816/816 -128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9375 -Model Test loss: 0.3819 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.58 sec -Time taken for epoch(SUBo): 300.30 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [136] END|---------------------------------------> - -Epoch: 137/486 (TSEC: 816) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00416]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 817/822 -128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 -Epoch 818/822 -128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 -Epoch 819/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 -Epoch 820/822 -128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 -Epoch 821/822 -128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 -Epoch 822/822 -128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.4434 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.54 sec -Time taken for epoch(SUBo): 297.62 sec -Time taken for epoch(OTHERo): 118.92 sec -<---------------------------------------|Epoch [137] END|---------------------------------------> - -Epoch: 138/486 (TSEC: 822) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0041]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 823/828 -128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 -Epoch 824/828 -128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 -Epoch 825/828 -128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 -Epoch 826/828 -128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 -Epoch 827/828 -128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 -Epoch 828/828 -128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.4551 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.46 sec -Time taken for epoch(SUBo): 297.55 sec -Time taken for epoch(OTHERo): 117.91 sec -<---------------------------------------|Epoch [138] END|---------------------------------------> - -Epoch: 139/486 (TSEC: 828) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00404]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 829/834 -128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 -Epoch 830/834 -128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 -Epoch 831/834 -128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 -Epoch 832/834 -128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 -Epoch 833/834 -128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 -Epoch 834/834 -128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9295 -Model Test loss: 0.5237 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 418.66 sec -Time taken for epoch(SUBo): 295.90 sec -Time taken for epoch(OTHERo): 122.76 sec -<---------------------------------------|Epoch [139] END|---------------------------------------> - -Epoch: 140/486 (TSEC: 834) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00398]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 835/840 -128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 -Epoch 836/840 -128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 -Epoch 837/840 -128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 -Epoch 838/840 -128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 -Epoch 839/840 -128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 -Epoch 840/840 -128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5710 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 420.43 sec -Time taken for epoch(SUBo): 298.12 sec -Time taken for epoch(OTHERo): 122.31 sec -<---------------------------------------|Epoch [140] END|---------------------------------------> - -Epoch: 141/486 (TSEC: 840) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00392]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 841/846 -128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 -Epoch 842/846 -128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 -Epoch 843/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 -Epoch 844/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 -Epoch 845/846 -128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 -Epoch 846/846 -128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9247 -Model Test loss: 0.5128 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 423.15 sec -Time taken for epoch(SUBo): 298.17 sec -Time taken for epoch(OTHERo): 124.98 sec -<---------------------------------------|Epoch [141] END|---------------------------------------> - -Epoch: 142/486 (TSEC: 846) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00386]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 847/852 -128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 -Epoch 848/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 -Epoch 849/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 -Epoch 850/852 -128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 -Epoch 851/852 -128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 -Epoch 852/852 -128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.5103 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.49 sec -Time taken for epoch(SUBo): 301.87 sec -Time taken for epoch(OTHERo): 114.61 sec -<---------------------------------------|Epoch [142] END|---------------------------------------> - -Epoch: 143/486 (TSEC: 852) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0038]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 853/858 -128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 -Epoch 854/858 -128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 -Epoch 855/858 -128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 -Epoch 856/858 -128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 -Epoch 857/858 -128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 -Epoch 858/858 -128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.7814 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.74 sec -Time taken for epoch(SUBo): 295.81 sec -Time taken for epoch(OTHERo): 113.94 sec -<---------------------------------------|Epoch [143] END|---------------------------------------> - -Epoch: 144/486 (TSEC: 858) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00374]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 859/864 -128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 -Epoch 860/864 -128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 -Epoch 861/864 -128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 -Epoch 862/864 -128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 -Epoch 863/864 -128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 -Epoch 864/864 -128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3838 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.79 sec -Time taken for epoch(SUBo): 295.17 sec -Time taken for epoch(OTHERo): 118.61 sec -<---------------------------------------|Epoch [144] END|---------------------------------------> - -Epoch: 145/486 (TSEC: 864) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00368]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 865/870 -128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 -Epoch 866/870 -128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 -Epoch 867/870 -128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 -Epoch 868/870 -128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 -Epoch 869/870 -128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 -Epoch 870/870 -128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3789 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 413.68 sec -Time taken for epoch(SUBo): 295.62 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [145] END|---------------------------------------> - -Epoch: 146/486 (TSEC: 870) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00362]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 871/876 -128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 -Epoch 872/876 -128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 -Epoch 873/876 -128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 -Epoch 874/876 -128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 -Epoch 875/876 -128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 -Epoch 876/876 -128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9071 -Model Test loss: 0.5834 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.30 sec -Time taken for epoch(SUBo): 298.45 sec -Time taken for epoch(OTHERo): 116.86 sec -<---------------------------------------|Epoch [146] END|---------------------------------------> - -Epoch: 147/486 (TSEC: 876) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00356]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 877/882 -128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 -Epoch 878/882 -128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 -Epoch 879/882 -128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 -Epoch 880/882 -128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 -Epoch 881/882 -128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 -Epoch 882/882 -128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.3896 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 417.42 sec -Time taken for epoch(SUBo): 297.44 sec -Time taken for epoch(OTHERo): 119.98 sec -<---------------------------------------|Epoch [147] END|---------------------------------------> - -Epoch: 148/486 (TSEC: 882) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.0035]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 883/888 -128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 -Epoch 884/888 -128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 -Epoch 885/888 -128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 -Epoch 886/888 -128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 -Epoch 887/888 -128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 -Epoch 888/888 -128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9199 -Model Test loss: 0.5339 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 411.09 sec -Time taken for epoch(SUBo): 293.02 sec -Time taken for epoch(OTHERo): 118.07 sec -<---------------------------------------|Epoch [148] END|---------------------------------------> - -Epoch: 149/486 (TSEC: 888) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00344]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 889/894 -128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 -Epoch 890/894 -128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 -Epoch 891/894 -128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 -Epoch 892/894 -128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 -Epoch 893/894 -128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 -Epoch 894/894 -128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9279 -Model Test loss: 0.4329 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 415.69 sec -Time taken for epoch(SUBo): 298.62 sec -Time taken for epoch(OTHERo): 117.07 sec -<---------------------------------------|Epoch [149] END|---------------------------------------> - -Epoch: 150/486 (TSEC: 894) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00338]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 895/900 -128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 -Epoch 896/900 -128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 -Epoch 897/900 -128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 -Epoch 898/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 -Epoch 899/900 -128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 -Epoch 900/900 -128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9087 -Model Test loss: 0.6034 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 409.43 sec -Time taken for epoch(SUBo): 292.15 sec -Time taken for epoch(OTHERo): 117.28 sec -<---------------------------------------|Epoch [150] END|---------------------------------------> - -Epoch: 151/486 (TSEC: 900) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00332]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 901/906 -128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 -Epoch 902/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 -Epoch 903/906 -128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 -Epoch 904/906 -128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 -Epoch 905/906 -128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 -Epoch 906/906 -128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 -Subset training done. -Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] -Model Test acc: 0.9407 -Model Test loss: 0.3160 -Model accuracy did not improve from 0.9695512652397156. Not saving model. -Model loss did not improve from 0.11880630999803543. Not saving model. -Time taken for epoch(FULL): 416.64 sec -Time taken for epoch(SUBo): 296.21 sec -Time taken for epoch(OTHERo): 120.43 sec -<---------------------------------------|Epoch [151] END|---------------------------------------> - -Epoch: 152/486 (TSEC: 906) | [Fine tuning] -Taking a subset of [|2048|AdvSubset:True]... -Preparing train data... -- Augmenting Image Data... -- Normalizing Image Data... -Setting training OneCycleLr::maxlr to [0.00326]... -Setting training subset epoch.c to [6]... -Training on subset... -Epoch 907/912 -128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 -Epoch 908/912 -128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 -Epoch 909/912 -128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 -Epoch 910/912 -128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 -Epoch 911/912 - 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 -KeyboardInterrupt. -Training done. - +Training the model... + +Setup Verbose: +Setting TensorBoard Log dir to [logs/fit/y2023_m12_d26-h05_m19_s58]... +Use_extended_tensorboard [False]. +Debug_OUTPUT_DPS [True]. +OneCycleLr_UFTS [False]. +Setup Verbose END. + +Epoch: 1/486 (TSEC: 0) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Fitting ImageDataGenerator... +- ImageDataGenerator fit done. +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h05_m26_s22 +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 1/6 +128/128 [==============================] - 60s 353ms/step - loss: 21.4322 - accuracy: 0.6172 - val_loss: 18.0983 - val_accuracy: 0.7260 +Epoch 2/6 +128/128 [==============================] - 42s 330ms/step - loss: 13.7766 - accuracy: 0.7368 - val_loss: 9.9862 - val_accuracy: 0.7740 +Epoch 3/6 +128/128 [==============================] - 42s 329ms/step - loss: 7.5493 - accuracy: 0.8096 - val_loss: 5.5326 - val_accuracy: 0.8926 +Epoch 4/6 +128/128 [==============================] - 42s 323ms/step - loss: 4.4263 - accuracy: 0.8643 - val_loss: 3.5763 - val_accuracy: 0.8173 +Epoch 5/6 +128/128 [==============================] - 42s 325ms/step - loss: 2.9461 - accuracy: 0.8999 - val_loss: 2.6104 - val_accuracy: 0.8894 +Epoch 6/6 +128/128 [==============================] - 42s 330ms/step - loss: 2.3881 - accuracy: 0.9272 - val_loss: 2.4019 - val_accuracy: 0.8974 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-006-0.8974.h5... +Model Test acc: 0.8974 +Model Test loss: 2.4019 +Improved model accuracy from 0 to 0.8974359035491943. Saving model. +Saving full model H5 format... +Improved model loss from inf to 2.4019267559051514. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 676.74 sec +Time taken for epoch(SUBo): 271.12 sec +Time taken for epoch(OTHERo): 405.62 sec +<---------------------------------------|Epoch [1] END|---------------------------------------> + +Epoch: 2/486 (TSEC: 6) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 7/12 +128/128 [==============================] - 48s 340ms/step - loss: 2.3521 - accuracy: 0.8696 - val_loss: 2.1558 - val_accuracy: 0.8029 +Epoch 8/12 +128/128 [==============================] - 42s 328ms/step - loss: 1.7436 - accuracy: 0.8691 - val_loss: 1.3484 - val_accuracy: 0.9295 +Epoch 9/12 +128/128 [==============================] - 41s 322ms/step - loss: 1.1746 - accuracy: 0.8804 - val_loss: 0.9656 - val_accuracy: 0.8926 +Epoch 10/12 +128/128 [==============================] - 41s 322ms/step - loss: 0.8446 - accuracy: 0.9155 - val_loss: 0.8035 - val_accuracy: 0.8702 +Epoch 11/12 +128/128 [==============================] - 41s 323ms/step - loss: 0.6384 - accuracy: 0.9253 - val_loss: 0.5933 - val_accuracy: 0.9071 +Epoch 12/12 +128/128 [==============================] - 43s 330ms/step - loss: 0.5399 - accuracy: 0.9409 - val_loss: 0.5406 - val_accuracy: 0.9407 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-012-0.9407.h5... +Model Test acc: 0.9407 +Model Test loss: 0.5406 +Improved model accuracy from 0.8974359035491943 to 0.9407051205635071. Saving model. +Saving full model H5 format... +Improved model loss from 2.4019267559051514 to 0.5405705571174622. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.91 sec +Time taken for epoch(SUBo): 257.59 sec +Time taken for epoch(OTHERo): 68.33 sec +<---------------------------------------|Epoch [2] END|---------------------------------------> + +Epoch: 3/486 (TSEC: 12) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 13/18 +128/128 [==============================] - 48s 339ms/step - loss: 0.6130 - accuracy: 0.8945 - val_loss: 0.4656 - val_accuracy: 0.9423 +Epoch 14/18 +128/128 [==============================] - 42s 322ms/step - loss: 0.5469 - accuracy: 0.8926 - val_loss: 0.5696 - val_accuracy: 0.9247 +Epoch 15/18 +128/128 [==============================] - 41s 323ms/step - loss: 0.4341 - accuracy: 0.9053 - val_loss: 0.7678 - val_accuracy: 0.8958 +Epoch 16/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.3669 - accuracy: 0.9160 - val_loss: 0.5045 - val_accuracy: 0.9135 +Epoch 17/18 +128/128 [==============================] - 42s 323ms/step - loss: 0.2699 - accuracy: 0.9492 - val_loss: 0.3521 - val_accuracy: 0.9247 +Epoch 18/18 +128/128 [==============================] - 41s 322ms/step - loss: 0.2419 - accuracy: 0.9541 - val_loss: 0.3128 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-013-0.9423.h5... +Model Test acc: 0.9423 +Model Test loss: 0.4656 +Improved model accuracy from 0.9407051205635071 to 0.942307710647583. Saving model. +Saving full model H5 format... +Improved model loss from 0.5405705571174622 to 0.4656426012516022. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 324.58 sec +Time taken for epoch(SUBo): 255.82 sec +Time taken for epoch(OTHERo): 68.76 sec +<---------------------------------------|Epoch [3] END|---------------------------------------> + +Epoch: 4/486 (TSEC: 18) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 19/24 +128/128 [==============================] - 47s 338ms/step - loss: 0.5786 - accuracy: 0.8955 - val_loss: 0.5133 - val_accuracy: 0.9263 +Epoch 20/24 +128/128 [==============================] - 42s 329ms/step - loss: 0.5153 - accuracy: 0.8911 - val_loss: 0.4089 - val_accuracy: 0.9343 +Epoch 21/24 +128/128 [==============================] - 42s 323ms/step - loss: 0.4315 - accuracy: 0.9023 - val_loss: 0.4206 - val_accuracy: 0.9199 +Epoch 22/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.3518 - accuracy: 0.9209 - val_loss: 0.3816 - val_accuracy: 0.9263 +Epoch 23/24 +128/128 [==============================] - 41s 321ms/step - loss: 0.2963 - accuracy: 0.9268 - val_loss: 0.3045 - val_accuracy: 0.9327 +Epoch 24/24 +128/128 [==============================] - 42s 324ms/step - loss: 0.2433 - accuracy: 0.9473 - val_loss: 0.3747 - val_accuracy: 0.8894 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-020-0.9343.h5... +Model Test acc: 0.9343 +Model Test loss: 0.4089 +Model accuracy did not improve from 0.942307710647583. Not saving model. +Improved model loss from 0.4656426012516022 to 0.40894174575805664. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 323.62 sec +Time taken for epoch(SUBo): 256.60 sec +Time taken for epoch(OTHERo): 67.02 sec +<---------------------------------------|Epoch [4] END|---------------------------------------> + +Epoch: 5/486 (TSEC: 24) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 25/30 +128/128 [==============================] - 48s 339ms/step - loss: 0.4736 - accuracy: 0.8926 - val_loss: 0.4157 - val_accuracy: 0.9054 +Epoch 26/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.4237 - accuracy: 0.8965 - val_loss: 0.3027 - val_accuracy: 0.9407 +Epoch 27/30 +128/128 [==============================] - 42s 330ms/step - loss: 0.3685 - accuracy: 0.9121 - val_loss: 0.2557 - val_accuracy: 0.9455 +Epoch 28/30 +128/128 [==============================] - 42s 325ms/step - loss: 0.2824 - accuracy: 0.9282 - val_loss: 0.2802 - val_accuracy: 0.9439 +Epoch 29/30 +128/128 [==============================] - 42s 329ms/step - loss: 0.2481 - accuracy: 0.9355 - val_loss: 0.2338 - val_accuracy: 0.9519 +Epoch 30/30 +128/128 [==============================] - 42s 323ms/step - loss: 0.1852 - accuracy: 0.9556 - val_loss: 0.2495 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-029-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.2338 +Improved model accuracy from 0.942307710647583 to 0.9519230723381042. Saving model. +Saving full model H5 format... +Improved model loss from 0.40894174575805664 to 0.23381969332695007. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.89 sec +Time taken for epoch(SUBo): 258.52 sec +Time taken for epoch(OTHERo): 67.37 sec +<---------------------------------------|Epoch [5] END|---------------------------------------> + +Epoch: 6/486 (TSEC: 30) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 31/36 +128/128 [==============================] - 48s 339ms/step - loss: 0.3385 - accuracy: 0.9058 - val_loss: 0.2388 - val_accuracy: 0.9471 +Epoch 32/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.3076 - accuracy: 0.9092 - val_loss: 0.2625 - val_accuracy: 0.9439 +Epoch 33/36 +128/128 [==============================] - 42s 329ms/step - loss: 0.2696 - accuracy: 0.9126 - val_loss: 0.2253 - val_accuracy: 0.9487 +Epoch 34/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2354 - accuracy: 0.9233 - val_loss: 0.2049 - val_accuracy: 0.9311 +Epoch 35/36 +128/128 [==============================] - 41s 322ms/step - loss: 0.2178 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9391 +Epoch 36/36 +128/128 [==============================] - 41s 321ms/step - loss: 0.1883 - accuracy: 0.9453 - val_loss: 0.1936 - val_accuracy: 0.9455 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-033-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2253 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.23381969332695007 to 0.2253303825855255. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 321.73 sec +Time taken for epoch(SUBo): 256.17 sec +Time taken for epoch(OTHERo): 65.57 sec +<---------------------------------------|Epoch [6] END|---------------------------------------> + +Epoch: 7/486 (TSEC: 36) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 37/42 +128/128 [==============================] - 48s 339ms/step - loss: 0.3160 - accuracy: 0.8926 - val_loss: 0.1995 - val_accuracy: 0.9439 +Epoch 38/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2871 - accuracy: 0.9043 - val_loss: 0.1912 - val_accuracy: 0.9455 +Epoch 39/42 +128/128 [==============================] - 42s 324ms/step - loss: 0.2617 - accuracy: 0.9136 - val_loss: 0.4363 - val_accuracy: 0.9215 +Epoch 40/42 +128/128 [==============================] - 42s 330ms/step - loss: 0.2206 - accuracy: 0.9365 - val_loss: 0.1801 - val_accuracy: 0.9471 +Epoch 41/42 +128/128 [==============================] - 41s 323ms/step - loss: 0.1992 - accuracy: 0.9414 - val_loss: 0.3309 - val_accuracy: 0.9439 +Epoch 42/42 +128/128 [==============================] - 43s 332ms/step - loss: 0.1552 - accuracy: 0.9551 - val_loss: 0.2070 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-042-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2070 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Improved model loss from 0.2253303825855255 to 0.20697814226150513. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 326.03 sec +Time taken for epoch(SUBo): 259.27 sec +Time taken for epoch(OTHERo): 66.76 sec +<---------------------------------------|Epoch [7] END|---------------------------------------> + +Epoch: 8/486 (TSEC: 42) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 43/48 +128/128 [==============================] - 48s 341ms/step - loss: 0.2665 - accuracy: 0.9146 - val_loss: 0.2199 - val_accuracy: 0.9503 +Epoch 44/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2612 - accuracy: 0.9155 - val_loss: 0.1724 - val_accuracy: 0.9439 +Epoch 45/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2281 - accuracy: 0.9268 - val_loss: 0.2323 - val_accuracy: 0.9215 +Epoch 46/48 +128/128 [==============================] - 42s 324ms/step - loss: 0.2221 - accuracy: 0.9404 - val_loss: 0.2246 - val_accuracy: 0.9375 +Epoch 47/48 +128/128 [==============================] - 41s 323ms/step - loss: 0.1874 - accuracy: 0.9424 - val_loss: 0.1997 - val_accuracy: 0.9439 +Epoch 48/48 +128/128 [==============================] - 42s 323ms/step - loss: 0.1315 - accuracy: 0.9648 - val_loss: 0.2674 - val_accuracy: 0.9375 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-043-0.9503.h5... +Model Test acc: 0.9503 +Model Test loss: 0.2199 +Model accuracy did not improve from 0.9519230723381042. Not saving model. +Model loss did not improve from 0.20697814226150513. Not saving model. +Time taken for epoch(FULL): 322.67 sec +Time taken for epoch(SUBo): 256.59 sec +Time taken for epoch(OTHERo): 66.08 sec +<---------------------------------------|Epoch [8] END|---------------------------------------> + +Epoch: 9/486 (TSEC: 48) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 49/54 +128/128 [==============================] - 48s 341ms/step - loss: 0.2678 - accuracy: 0.9072 - val_loss: 0.2143 - val_accuracy: 0.9487 +Epoch 50/54 +128/128 [==============================] - 43s 331ms/step - loss: 0.2609 - accuracy: 0.9111 - val_loss: 0.1662 - val_accuracy: 0.9535 +Epoch 51/54 +128/128 [==============================] - 42s 324ms/step - loss: 0.2169 - accuracy: 0.9370 - val_loss: 0.3990 - val_accuracy: 0.9054 +Epoch 52/54 +128/128 [==============================] - 42s 325ms/step - loss: 0.1766 - accuracy: 0.9453 - val_loss: 0.2543 - val_accuracy: 0.9471 +Epoch 53/54 +128/128 [==============================] - 42s 323ms/step - loss: 0.1618 - accuracy: 0.9556 - val_loss: 0.1851 - val_accuracy: 0.9519 +Epoch 54/54 +128/128 [==============================] - 41s 323ms/step - loss: 0.1481 - accuracy: 0.9629 - val_loss: 0.2174 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-050-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.1662 +Improved model accuracy from 0.9519230723381042 to 0.9535256624221802. Saving model. +Saving full model H5 format... +Improved model loss from 0.20697814226150513 to 0.16622641682624817. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 327.90 sec +Time taken for epoch(SUBo): 257.53 sec +Time taken for epoch(OTHERo): 70.37 sec +<---------------------------------------|Epoch [9] END|---------------------------------------> + +Epoch: 10/486 (TSEC: 54) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 55/60 +128/128 [==============================] - 48s 342ms/step - loss: 0.2663 - accuracy: 0.9058 - val_loss: 0.2130 - val_accuracy: 0.9439 +Epoch 56/60 +128/128 [==============================] - 43s 334ms/step - loss: 0.2433 - accuracy: 0.9194 - val_loss: 0.2421 - val_accuracy: 0.9519 +Epoch 57/60 +128/128 [==============================] - 42s 326ms/step - loss: 0.2127 - accuracy: 0.9282 - val_loss: 0.1974 - val_accuracy: 0.9343 +Epoch 58/60 +128/128 [==============================] - 43s 333ms/step - loss: 0.2225 - accuracy: 0.9326 - val_loss: 0.2059 - val_accuracy: 0.9535 +Epoch 59/60 +128/128 [==============================] - 42s 327ms/step - loss: 0.1613 - accuracy: 0.9556 - val_loss: 0.1992 - val_accuracy: 0.9487 +Epoch 60/60 +128/128 [==============================] - 42s 325ms/step - loss: 0.1382 - accuracy: 0.9663 - val_loss: 0.2249 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-058-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2059 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 327.86 sec +Time taken for epoch(SUBo): 259.66 sec +Time taken for epoch(OTHERo): 68.20 sec +<---------------------------------------|Epoch [10] END|---------------------------------------> + +Epoch: 11/486 (TSEC: 60) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 61/66 +128/128 [==============================] - 48s 341ms/step - loss: 0.2918 - accuracy: 0.9048 - val_loss: 0.2938 - val_accuracy: 0.9487 +Epoch 62/66 +128/128 [==============================] - 42s 323ms/step - loss: 0.2444 - accuracy: 0.9248 - val_loss: 0.3003 - val_accuracy: 0.9471 +Epoch 63/66 +128/128 [==============================] - 42s 324ms/step - loss: 0.2027 - accuracy: 0.9380 - val_loss: 0.2087 - val_accuracy: 0.9487 +Epoch 64/66 +128/128 [==============================] - 42s 325ms/step - loss: 0.1887 - accuracy: 0.9370 - val_loss: 0.2348 - val_accuracy: 0.9391 +Epoch 65/66 +128/128 [==============================] - 42s 327ms/step - loss: 0.1461 - accuracy: 0.9595 - val_loss: 0.2043 - val_accuracy: 0.9487 +Epoch 66/66 +128/128 [==============================] - 42s 326ms/step - loss: 0.1483 - accuracy: 0.9580 - val_loss: 0.1955 - val_accuracy: 0.9391 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-061-0.9487.h5... +Model Test acc: 0.9487 +Model Test loss: 0.2938 +Model accuracy did not improve from 0.9535256624221802. Not saving model. +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 257.49 sec +Time taken for epoch(OTHERo): 69.06 sec +<---------------------------------------|Epoch [11] END|---------------------------------------> + +Epoch: 12/486 (TSEC: 66) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 67/72 +128/128 [==============================] - 47s 334ms/step - loss: 0.2553 - accuracy: 0.9106 - val_loss: 0.1993 - val_accuracy: 0.9535 +Epoch 68/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.2569 - accuracy: 0.9229 - val_loss: 0.3983 - val_accuracy: 0.9471 +Epoch 69/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.2162 - accuracy: 0.9355 - val_loss: 0.1895 - val_accuracy: 0.9567 +Epoch 70/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1894 - accuracy: 0.9365 - val_loss: 0.2424 - val_accuracy: 0.9567 +Epoch 71/72 +128/128 [==============================] - 42s 326ms/step - loss: 0.1500 - accuracy: 0.9541 - val_loss: 0.2115 - val_accuracy: 0.9631 +Epoch 72/72 +128/128 [==============================] - 41s 317ms/step - loss: 0.1237 - accuracy: 0.9609 - val_loss: 0.2145 - val_accuracy: 0.9599 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-071-0.9631.h5... +Model Test acc: 0.9631 +Model Test loss: 0.2115 +Improved model accuracy from 0.9535256624221802 to 0.9631410241127014. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.16622641682624817. Not saving model. +Time taken for epoch(FULL): 324.68 sec +Time taken for epoch(SUBo): 253.65 sec +Time taken for epoch(OTHERo): 71.03 sec +<---------------------------------------|Epoch [12] END|---------------------------------------> + +Epoch: 13/486 (TSEC: 72) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 73/78 +128/128 [==============================] - 47s 332ms/step - loss: 0.2653 - accuracy: 0.9106 - val_loss: 0.1676 - val_accuracy: 0.9599 +Epoch 74/78 +128/128 [==============================] - 41s 317ms/step - loss: 0.2379 - accuracy: 0.9141 - val_loss: 0.2634 - val_accuracy: 0.9567 +Epoch 75/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.2388 - accuracy: 0.9287 - val_loss: 0.1944 - val_accuracy: 0.9551 +Epoch 76/78 +128/128 [==============================] - 41s 315ms/step - loss: 0.1933 - accuracy: 0.9404 - val_loss: 0.3442 - val_accuracy: 0.9439 +Epoch 77/78 +128/128 [==============================] - 42s 325ms/step - loss: 0.1803 - accuracy: 0.9482 - val_loss: 0.1545 - val_accuracy: 0.9647 +Epoch 78/78 +128/128 [==============================] - 41s 316ms/step - loss: 0.1348 - accuracy: 0.9658 - val_loss: 0.1778 - val_accuracy: 0.9583 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-077-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.1545 +Improved model accuracy from 0.9631410241127014 to 0.9647436141967773. Saving model. +Saving full model H5 format... +Improved model loss from 0.16622641682624817 to 0.1544923484325409. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 325.97 sec +Time taken for epoch(SUBo): 251.55 sec +Time taken for epoch(OTHERo): 74.42 sec +<---------------------------------------|Epoch [13] END|---------------------------------------> + +Epoch: 14/486 (TSEC: 78) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 79/84 +128/128 [==============================] - 47s 336ms/step - loss: 0.2421 - accuracy: 0.9253 - val_loss: 0.2244 - val_accuracy: 0.9359 +Epoch 80/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.2232 - accuracy: 0.9204 - val_loss: 0.2063 - val_accuracy: 0.9535 +Epoch 81/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.2236 - accuracy: 0.9268 - val_loss: 0.3691 - val_accuracy: 0.9359 +Epoch 82/84 +128/128 [==============================] - 42s 324ms/step - loss: 0.1919 - accuracy: 0.9463 - val_loss: 0.1780 - val_accuracy: 0.9599 +Epoch 83/84 +128/128 [==============================] - 41s 317ms/step - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.2085 - val_accuracy: 0.9567 +Epoch 84/84 +128/128 [==============================] - 41s 318ms/step - loss: 0.1203 - accuracy: 0.9702 - val_loss: 0.3022 - val_accuracy: 0.9503 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-082-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1780 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 71.59 sec +<---------------------------------------|Epoch [14] END|---------------------------------------> + +Epoch: 15/486 (TSEC: 84) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 85/90 +128/128 [==============================] - 47s 333ms/step - loss: 0.2522 - accuracy: 0.9180 - val_loss: 0.2090 - val_accuracy: 0.9487 +Epoch 86/90 +128/128 [==============================] - 41s 316ms/step - loss: 0.2577 - accuracy: 0.9121 - val_loss: 0.3674 - val_accuracy: 0.9327 +Epoch 87/90 +128/128 [==============================] - 40s 315ms/step - loss: 0.2290 - accuracy: 0.9243 - val_loss: 0.5777 - val_accuracy: 0.8926 +Epoch 88/90 +128/128 [==============================] - 41s 317ms/step - loss: 0.1968 - accuracy: 0.9419 - val_loss: 0.2299 - val_accuracy: 0.9327 +Epoch 89/90 +128/128 [==============================] - 42s 325ms/step - loss: 0.1391 - accuracy: 0.9575 - val_loss: 0.1810 - val_accuracy: 0.9535 +Epoch 90/90 +128/128 [==============================] - 42s 324ms/step - loss: 0.1325 - accuracy: 0.9692 - val_loss: 0.2233 - val_accuracy: 0.9615 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-090-0.9615.h5... +Model Test acc: 0.9615 +Model Test loss: 0.2233 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.17 sec +Time taken for epoch(SUBo): 252.81 sec +Time taken for epoch(OTHERo): 70.36 sec +<---------------------------------------|Epoch [15] END|---------------------------------------> + +Epoch: 16/486 (TSEC: 90) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 91/96 +128/128 [==============================] - 47s 331ms/step - loss: 0.2332 - accuracy: 0.9258 - val_loss: 0.1648 - val_accuracy: 0.9599 +Epoch 92/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2297 - accuracy: 0.9263 - val_loss: 0.5232 - val_accuracy: 0.8990 +Epoch 93/96 +128/128 [==============================] - 40s 315ms/step - loss: 0.1736 - accuracy: 0.9434 - val_loss: 0.2227 - val_accuracy: 0.9583 +Epoch 94/96 +128/128 [==============================] - 40s 314ms/step - loss: 0.2072 - accuracy: 0.9395 - val_loss: 0.2290 - val_accuracy: 0.9519 +Epoch 95/96 +128/128 [==============================] - 41s 317ms/step - loss: 0.1595 - accuracy: 0.9546 - val_loss: 0.3474 - val_accuracy: 0.9311 +Epoch 96/96 +128/128 [==============================] - 41s 314ms/step - loss: 0.1284 - accuracy: 0.9663 - val_loss: 0.2498 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-091-0.9599.h5... +Model Test acc: 0.9599 +Model Test loss: 0.1648 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 319.96 sec +Time taken for epoch(SUBo): 249.52 sec +Time taken for epoch(OTHERo): 70.43 sec +<---------------------------------------|Epoch [16] END|---------------------------------------> + +Epoch: 17/486 (TSEC: 96) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 97/102 +128/128 [==============================] - 47s 336ms/step - loss: 0.2118 - accuracy: 0.9268 - val_loss: 0.3481 - val_accuracy: 0.9311 +Epoch 98/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.2079 - accuracy: 0.9331 - val_loss: 0.6189 - val_accuracy: 0.9135 +Epoch 99/102 +128/128 [==============================] - 41s 318ms/step - loss: 0.1801 - accuracy: 0.9473 - val_loss: 0.4662 - val_accuracy: 0.9022 +Epoch 100/102 +128/128 [==============================] - 42s 324ms/step - loss: 0.1659 - accuracy: 0.9565 - val_loss: 0.1764 - val_accuracy: 0.9519 +Epoch 101/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.1411 - accuracy: 0.9590 - val_loss: 0.2718 - val_accuracy: 0.9471 +Epoch 102/102 +128/128 [==============================] - 41s 319ms/step - loss: 0.0904 - accuracy: 0.9785 - val_loss: 0.2405 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-100-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1764 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.46 sec +Time taken for epoch(SUBo): 253.14 sec +Time taken for epoch(OTHERo): 67.31 sec +<---------------------------------------|Epoch [17] END|---------------------------------------> + +Epoch: 18/486 (TSEC: 102) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 103/108 +128/128 [==============================] - 47s 334ms/step - loss: 0.2261 - accuracy: 0.9233 - val_loss: 0.3131 - val_accuracy: 0.9423 +Epoch 104/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.2091 - accuracy: 0.9326 - val_loss: 0.3381 - val_accuracy: 0.9423 +Epoch 105/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1950 - accuracy: 0.9404 - val_loss: 0.3162 - val_accuracy: 0.9391 +Epoch 106/108 +128/128 [==============================] - 42s 327ms/step - loss: 0.1762 - accuracy: 0.9419 - val_loss: 0.2677 - val_accuracy: 0.9535 +Epoch 107/108 +128/128 [==============================] - 41s 320ms/step - loss: 0.1234 - accuracy: 0.9634 - val_loss: 0.3080 - val_accuracy: 0.9423 +Epoch 108/108 +128/128 [==============================] - 41s 318ms/step - loss: 0.1114 - accuracy: 0.9688 - val_loss: 0.2260 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-106-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2677 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.64 sec +Time taken for epoch(SUBo): 253.71 sec +Time taken for epoch(OTHERo): 70.93 sec +<---------------------------------------|Epoch [18] END|---------------------------------------> + +Epoch: 19/486 (TSEC: 108) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 109/114 +128/128 [==============================] - 47s 334ms/step - loss: 0.2336 - accuracy: 0.9258 - val_loss: 0.4601 - val_accuracy: 0.9439 +Epoch 110/114 +128/128 [==============================] - 41s 317ms/step - loss: 0.2186 - accuracy: 0.9312 - val_loss: 0.2426 - val_accuracy: 0.9343 +Epoch 111/114 +128/128 [==============================] - 41s 316ms/step - loss: 0.2075 - accuracy: 0.9395 - val_loss: 0.2122 - val_accuracy: 0.9439 +Epoch 112/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1843 - accuracy: 0.9521 - val_loss: 0.2533 - val_accuracy: 0.9471 +Epoch 113/114 +128/128 [==============================] - 42s 325ms/step - loss: 0.1317 - accuracy: 0.9644 - val_loss: 0.2055 - val_accuracy: 0.9535 +Epoch 114/114 +128/128 [==============================] - 41s 315ms/step - loss: 0.0992 - accuracy: 0.9775 - val_loss: 0.2684 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-113-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2055 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.02 sec +Time taken for epoch(SUBo): 253.02 sec +Time taken for epoch(OTHERo): 69.00 sec +<---------------------------------------|Epoch [19] END|---------------------------------------> + +Epoch: 20/486 (TSEC: 114) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 115/120 +128/128 [==============================] - 47s 334ms/step - loss: 0.2283 - accuracy: 0.9282 - val_loss: 0.3171 - val_accuracy: 0.9119 +Epoch 116/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.2118 - accuracy: 0.9272 - val_loss: 0.4551 - val_accuracy: 0.8638 +Epoch 117/120 +128/128 [==============================] - 42s 325ms/step - loss: 0.1832 - accuracy: 0.9458 - val_loss: 0.3367 - val_accuracy: 0.9439 +Epoch 118/120 +128/128 [==============================] - 41s 317ms/step - loss: 0.1470 - accuracy: 0.9580 - val_loss: 0.3322 - val_accuracy: 0.9407 +Epoch 119/120 +128/128 [==============================] - 41s 319ms/step - loss: 0.1070 - accuracy: 0.9712 - val_loss: 0.4984 - val_accuracy: 0.9022 +Epoch 120/120 +128/128 [==============================] - 41s 316ms/step - loss: 0.0964 - accuracy: 0.9692 - val_loss: 0.3933 - val_accuracy: 0.9279 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-117-0.9439.h5... +Model Test acc: 0.9439 +Model Test loss: 0.3367 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 252.69 sec +Time taken for epoch(OTHERo): 70.57 sec +<---------------------------------------|Epoch [20] END|---------------------------------------> + +Epoch: 21/486 (TSEC: 120) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 121/126 +128/128 [==============================] - 47s 333ms/step - loss: 0.2310 - accuracy: 0.9229 - val_loss: 0.2885 - val_accuracy: 0.9567 +Epoch 122/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.2252 - accuracy: 0.9263 - val_loss: 0.2842 - val_accuracy: 0.9487 +Epoch 123/126 +128/128 [==============================] - 41s 317ms/step - loss: 0.1919 - accuracy: 0.9404 - val_loss: 0.1730 - val_accuracy: 0.9503 +Epoch 124/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1539 - accuracy: 0.9556 - val_loss: 0.1640 - val_accuracy: 0.9535 +Epoch 125/126 +128/128 [==============================] - 42s 325ms/step - loss: 0.1327 - accuracy: 0.9619 - val_loss: 0.2373 - val_accuracy: 0.9583 +Epoch 126/126 +128/128 [==============================] - 41s 318ms/step - loss: 0.1144 - accuracy: 0.9707 - val_loss: 0.2522 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-125-0.9583.h5... +Model Test acc: 0.9583 +Model Test loss: 0.2373 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.10 sec +Time taken for epoch(SUBo): 252.57 sec +Time taken for epoch(OTHERo): 68.53 sec +<---------------------------------------|Epoch [21] END|---------------------------------------> + +Epoch: 22/486 (TSEC: 126) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 127/132 +128/128 [==============================] - 47s 334ms/step - loss: 0.1927 - accuracy: 0.9429 - val_loss: 0.2540 - val_accuracy: 0.8942 +Epoch 128/132 +128/128 [==============================] - 41s 322ms/step - loss: 0.2146 - accuracy: 0.9321 - val_loss: 0.1895 - val_accuracy: 0.9455 +Epoch 129/132 +128/128 [==============================] - 40s 315ms/step - loss: 0.1757 - accuracy: 0.9424 - val_loss: 0.2458 - val_accuracy: 0.9439 +Epoch 130/132 +128/128 [==============================] - 42s 324ms/step - loss: 0.1391 - accuracy: 0.9644 - val_loss: 0.2035 - val_accuracy: 0.9535 +Epoch 131/132 +128/128 [==============================] - 41s 317ms/step - loss: 0.1071 - accuracy: 0.9741 - val_loss: 0.2042 - val_accuracy: 0.9455 +Epoch 132/132 +128/128 [==============================] - 41s 316ms/step - loss: 0.0805 - accuracy: 0.9795 - val_loss: 0.2279 - val_accuracy: 0.9471 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-130-0.9535.h5... +Model Test acc: 0.9535 +Model Test loss: 0.2035 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 321.92 sec +Time taken for epoch(SUBo): 252.61 sec +Time taken for epoch(OTHERo): 69.31 sec +<---------------------------------------|Epoch [22] END|---------------------------------------> + +Epoch: 23/486 (TSEC: 132) | [Learning the patterns] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.011]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 133/138 +128/128 [==============================] - 47s 331ms/step - loss: 0.2042 - accuracy: 0.9365 - val_loss: 0.1930 - val_accuracy: 0.9423 +Epoch 134/138 +128/128 [==============================] - 42s 323ms/step - loss: 0.1992 - accuracy: 0.9385 - val_loss: 0.1983 - val_accuracy: 0.9519 +Epoch 135/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.1650 - accuracy: 0.9556 - val_loss: 0.2616 - val_accuracy: 0.9487 +Epoch 136/138 +128/128 [==============================] - 40s 314ms/step - loss: 0.1399 - accuracy: 0.9624 - val_loss: 0.2525 - val_accuracy: 0.9503 +Epoch 137/138 +128/128 [==============================] - 40s 315ms/step - loss: 0.1090 - accuracy: 0.9736 - val_loss: 0.2941 - val_accuracy: 0.9519 +Epoch 138/138 +128/128 [==============================] - 41s 316ms/step - loss: 0.0715 - accuracy: 0.9839 - val_loss: 0.1802 - val_accuracy: 0.9519 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-134-0.9519.h5... +Model Test acc: 0.9519 +Model Test loss: 0.1983 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.26 sec +Time taken for epoch(SUBo): 251.30 sec +Time taken for epoch(OTHERo): 71.96 sec +<---------------------------------------|Epoch [23] END|---------------------------------------> + +Epoch: 24/486 (TSEC: 138) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01094]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 139/144 +128/128 [==============================] - 47s 334ms/step - loss: 0.2203 - accuracy: 0.9331 - val_loss: 0.3238 - val_accuracy: 0.9439 +Epoch 140/144 +128/128 [==============================] - 41s 323ms/step - loss: 0.1929 - accuracy: 0.9434 - val_loss: 0.2415 - val_accuracy: 0.9567 +Epoch 141/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.1600 - accuracy: 0.9580 - val_loss: 0.1929 - val_accuracy: 0.9551 +Epoch 142/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2914 - val_accuracy: 0.9487 +Epoch 143/144 +128/128 [==============================] - 41s 316ms/step - loss: 0.1083 - accuracy: 0.9761 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 144/144 +128/128 [==============================] - 41s 317ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2451 - val_accuracy: 0.9535 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-140-0.9567.h5... +Model Test acc: 0.9567 +Model Test loss: 0.2415 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 324.37 sec +Time taken for epoch(SUBo): 251.97 sec +Time taken for epoch(OTHERo): 72.40 sec +<---------------------------------------|Epoch [24] END|---------------------------------------> + +Epoch: 25/486 (TSEC: 144) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01088]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 145/150 +128/128 [==============================] - 47s 333ms/step - loss: 0.2265 - accuracy: 0.9297 - val_loss: 0.1848 - val_accuracy: 0.9503 +Epoch 146/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1751 - accuracy: 0.9409 - val_loss: 0.3971 - val_accuracy: 0.9375 +Epoch 147/150 +128/128 [==============================] - 41s 317ms/step - loss: 0.1699 - accuracy: 0.9478 - val_loss: 0.5504 - val_accuracy: 0.8750 +Epoch 148/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.1346 - accuracy: 0.9629 - val_loss: 0.3018 - val_accuracy: 0.9423 +Epoch 149/150 +128/128 [==============================] - 41s 315ms/step - loss: 0.1057 - accuracy: 0.9751 - val_loss: 0.3112 - val_accuracy: 0.9487 +Epoch 150/150 +128/128 [==============================] - 41s 316ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.2961 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2961 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.24 sec +Time taken for epoch(SUBo): 250.77 sec +Time taken for epoch(OTHERo): 69.47 sec +<---------------------------------------|Epoch [25] END|---------------------------------------> + +Epoch: 26/486 (TSEC: 150) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01082]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 151/156 +128/128 [==============================] - 47s 336ms/step - loss: 0.2059 - accuracy: 0.9336 - val_loss: 0.3040 - val_accuracy: 0.9487 +Epoch 152/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1910 - accuracy: 0.9351 - val_loss: 0.3500 - val_accuracy: 0.9311 +Epoch 153/156 +128/128 [==============================] - 41s 317ms/step - loss: 0.1830 - accuracy: 0.9458 - val_loss: 0.2815 - val_accuracy: 0.9455 +Epoch 154/156 +128/128 [==============================] - 42s 323ms/step - loss: 0.1320 - accuracy: 0.9634 - val_loss: 0.2612 - val_accuracy: 0.9519 +Epoch 155/156 +128/128 [==============================] - 42s 325ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2607 - val_accuracy: 0.9551 +Epoch 156/156 +128/128 [==============================] - 41s 318ms/step - loss: 0.0676 - accuracy: 0.9824 - val_loss: 0.2054 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2054 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.50 sec +Time taken for epoch(SUBo): 253.89 sec +Time taken for epoch(OTHERo): 68.61 sec +<---------------------------------------|Epoch [26] END|---------------------------------------> + +Epoch: 27/486 (TSEC: 156) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01076]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 157/162 +128/128 [==============================] - 47s 334ms/step - loss: 0.2030 - accuracy: 0.9370 - val_loss: 0.3111 - val_accuracy: 0.9519 +Epoch 158/162 +128/128 [==============================] - 41s 323ms/step - loss: 0.1620 - accuracy: 0.9517 - val_loss: 0.4831 - val_accuracy: 0.9535 +Epoch 159/162 +128/128 [==============================] - 41s 318ms/step - loss: 0.1655 - accuracy: 0.9492 - val_loss: 0.3814 - val_accuracy: 0.8974 +Epoch 160/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.3127 - val_accuracy: 0.9487 +Epoch 161/162 +128/128 [==============================] - 42s 326ms/step - loss: 0.0898 - accuracy: 0.9771 - val_loss: 0.2725 - val_accuracy: 0.9551 +Epoch 162/162 +128/128 [==============================] - 41s 317ms/step - loss: 0.0683 - accuracy: 0.9878 - val_loss: 0.2812 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2812 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.25 sec +Time taken for epoch(SUBo): 253.57 sec +Time taken for epoch(OTHERo): 69.69 sec +<---------------------------------------|Epoch [27] END|---------------------------------------> + +Epoch: 28/486 (TSEC: 162) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0107]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 163/168 +128/128 [==============================] - 47s 336ms/step - loss: 0.1883 - accuracy: 0.9419 - val_loss: 0.2668 - val_accuracy: 0.9439 +Epoch 164/168 +128/128 [==============================] - 42s 324ms/step - loss: 0.1696 - accuracy: 0.9404 - val_loss: 0.2142 - val_accuracy: 0.9535 +Epoch 165/168 +128/128 [==============================] - 41s 316ms/step - loss: 0.1477 - accuracy: 0.9507 - val_loss: 0.2826 - val_accuracy: 0.9471 +Epoch 166/168 +128/128 [==============================] - 41s 317ms/step - loss: 0.1154 - accuracy: 0.9653 - val_loss: 0.3680 - val_accuracy: 0.9295 +Epoch 167/168 +128/128 [==============================] - 41s 315ms/step - loss: 0.0898 - accuracy: 0.9775 - val_loss: 0.2541 - val_accuracy: 0.9391 +Epoch 168/168 +128/128 [==============================] - 41s 318ms/step - loss: 0.0693 - accuracy: 0.9849 - val_loss: 0.3527 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3527 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 320.79 sec +Time taken for epoch(SUBo): 252.26 sec +Time taken for epoch(OTHERo): 68.52 sec +<---------------------------------------|Epoch [28] END|---------------------------------------> + +Epoch: 29/486 (TSEC: 168) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01064]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 169/174 +128/128 [==============================] - 47s 335ms/step - loss: 0.1663 - accuracy: 0.9512 - val_loss: 0.3551 - val_accuracy: 0.9247 +Epoch 170/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1545 - accuracy: 0.9453 - val_loss: 0.3584 - val_accuracy: 0.9343 +Epoch 171/174 +128/128 [==============================] - 42s 323ms/step - loss: 0.1221 - accuracy: 0.9624 - val_loss: 0.2740 - val_accuracy: 0.9487 +Epoch 172/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1067 - accuracy: 0.9736 - val_loss: 0.7232 - val_accuracy: 0.9135 +Epoch 173/174 +128/128 [==============================] - 41s 318ms/step - loss: 0.1092 - accuracy: 0.9761 - val_loss: 0.2708 - val_accuracy: 0.9439 +Epoch 174/174 +128/128 [==============================] - 41s 317ms/step - loss: 0.0605 - accuracy: 0.9849 - val_loss: 0.3280 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3280 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 323.85 sec +Time taken for epoch(SUBo): 253.51 sec +Time taken for epoch(OTHERo): 70.35 sec +<---------------------------------------|Epoch [29] END|---------------------------------------> + +Epoch: 30/486 (TSEC: 174) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01058]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 175/180 +128/128 [==============================] - 47s 335ms/step - loss: 0.2171 - accuracy: 0.9399 - val_loss: 0.2379 - val_accuracy: 0.9567 +Epoch 176/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.1811 - accuracy: 0.9429 - val_loss: 0.2557 - val_accuracy: 0.9215 +Epoch 177/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.1526 - accuracy: 0.9556 - val_loss: 0.1915 - val_accuracy: 0.9551 +Epoch 178/180 +128/128 [==============================] - 41s 319ms/step - loss: 0.1185 - accuracy: 0.9692 - val_loss: 0.2385 - val_accuracy: 0.9519 +Epoch 179/180 +128/128 [==============================] - 41s 318ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2647 - val_accuracy: 0.9567 +Epoch 180/180 +128/128 [==============================] - 41s 317ms/step - loss: 0.0615 - accuracy: 0.9854 - val_loss: 0.2430 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2430 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.08 sec +Time taken for epoch(SUBo): 252.22 sec +Time taken for epoch(OTHERo): 69.87 sec +<---------------------------------------|Epoch [30] END|---------------------------------------> + +Epoch: 31/486 (TSEC: 180) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01052]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 181/186 +128/128 [==============================] - 47s 335ms/step - loss: 0.1776 - accuracy: 0.9448 - val_loss: 0.3901 - val_accuracy: 0.9231 +Epoch 182/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1441 - accuracy: 0.9556 - val_loss: 0.4309 - val_accuracy: 0.9279 +Epoch 183/186 +128/128 [==============================] - 42s 324ms/step - loss: 0.1535 - accuracy: 0.9521 - val_loss: 0.2362 - val_accuracy: 0.9535 +Epoch 184/186 +128/128 [==============================] - 41s 318ms/step - loss: 0.1034 - accuracy: 0.9741 - val_loss: 0.4067 - val_accuracy: 0.9375 +Epoch 185/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9854 - val_loss: 0.4735 - val_accuracy: 0.9135 +Epoch 186/186 +128/128 [==============================] - 41s 317ms/step - loss: 0.0560 - accuracy: 0.9878 - val_loss: 0.5451 - val_accuracy: 0.9022 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9022 +Model Test loss: 0.5451 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 322.75 sec +Time taken for epoch(SUBo): 253.25 sec +Time taken for epoch(OTHERo): 69.50 sec +<---------------------------------------|Epoch [31] END|---------------------------------------> + +Epoch: 32/486 (TSEC: 186) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h08_m14_s13 +Setting training OneCycleLr::maxlr to [0.01046]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 187/192 +128/128 [==============================] - 47s 335ms/step - loss: 0.1805 - accuracy: 0.9492 - val_loss: 0.2431 - val_accuracy: 0.9295 +Epoch 188/192 +128/128 [==============================] - 42s 325ms/step - loss: 0.1582 - accuracy: 0.9570 - val_loss: 0.1746 - val_accuracy: 0.9567 +Epoch 189/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.1247 - accuracy: 0.9683 - val_loss: 0.2831 - val_accuracy: 0.9471 +Epoch 190/192 +128/128 [==============================] - 41s 316ms/step - loss: 0.1104 - accuracy: 0.9741 - val_loss: 0.3366 - val_accuracy: 0.9455 +Epoch 191/192 +128/128 [==============================] - 41s 317ms/step - loss: 0.0675 - accuracy: 0.9834 - val_loss: 0.2152 - val_accuracy: 0.9519 +Epoch 192/192 +128/128 [==============================] - 41s 319ms/step - loss: 0.0698 - accuracy: 0.9829 - val_loss: 0.2548 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.2548 +Model accuracy did not improve from 0.9647436141967773. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 338.08 sec +Time taken for epoch(SUBo): 252.96 sec +Time taken for epoch(OTHERo): 85.12 sec +<---------------------------------------|Epoch [32] END|---------------------------------------> + +Epoch: 33/486 (TSEC: 192) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0104]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 193/198 +128/128 [==============================] - 47s 336ms/step - loss: 0.1692 - accuracy: 0.9526 - val_loss: 0.2728 - val_accuracy: 0.9583 +Epoch 194/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1456 - accuracy: 0.9580 - val_loss: 0.2879 - val_accuracy: 0.9391 +Epoch 195/198 +128/128 [==============================] - 42s 324ms/step - loss: 0.1384 - accuracy: 0.9629 - val_loss: 0.1816 - val_accuracy: 0.9663 +Epoch 196/198 +128/128 [==============================] - 41s 317ms/step - loss: 0.1157 - accuracy: 0.9658 - val_loss: 0.1837 - val_accuracy: 0.9583 +Epoch 197/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0825 - accuracy: 0.9775 - val_loss: 0.2042 - val_accuracy: 0.9583 +Epoch 198/198 +128/128 [==============================] - 41s 318ms/step - loss: 0.0523 - accuracy: 0.9878 - val_loss: 0.2148 - val_accuracy: 0.9567 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-195-0.9663.h5... +Model Test acc: 0.9663 +Model Test loss: 0.1816 +Improved model accuracy from 0.9647436141967773 to 0.9663461446762085. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 328.41 sec +Time taken for epoch(SUBo): 253.11 sec +Time taken for epoch(OTHERo): 75.30 sec +<---------------------------------------|Epoch [33] END|---------------------------------------> + +Epoch: 34/486 (TSEC: 198) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01034]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 199/204 +128/128 [==============================] - 47s 335ms/step - loss: 0.1624 - accuracy: 0.9580 - val_loss: 0.1644 - val_accuracy: 0.9551 +Epoch 200/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1435 - accuracy: 0.9585 - val_loss: 0.1795 - val_accuracy: 0.9599 +Epoch 201/204 +128/128 [==============================] - 42s 327ms/step - loss: 0.1188 - accuracy: 0.9697 - val_loss: 0.1687 - val_accuracy: 0.9647 +Epoch 202/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.1013 - accuracy: 0.9741 - val_loss: 0.1816 - val_accuracy: 0.9567 +Epoch 203/204 +128/128 [==============================] - 41s 317ms/step - loss: 0.0788 - accuracy: 0.9844 - val_loss: 0.1669 - val_accuracy: 0.9599 +Epoch 204/204 +128/128 [==============================] - 41s 318ms/step - loss: 0.0593 - accuracy: 0.9863 - val_loss: 0.2117 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2118 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.1544923484325409. Not saving model. +Time taken for epoch(FULL): 327.41 sec +Time taken for epoch(SUBo): 254.14 sec +Time taken for epoch(OTHERo): 73.27 sec +<---------------------------------------|Epoch [34] END|---------------------------------------> + +Epoch: 35/486 (TSEC: 204) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01028]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 205/210 +128/128 [==============================] - 47s 336ms/step - loss: 0.1549 - accuracy: 0.9600 - val_loss: 0.1544 - val_accuracy: 0.9551 +Epoch 206/210 +128/128 [==============================] - 41s 320ms/step - loss: 0.1439 - accuracy: 0.9604 - val_loss: 0.2276 - val_accuracy: 0.9503 +Epoch 207/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.1326 - accuracy: 0.9629 - val_loss: 0.2690 - val_accuracy: 0.9391 +Epoch 208/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0984 - accuracy: 0.9795 - val_loss: 0.2248 - val_accuracy: 0.9551 +Epoch 209/210 +128/128 [==============================] - 41s 317ms/step - loss: 0.0851 - accuracy: 0.9829 - val_loss: 0.2186 - val_accuracy: 0.9503 +Epoch 210/210 +128/128 [==============================] - 41s 318ms/step - loss: 0.0714 - accuracy: 0.9863 - val_loss: 0.1907 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-205-0.9551.h5... +Model Test acc: 0.9551 +Model Test loss: 0.1544 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Improved model loss from 0.1544923484325409 to 0.15437141060829163. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 329.96 sec +Time taken for epoch(SUBo): 252.88 sec +Time taken for epoch(OTHERo): 77.08 sec +<---------------------------------------|Epoch [35] END|---------------------------------------> + +Epoch: 36/486 (TSEC: 210) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01022]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 211/216 +128/128 [==============================] - 47s 336ms/step - loss: 0.1497 - accuracy: 0.9502 - val_loss: 0.1893 - val_accuracy: 0.9551 +Epoch 212/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1667 - accuracy: 0.9521 - val_loss: 0.3545 - val_accuracy: 0.9263 +Epoch 213/216 +128/128 [==============================] - 41s 317ms/step - loss: 0.1468 - accuracy: 0.9575 - val_loss: 0.5278 - val_accuracy: 0.8750 +Epoch 214/216 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9780 - val_loss: 0.1828 - val_accuracy: 0.9615 +Epoch 215/216 +128/128 [==============================] - 41s 320ms/step - loss: 0.0711 - accuracy: 0.9824 - val_loss: 0.3208 - val_accuracy: 0.9327 +Epoch 216/216 +128/128 [==============================] - 41s 318ms/step - loss: 0.0442 - accuracy: 0.9946 - val_loss: 0.3144 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3144 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 328.83 sec +Time taken for epoch(SUBo): 253.49 sec +Time taken for epoch(OTHERo): 75.34 sec +<---------------------------------------|Epoch [36] END|---------------------------------------> + +Epoch: 37/486 (TSEC: 216) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01016]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 217/222 +128/128 [==============================] - 47s 336ms/step - loss: 0.1880 - accuracy: 0.9443 - val_loss: 0.3129 - val_accuracy: 0.9199 +Epoch 218/222 +128/128 [==============================] - 42s 324ms/step - loss: 0.1602 - accuracy: 0.9565 - val_loss: 0.3133 - val_accuracy: 0.9391 +Epoch 219/222 +128/128 [==============================] - 42s 326ms/step - loss: 0.1171 - accuracy: 0.9678 - val_loss: 0.2472 - val_accuracy: 0.9535 +Epoch 220/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.1136 - accuracy: 0.9722 - val_loss: 0.5505 - val_accuracy: 0.9199 +Epoch 221/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0791 - accuracy: 0.9824 - val_loss: 0.3557 - val_accuracy: 0.9247 +Epoch 222/222 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9824 - val_loss: 0.4185 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.4185 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.53 sec +Time taken for epoch(SUBo): 253.85 sec +Time taken for epoch(OTHERo): 73.68 sec +<---------------------------------------|Epoch [37] END|---------------------------------------> + +Epoch: 38/486 (TSEC: 222) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0101]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 223/228 +128/128 [==============================] - 47s 335ms/step - loss: 0.1541 - accuracy: 0.9565 - val_loss: 0.2467 - val_accuracy: 0.9519 +Epoch 224/228 +128/128 [==============================] - 41s 318ms/step - loss: 0.1767 - accuracy: 0.9443 - val_loss: 0.3775 - val_accuracy: 0.9119 +Epoch 225/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1414 - accuracy: 0.9551 - val_loss: 0.3540 - val_accuracy: 0.9455 +Epoch 226/228 +128/128 [==============================] - 41s 319ms/step - loss: 0.1003 - accuracy: 0.9771 - val_loss: 0.4779 - val_accuracy: 0.9295 +Epoch 227/228 +128/128 [==============================] - 42s 324ms/step - loss: 0.0976 - accuracy: 0.9785 - val_loss: 0.1954 - val_accuracy: 0.9599 +Epoch 228/228 +128/128 [==============================] - 41s 317ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.2645 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2645 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.10 sec +Time taken for epoch(SUBo): 252.83 sec +Time taken for epoch(OTHERo): 72.28 sec +<---------------------------------------|Epoch [38] END|---------------------------------------> + +Epoch: 39/486 (TSEC: 228) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.01004]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 229/234 +128/128 [==============================] - 47s 337ms/step - loss: 0.1943 - accuracy: 0.9424 - val_loss: 0.2957 - val_accuracy: 0.8942 +Epoch 230/234 +128/128 [==============================] - 42s 324ms/step - loss: 0.1701 - accuracy: 0.9468 - val_loss: 0.3393 - val_accuracy: 0.9231 +Epoch 231/234 +128/128 [==============================] - 42s 326ms/step - loss: 0.1325 - accuracy: 0.9609 - val_loss: 0.3046 - val_accuracy: 0.9471 +Epoch 232/234 +128/128 [==============================] - 42s 325ms/step - loss: 0.1046 - accuracy: 0.9727 - val_loss: 0.2105 - val_accuracy: 0.9551 +Epoch 233/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0784 - accuracy: 0.9819 - val_loss: 0.4733 - val_accuracy: 0.9022 +Epoch 234/234 +128/128 [==============================] - 41s 317ms/step - loss: 0.0696 - accuracy: 0.9878 - val_loss: 0.3982 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.3982 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.39 sec +Time taken for epoch(SUBo): 254.95 sec +Time taken for epoch(OTHERo): 71.43 sec +<---------------------------------------|Epoch [39] END|---------------------------------------> + +Epoch: 40/486 (TSEC: 234) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00998]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 235/240 +128/128 [==============================] - 47s 334ms/step - loss: 0.1567 - accuracy: 0.9551 - val_loss: 0.4088 - val_accuracy: 0.9183 +Epoch 236/240 +128/128 [==============================] - 42s 327ms/step - loss: 0.1637 - accuracy: 0.9531 - val_loss: 0.2168 - val_accuracy: 0.9583 +Epoch 237/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.1200 - accuracy: 0.9707 - val_loss: 0.2209 - val_accuracy: 0.9551 +Epoch 238/240 +128/128 [==============================] - 41s 318ms/step - loss: 0.1224 - accuracy: 0.9722 - val_loss: 0.3509 - val_accuracy: 0.9439 +Epoch 239/240 +128/128 [==============================] - 42s 325ms/step - loss: 0.0819 - accuracy: 0.9814 - val_loss: 0.2052 - val_accuracy: 0.9599 +Epoch 240/240 +128/128 [==============================] - 41s 317ms/step - loss: 0.0590 - accuracy: 0.9883 - val_loss: 0.2006 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2006 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 325.76 sec +Time taken for epoch(SUBo): 253.96 sec +Time taken for epoch(OTHERo): 71.80 sec +<---------------------------------------|Epoch [40] END|---------------------------------------> + +Epoch: 41/486 (TSEC: 240) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00992]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 241/246 +128/128 [==============================] - 47s 335ms/step - loss: 0.1420 - accuracy: 0.9570 - val_loss: 0.2761 - val_accuracy: 0.9487 +Epoch 242/246 +128/128 [==============================] - 42s 326ms/step - loss: 0.1315 - accuracy: 0.9609 - val_loss: 0.2534 - val_accuracy: 0.9535 +Epoch 243/246 +128/128 [==============================] - 42s 327ms/step - loss: 0.1119 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9631 +Epoch 244/246 +128/128 [==============================] - 41s 317ms/step - loss: 0.0742 - accuracy: 0.9844 - val_loss: 0.2034 - val_accuracy: 0.9615 +Epoch 245/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0772 - accuracy: 0.9854 - val_loss: 0.1984 - val_accuracy: 0.9599 +Epoch 246/246 +128/128 [==============================] - 41s 318ms/step - loss: 0.0528 - accuracy: 0.9897 - val_loss: 0.2011 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2011 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.07 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.68 sec +<---------------------------------------|Epoch [41] END|---------------------------------------> + +Epoch: 42/486 (TSEC: 246) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00986]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 247/252 +128/128 [==============================] - 47s 336ms/step - loss: 0.1604 - accuracy: 0.9536 - val_loss: 0.1886 - val_accuracy: 0.9599 +Epoch 248/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.1412 - accuracy: 0.9619 - val_loss: 0.2467 - val_accuracy: 0.9535 +Epoch 249/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.1131 - accuracy: 0.9683 - val_loss: 0.1881 - val_accuracy: 0.9535 +Epoch 250/252 +128/128 [==============================] - 42s 327ms/step - loss: 0.0824 - accuracy: 0.9819 - val_loss: 0.2461 - val_accuracy: 0.9615 +Epoch 251/252 +128/128 [==============================] - 41s 319ms/step - loss: 0.0666 - accuracy: 0.9834 - val_loss: 0.1880 - val_accuracy: 0.9583 +Epoch 252/252 +128/128 [==============================] - 41s 318ms/step - loss: 0.0533 - accuracy: 0.9893 - val_loss: 0.2136 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2136 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 253.59 sec +Time taken for epoch(OTHERo): 72.54 sec +<---------------------------------------|Epoch [42] END|---------------------------------------> + +Epoch: 43/486 (TSEC: 252) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0098]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 253/258 +128/128 [==============================] - 47s 336ms/step - loss: 0.1524 - accuracy: 0.9512 - val_loss: 0.2455 - val_accuracy: 0.9583 +Epoch 254/258 +128/128 [==============================] - 42s 328ms/step - loss: 0.1381 - accuracy: 0.9570 - val_loss: 0.1787 - val_accuracy: 0.9631 +Epoch 255/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0923 - accuracy: 0.9751 - val_loss: 0.2360 - val_accuracy: 0.9599 +Epoch 256/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0843 - accuracy: 0.9819 - val_loss: 0.2152 - val_accuracy: 0.9599 +Epoch 257/258 +128/128 [==============================] - 41s 319ms/step - loss: 0.0523 - accuracy: 0.9912 - val_loss: 0.2044 - val_accuracy: 0.9599 +Epoch 258/258 +128/128 [==============================] - 41s 321ms/step - loss: 0.0513 - accuracy: 0.9907 - val_loss: 0.2041 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2042 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 327.11 sec +Time taken for epoch(SUBo): 254.27 sec +Time taken for epoch(OTHERo): 72.84 sec +<---------------------------------------|Epoch [43] END|---------------------------------------> + +Epoch: 44/486 (TSEC: 258) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00974]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 259/264 +128/128 [==============================] - 47s 336ms/step - loss: 0.1498 - accuracy: 0.9585 - val_loss: 0.2349 - val_accuracy: 0.9599 +Epoch 260/264 +128/128 [==============================] - 41s 320ms/step - loss: 0.1329 - accuracy: 0.9644 - val_loss: 0.2119 - val_accuracy: 0.9439 +Epoch 261/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0964 - accuracy: 0.9722 - val_loss: 0.3902 - val_accuracy: 0.9343 +Epoch 262/264 +128/128 [==============================] - 41s 317ms/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2996 - val_accuracy: 0.9439 +Epoch 263/264 +128/128 [==============================] - 41s 319ms/step - loss: 0.0676 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9343 +Epoch 264/264 +128/128 [==============================] - 41s 321ms/step - loss: 0.0587 - accuracy: 0.9897 - val_loss: 0.3485 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.3485 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.12 sec +Time taken for epoch(SUBo): 252.93 sec +Time taken for epoch(OTHERo): 73.19 sec +<---------------------------------------|Epoch [44] END|---------------------------------------> + +Epoch: 45/486 (TSEC: 264) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00968]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 265/270 +128/128 [==============================] - 47s 338ms/step - loss: 0.1289 - accuracy: 0.9648 - val_loss: 0.2281 - val_accuracy: 0.9535 +Epoch 266/270 +128/128 [==============================] - 41s 318ms/step - loss: 0.1162 - accuracy: 0.9634 - val_loss: 0.2183 - val_accuracy: 0.9471 +Epoch 267/270 +128/128 [==============================] - 41s 319ms/step - loss: 0.1008 - accuracy: 0.9673 - val_loss: 0.2254 - val_accuracy: 0.9455 +Epoch 268/270 +128/128 [==============================] - 42s 328ms/step - loss: 0.0772 - accuracy: 0.9805 - val_loss: 0.2190 - val_accuracy: 0.9599 +Epoch 269/270 +128/128 [==============================] - 41s 317ms/step - loss: 0.0632 - accuracy: 0.9883 - val_loss: 0.2154 - val_accuracy: 0.9535 +Epoch 270/270 +128/128 [==============================] - 41s 322ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2324 - val_accuracy: 0.9535 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9535 +Model Test loss: 0.2324 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.56 sec +Time taken for epoch(SUBo): 254.39 sec +Time taken for epoch(OTHERo): 72.17 sec +<---------------------------------------|Epoch [45] END|---------------------------------------> + +Epoch: 46/486 (TSEC: 270) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00962]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 271/276 +128/128 [==============================] - 47s 337ms/step - loss: 0.1797 - accuracy: 0.9448 - val_loss: 0.1607 - val_accuracy: 0.9407 +Epoch 272/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.1472 - accuracy: 0.9556 - val_loss: 0.4108 - val_accuracy: 0.9199 +Epoch 273/276 +128/128 [==============================] - 42s 327ms/step - loss: 0.1242 - accuracy: 0.9683 - val_loss: 0.1753 - val_accuracy: 0.9631 +Epoch 274/276 +128/128 [==============================] - 41s 319ms/step - loss: 0.0948 - accuracy: 0.9746 - val_loss: 0.2700 - val_accuracy: 0.9519 +Epoch 275/276 +128/128 [==============================] - 41s 320ms/step - loss: 0.0590 - accuracy: 0.9839 - val_loss: 0.3052 - val_accuracy: 0.9487 +Epoch 276/276 +128/128 [==============================] - 41s 321ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.3107 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3108 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.76 sec +Time taken for epoch(SUBo): 254.60 sec +Time taken for epoch(OTHERo): 72.16 sec +<---------------------------------------|Epoch [46] END|---------------------------------------> + +Epoch: 47/486 (TSEC: 276) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00956]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 277/282 +128/128 [==============================] - 48s 339ms/step - loss: 0.1441 - accuracy: 0.9561 - val_loss: 0.2333 - val_accuracy: 0.9519 +Epoch 278/282 +128/128 [==============================] - 41s 320ms/step - loss: 0.1321 - accuracy: 0.9551 - val_loss: 0.4633 - val_accuracy: 0.9215 +Epoch 279/282 +128/128 [==============================] - 41s 318ms/step - loss: 0.0868 - accuracy: 0.9761 - val_loss: 0.4848 - val_accuracy: 0.8894 +Epoch 280/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0713 - accuracy: 0.9834 - val_loss: 0.3469 - val_accuracy: 0.9471 +Epoch 281/282 +128/128 [==============================] - 41s 321ms/step - loss: 0.0440 - accuracy: 0.9897 - val_loss: 0.3346 - val_accuracy: 0.9407 +Epoch 282/282 +128/128 [==============================] - 41s 319ms/step - loss: 0.0389 - accuracy: 0.9912 - val_loss: 0.3641 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3641 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 326.51 sec +Time taken for epoch(SUBo): 253.63 sec +Time taken for epoch(OTHERo): 72.88 sec +<---------------------------------------|Epoch [47] END|---------------------------------------> + +Epoch: 48/486 (TSEC: 282) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0095]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 283/288 +128/128 [==============================] - 47s 339ms/step - loss: 0.1535 - accuracy: 0.9546 - val_loss: 0.4766 - val_accuracy: 0.8638 +Epoch 284/288 +128/128 [==============================] - 42s 327ms/step - loss: 0.1403 - accuracy: 0.9575 - val_loss: 0.5117 - val_accuracy: 0.9183 +Epoch 285/288 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.3697 - val_accuracy: 0.9327 +Epoch 286/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0672 - accuracy: 0.9805 - val_loss: 0.7594 - val_accuracy: 0.8478 +Epoch 287/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0577 - accuracy: 0.9824 - val_loss: 0.9916 - val_accuracy: 0.8862 +Epoch 288/288 +128/128 [==============================] - 41s 319ms/step - loss: 0.0443 - accuracy: 0.9922 - val_loss: 0.7103 - val_accuracy: 0.8958 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8958 +Model Test loss: 0.7104 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.17 sec +Time taken for epoch(SUBo): 255.62 sec +Time taken for epoch(OTHERo): 74.55 sec +<---------------------------------------|Epoch [48] END|---------------------------------------> + +Epoch: 49/486 (TSEC: 288) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00944]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 289/294 +128/128 [==============================] - 48s 338ms/step - loss: 0.1300 - accuracy: 0.9609 - val_loss: 0.4313 - val_accuracy: 0.9167 +Epoch 290/294 +128/128 [==============================] - 42s 325ms/step - loss: 0.1202 - accuracy: 0.9673 - val_loss: 0.4166 - val_accuracy: 0.9247 +Epoch 291/294 +128/128 [==============================] - 41s 319ms/step - loss: 0.0837 - accuracy: 0.9795 - val_loss: 0.5159 - val_accuracy: 0.9103 +Epoch 292/294 +128/128 [==============================] - 42s 327ms/step - loss: 0.0749 - accuracy: 0.9805 - val_loss: 0.5533 - val_accuracy: 0.9279 +Epoch 293/294 +128/128 [==============================] - 41s 317ms/step - loss: 0.0380 - accuracy: 0.9912 - val_loss: 0.5517 - val_accuracy: 0.9215 +Epoch 294/294 +128/128 [==============================] - 41s 318ms/step - loss: 0.0488 - accuracy: 0.9893 - val_loss: 0.5959 - val_accuracy: 0.9183 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9183 +Model Test loss: 0.5959 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 330.11 sec +Time taken for epoch(SUBo): 254.80 sec +Time taken for epoch(OTHERo): 75.32 sec +<---------------------------------------|Epoch [49] END|---------------------------------------> + +Epoch: 50/486 (TSEC: 294) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00938]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 295/300 +128/128 [==============================] - 47s 337ms/step - loss: 0.1262 - accuracy: 0.9590 - val_loss: 0.5855 - val_accuracy: 0.9151 +Epoch 296/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0996 - accuracy: 0.9727 - val_loss: 1.5691 - val_accuracy: 0.8494 +Epoch 297/300 +128/128 [==============================] - 42s 326ms/step - loss: 0.1047 - accuracy: 0.9766 - val_loss: 0.2379 - val_accuracy: 0.9279 +Epoch 298/300 +128/128 [==============================] - 42s 327ms/step - loss: 0.0940 - accuracy: 0.9756 - val_loss: 0.3291 - val_accuracy: 0.9327 +Epoch 299/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0694 - accuracy: 0.9912 - val_loss: 0.4035 - val_accuracy: 0.9311 +Epoch 300/300 +128/128 [==============================] - 41s 319ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.4308 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4308 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 331.10 sec +Time taken for epoch(SUBo): 255.03 sec +Time taken for epoch(OTHERo): 76.07 sec +<---------------------------------------|Epoch [50] END|---------------------------------------> + +Epoch: 51/486 (TSEC: 300) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00932]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 301/306 +128/128 [==============================] - 52s 371ms/step - loss: 0.1531 - accuracy: 0.9565 - val_loss: 0.6182 - val_accuracy: 0.8846 +Epoch 302/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.1503 - accuracy: 0.9614 - val_loss: 0.5275 - val_accuracy: 0.8990 +Epoch 303/306 +128/128 [==============================] - 47s 370ms/step - loss: 0.0956 - accuracy: 0.9766 - val_loss: 0.4508 - val_accuracy: 0.9311 +Epoch 304/306 +128/128 [==============================] - 46s 355ms/step - loss: 0.0631 - accuracy: 0.9854 - val_loss: 0.6242 - val_accuracy: 0.9151 +Epoch 305/306 +128/128 [==============================] - 46s 360ms/step - loss: 0.0591 - accuracy: 0.9863 - val_loss: 0.6694 - val_accuracy: 0.8990 +Epoch 306/306 +128/128 [==============================] - 47s 362ms/step - loss: 0.0375 - accuracy: 0.9922 - val_loss: 0.7052 - val_accuracy: 0.8974 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.8974 +Model Test loss: 0.7052 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 362.92 sec +Time taken for epoch(SUBo): 286.09 sec +Time taken for epoch(OTHERo): 76.83 sec +<---------------------------------------|Epoch [51] END|---------------------------------------> + +Epoch: 52/486 (TSEC: 306) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00926]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 307/312 +128/128 [==============================] - 54s 384ms/step - loss: 0.1345 - accuracy: 0.9624 - val_loss: 0.4739 - val_accuracy: 0.9183 +Epoch 308/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.1209 - accuracy: 0.9658 - val_loss: 0.3827 - val_accuracy: 0.9022 +Epoch 309/312 +128/128 [==============================] - 46s 360ms/step - loss: 0.0854 - accuracy: 0.9785 - val_loss: 0.8723 - val_accuracy: 0.8974 +Epoch 310/312 +128/128 [==============================] - 46s 359ms/step - loss: 0.0652 - accuracy: 0.9854 - val_loss: 0.5308 - val_accuracy: 0.9279 +Epoch 311/312 +128/128 [==============================] - 46s 357ms/step - loss: 0.0672 - accuracy: 0.9863 - val_loss: 0.5376 - val_accuracy: 0.9135 +Epoch 312/312 +128/128 [==============================] - 45s 354ms/step - loss: 0.0423 - accuracy: 0.9951 - val_loss: 0.5680 - val_accuracy: 0.9135 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9135 +Model Test loss: 0.5680 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.05 sec +Time taken for epoch(SUBo): 284.61 sec +Time taken for epoch(OTHERo): 95.44 sec +<---------------------------------------|Epoch [52] END|---------------------------------------> + +Epoch: 53/486 (TSEC: 312) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0092]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 313/318 +128/128 [==============================] - 55s 390ms/step - loss: 0.1498 - accuracy: 0.9580 - val_loss: 0.3442 - val_accuracy: 0.9247 +Epoch 314/318 +128/128 [==============================] - 46s 356ms/step - loss: 0.1192 - accuracy: 0.9624 - val_loss: 0.6108 - val_accuracy: 0.8766 +Epoch 315/318 +128/128 [==============================] - 47s 366ms/step - loss: 0.1046 - accuracy: 0.9766 - val_loss: 0.4408 - val_accuracy: 0.9375 +Epoch 316/318 +128/128 [==============================] - 46s 355ms/step - loss: 0.0784 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9375 +Epoch 317/318 +128/128 [==============================] - 46s 358ms/step - loss: 0.0556 - accuracy: 0.9868 - val_loss: 0.4785 - val_accuracy: 0.9231 +Epoch 318/318 +128/128 [==============================] - 46s 361ms/step - loss: 0.0487 - accuracy: 0.9932 - val_loss: 0.4631 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4632 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.68 sec +Time taken for epoch(SUBo): 286.71 sec +Time taken for epoch(OTHERo): 93.97 sec +<---------------------------------------|Epoch [53] END|---------------------------------------> + +Epoch: 54/486 (TSEC: 318) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00914]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 319/324 +128/128 [==============================] - 54s 378ms/step - loss: 0.1205 - accuracy: 0.9629 - val_loss: 0.5291 - val_accuracy: 0.9263 +Epoch 320/324 +128/128 [==============================] - 47s 368ms/step - loss: 0.1224 - accuracy: 0.9639 - val_loss: 0.4687 - val_accuracy: 0.9439 +Epoch 321/324 +128/128 [==============================] - 47s 363ms/step - loss: 0.0922 - accuracy: 0.9746 - val_loss: 0.3358 - val_accuracy: 0.9455 +Epoch 322/324 +128/128 [==============================] - 46s 355ms/step - loss: 0.0647 - accuracy: 0.9829 - val_loss: 0.3614 - val_accuracy: 0.9375 +Epoch 323/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0557 - accuracy: 0.9863 - val_loss: 0.3546 - val_accuracy: 0.9423 +Epoch 324/324 +128/128 [==============================] - 47s 365ms/step - loss: 0.0409 - accuracy: 0.9922 - val_loss: 0.5100 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5101 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 389.45 sec +Time taken for epoch(SUBo): 287.64 sec +Time taken for epoch(OTHERo): 101.81 sec +<---------------------------------------|Epoch [54] END|---------------------------------------> + +Epoch: 55/486 (TSEC: 324) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00908]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 325/330 +128/128 [==============================] - 55s 386ms/step - loss: 0.1319 - accuracy: 0.9590 - val_loss: 0.5606 - val_accuracy: 0.9263 +Epoch 326/330 +128/128 [==============================] - 46s 358ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.3161 - val_accuracy: 0.9455 +Epoch 327/330 +128/128 [==============================] - 42s 329ms/step - loss: 0.0829 - accuracy: 0.9746 - val_loss: 0.3472 - val_accuracy: 0.9391 +Epoch 328/330 +128/128 [==============================] - 45s 352ms/step - loss: 0.0751 - accuracy: 0.9834 - val_loss: 0.3422 - val_accuracy: 0.9359 +Epoch 329/330 +128/128 [==============================] - 46s 356ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.3538 - val_accuracy: 0.9375 +Epoch 330/330 +128/128 [==============================] - 46s 361ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.3231 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3231 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 380.47 sec +Time taken for epoch(SUBo): 281.24 sec +Time taken for epoch(OTHERo): 99.23 sec +<---------------------------------------|Epoch [55] END|---------------------------------------> + +Epoch: 56/486 (TSEC: 330) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00902]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 331/336 +128/128 [==============================] - 55s 387ms/step - loss: 0.1542 - accuracy: 0.9536 - val_loss: 0.1925 - val_accuracy: 0.9535 +Epoch 332/336 +128/128 [==============================] - 47s 363ms/step - loss: 0.1151 - accuracy: 0.9663 - val_loss: 0.3647 - val_accuracy: 0.9519 +Epoch 333/336 +128/128 [==============================] - 47s 368ms/step - loss: 0.0820 - accuracy: 0.9810 - val_loss: 0.2064 - val_accuracy: 0.9583 +Epoch 334/336 +128/128 [==============================] - 46s 356ms/step - loss: 0.0598 - accuracy: 0.9829 - val_loss: 0.3637 - val_accuracy: 0.9439 +Epoch 335/336 +128/128 [==============================] - 47s 366ms/step - loss: 0.0651 - accuracy: 0.9854 - val_loss: 0.4960 - val_accuracy: 0.9311 +Epoch 336/336 +128/128 [==============================] - 46s 360ms/step - loss: 0.0331 - accuracy: 0.9907 - val_loss: 0.3478 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3479 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 392.43 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 103.65 sec +<---------------------------------------|Epoch [56] END|---------------------------------------> + +Epoch: 57/486 (TSEC: 336) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00896]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 337/342 +128/128 [==============================] - 57s 394ms/step - loss: 0.1406 - accuracy: 0.9629 - val_loss: 0.4344 - val_accuracy: 0.9327 +Epoch 338/342 +128/128 [==============================] - 46s 356ms/step - loss: 0.1054 - accuracy: 0.9707 - val_loss: 0.3732 - val_accuracy: 0.9167 +Epoch 339/342 +128/128 [==============================] - 46s 357ms/step - loss: 0.0958 - accuracy: 0.9692 - val_loss: 0.4313 - val_accuracy: 0.9247 +Epoch 340/342 +128/128 [==============================] - 47s 362ms/step - loss: 0.0641 - accuracy: 0.9893 - val_loss: 0.4840 - val_accuracy: 0.9183 +Epoch 341/342 +128/128 [==============================] - 46s 359ms/step - loss: 0.0521 - accuracy: 0.9912 - val_loss: 0.3801 - val_accuracy: 0.9263 +Epoch 342/342 +128/128 [==============================] - 44s 340ms/step - loss: 0.0324 - accuracy: 0.9937 - val_loss: 0.4083 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.4083 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 387.98 sec +Time taken for epoch(SUBo): 285.68 sec +Time taken for epoch(OTHERo): 102.30 sec +<---------------------------------------|Epoch [57] END|---------------------------------------> + +Epoch: 58/486 (TSEC: 342) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0089]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 343/348 +128/128 [==============================] - 52s 371ms/step - loss: 0.1229 - accuracy: 0.9639 - val_loss: 0.2839 - val_accuracy: 0.9343 +Epoch 344/348 +128/128 [==============================] - 42s 327ms/step - loss: 0.1056 - accuracy: 0.9702 - val_loss: 0.3552 - val_accuracy: 0.9279 +Epoch 345/348 +128/128 [==============================] - 42s 330ms/step - loss: 0.0896 - accuracy: 0.9771 - val_loss: 0.4439 - val_accuracy: 0.9359 +Epoch 346/348 +128/128 [==============================] - 41s 320ms/step - loss: 0.0683 - accuracy: 0.9858 - val_loss: 0.4294 - val_accuracy: 0.9343 +Epoch 347/348 +128/128 [==============================] - 44s 344ms/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.3231 - val_accuracy: 0.9375 +Epoch 348/348 +128/128 [==============================] - 46s 358ms/step - loss: 0.0327 - accuracy: 0.9937 - val_loss: 0.3776 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3776 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 350.83 sec +Time taken for epoch(SUBo): 268.69 sec +Time taken for epoch(OTHERo): 82.14 sec +<---------------------------------------|Epoch [58] END|---------------------------------------> + +Epoch: 59/486 (TSEC: 348) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00884]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 349/354 +128/128 [==============================] - 49s 348ms/step - loss: 0.1573 - accuracy: 0.9590 - val_loss: 0.1980 - val_accuracy: 0.9439 +Epoch 350/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.1056 - accuracy: 0.9707 - val_loss: 0.4215 - val_accuracy: 0.9135 +Epoch 351/354 +128/128 [==============================] - 41s 320ms/step - loss: 0.0833 - accuracy: 0.9795 - val_loss: 0.5733 - val_accuracy: 0.9327 +Epoch 352/354 +128/128 [==============================] - 42s 329ms/step - loss: 0.0676 - accuracy: 0.9780 - val_loss: 0.2398 - val_accuracy: 0.9599 +Epoch 353/354 +128/128 [==============================] - 42s 324ms/step - loss: 0.0403 - accuracy: 0.9917 - val_loss: 0.3821 - val_accuracy: 0.9375 +Epoch 354/354 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9937 - val_loss: 0.4066 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4066 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 353.60 sec +Time taken for epoch(SUBo): 258.60 sec +Time taken for epoch(OTHERo): 95.01 sec +<---------------------------------------|Epoch [59] END|---------------------------------------> + +Epoch: 60/486 (TSEC: 354) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00878]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 355/360 +128/128 [==============================] - 49s 343ms/step - loss: 0.1254 - accuracy: 0.9663 - val_loss: 0.3407 - val_accuracy: 0.9455 +Epoch 356/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.1073 - accuracy: 0.9668 - val_loss: 0.4440 - val_accuracy: 0.9119 +Epoch 357/360 +128/128 [==============================] - 42s 326ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.7960 - val_accuracy: 0.9071 +Epoch 358/360 +128/128 [==============================] - 41s 321ms/step - loss: 0.0743 - accuracy: 0.9805 - val_loss: 0.7154 - val_accuracy: 0.9022 +Epoch 359/360 +128/128 [==============================] - 42s 325ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.4332 - val_accuracy: 0.9295 +Epoch 360/360 +128/128 [==============================] - 41s 320ms/step - loss: 0.0427 - accuracy: 0.9932 - val_loss: 0.4142 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4142 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 346.87 sec +Time taken for epoch(SUBo): 257.34 sec +Time taken for epoch(OTHERo): 89.53 sec +<---------------------------------------|Epoch [60] END|---------------------------------------> + +Epoch: 61/486 (TSEC: 360) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00872]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 361/366 +128/128 [==============================] - 48s 338ms/step - loss: 0.1475 - accuracy: 0.9600 - val_loss: 0.2768 - val_accuracy: 0.9311 +Epoch 362/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1058 - accuracy: 0.9653 - val_loss: 0.3413 - val_accuracy: 0.9471 +Epoch 363/366 +128/128 [==============================] - 45s 354ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.7239 - val_accuracy: 0.9135 +Epoch 364/366 +128/128 [==============================] - 42s 330ms/step - loss: 0.0638 - accuracy: 0.9854 - val_loss: 0.4782 - val_accuracy: 0.9263 +Epoch 365/366 +128/128 [==============================] - 41s 322ms/step - loss: 0.0478 - accuracy: 0.9893 - val_loss: 0.6543 - val_accuracy: 0.9151 +Epoch 366/366 +128/128 [==============================] - 41s 323ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.7275 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.7276 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.90 sec +Time taken for epoch(SUBo): 264.37 sec +Time taken for epoch(OTHERo): 77.53 sec +<---------------------------------------|Epoch [61] END|---------------------------------------> + +Epoch: 62/486 (TSEC: 366) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00866]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 367/372 +128/128 [==============================] - 48s 341ms/step - loss: 0.1493 - accuracy: 0.9634 - val_loss: 0.3469 - val_accuracy: 0.9391 +Epoch 368/372 +128/128 [==============================] - 45s 353ms/step - loss: 0.1203 - accuracy: 0.9722 - val_loss: 0.3296 - val_accuracy: 0.9407 +Epoch 369/372 +128/128 [==============================] - 47s 366ms/step - loss: 0.0936 - accuracy: 0.9717 - val_loss: 0.2521 - val_accuracy: 0.9551 +Epoch 370/372 +128/128 [==============================] - 43s 331ms/step - loss: 0.0852 - accuracy: 0.9819 - val_loss: 0.2388 - val_accuracy: 0.9407 +Epoch 371/372 +128/128 [==============================] - 41s 323ms/step - loss: 0.0542 - accuracy: 0.9883 - val_loss: 0.2767 - val_accuracy: 0.9407 +Epoch 372/372 +128/128 [==============================] - 41s 320ms/step - loss: 0.0362 - accuracy: 0.9932 - val_loss: 0.2727 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.2727 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 344.05 sec +Time taken for epoch(SUBo): 266.44 sec +Time taken for epoch(OTHERo): 77.61 sec +<---------------------------------------|Epoch [62] END|---------------------------------------> + +Epoch: 63/486 (TSEC: 372) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0086]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 373/378 +128/128 [==============================] - 48s 341ms/step - loss: 0.1499 - accuracy: 0.9580 - val_loss: 0.3041 - val_accuracy: 0.9279 +Epoch 374/378 +128/128 [==============================] - 43s 334ms/step - loss: 0.1503 - accuracy: 0.9595 - val_loss: 0.2032 - val_accuracy: 0.9535 +Epoch 375/378 +128/128 [==============================] - 42s 325ms/step - loss: 0.0975 - accuracy: 0.9741 - val_loss: 0.3626 - val_accuracy: 0.9311 +Epoch 376/378 +128/128 [==============================] - 41s 321ms/step - loss: 0.0866 - accuracy: 0.9780 - val_loss: 0.2813 - val_accuracy: 0.9343 +Epoch 377/378 +128/128 [==============================] - 41s 323ms/step - loss: 0.0508 - accuracy: 0.9883 - val_loss: 0.4052 - val_accuracy: 0.9295 +Epoch 378/378 +128/128 [==============================] - 42s 327ms/step - loss: 0.0362 - accuracy: 0.9922 - val_loss: 0.4211 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4211 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 334.11 sec +Time taken for epoch(SUBo): 258.37 sec +Time taken for epoch(OTHERo): 75.73 sec +<---------------------------------------|Epoch [63] END|---------------------------------------> + +Epoch: 64/486 (TSEC: 378) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h11_m17_s24 +Setting training OneCycleLr::maxlr to [0.00854]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 379/384 +128/128 [==============================] - 48s 341ms/step - loss: 0.1332 - accuracy: 0.9673 - val_loss: 0.6303 - val_accuracy: 0.9006 +Epoch 380/384 +128/128 [==============================] - 42s 329ms/step - loss: 0.1069 - accuracy: 0.9717 - val_loss: 0.5002 - val_accuracy: 0.9263 +Epoch 381/384 +128/128 [==============================] - 41s 321ms/step - loss: 0.0842 - accuracy: 0.9810 - val_loss: 0.5058 - val_accuracy: 0.9183 +Epoch 382/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0635 - accuracy: 0.9819 - val_loss: 0.4695 - val_accuracy: 0.9359 +Epoch 383/384 +128/128 [==============================] - 43s 335ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.3165 - val_accuracy: 0.9519 +Epoch 384/384 +128/128 [==============================] - 42s 328ms/step - loss: 0.0297 - accuracy: 0.9951 - val_loss: 0.3692 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3692 +Model accuracy did not improve from 0.9663461446762085. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 356.90 sec +Time taken for epoch(SUBo): 259.87 sec +Time taken for epoch(OTHERo): 97.03 sec +<---------------------------------------|Epoch [64] END|---------------------------------------> + +Epoch: 65/486 (TSEC: 384) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00848]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 385/390 +128/128 [==============================] - 48s 342ms/step - loss: 0.1341 - accuracy: 0.9653 - val_loss: 0.2274 - val_accuracy: 0.9423 +Epoch 386/390 +128/128 [==============================] - 42s 324ms/step - loss: 0.1239 - accuracy: 0.9629 - val_loss: 0.5211 - val_accuracy: 0.9359 +Epoch 387/390 +128/128 [==============================] - 43s 333ms/step - loss: 0.0867 - accuracy: 0.9751 - val_loss: 0.1823 - val_accuracy: 0.9679 +Epoch 388/390 +128/128 [==============================] - 41s 320ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 0.2382 - val_accuracy: 0.9503 +Epoch 389/390 +128/128 [==============================] - 41s 321ms/step - loss: 0.0406 - accuracy: 0.9927 - val_loss: 0.3093 - val_accuracy: 0.9423 +Epoch 390/390 +128/128 [==============================] - 41s 322ms/step - loss: 0.0313 - accuracy: 0.9956 - val_loss: 0.2827 - val_accuracy: 0.9487 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-387-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1823 +Improved model accuracy from 0.9663461446762085 to 0.9679487347602844. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.22 sec +Time taken for epoch(SUBo): 257.30 sec +Time taken for epoch(OTHERo): 83.93 sec +<---------------------------------------|Epoch [65] END|---------------------------------------> + +Epoch: 66/486 (TSEC: 390) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00842]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 391/396 +128/128 [==============================] - 49s 347ms/step - loss: 0.1461 - accuracy: 0.9619 - val_loss: 0.1618 - val_accuracy: 0.9647 +Epoch 392/396 +128/128 [==============================] - 42s 327ms/step - loss: 0.1047 - accuracy: 0.9702 - val_loss: 0.2274 - val_accuracy: 0.9519 +Epoch 393/396 +128/128 [==============================] - 42s 325ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.4825 - val_accuracy: 0.9359 +Epoch 394/396 +128/128 [==============================] - 42s 330ms/step - loss: 0.0395 - accuracy: 0.9917 - val_loss: 0.4158 - val_accuracy: 0.9423 +Epoch 395/396 +128/128 [==============================] - 42s 328ms/step - loss: 0.0460 - accuracy: 0.9902 - val_loss: 0.2078 - val_accuracy: 0.9615 +Epoch 396/396 +128/128 [==============================] - 42s 326ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.2462 - val_accuracy: 0.9551 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9551 +Model Test loss: 0.2462 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 340.59 sec +Time taken for epoch(SUBo): 259.99 sec +Time taken for epoch(OTHERo): 80.59 sec +<---------------------------------------|Epoch [66] END|---------------------------------------> + +Epoch: 67/486 (TSEC: 396) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00836]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 397/402 +128/128 [==============================] - 49s 348ms/step - loss: 0.1334 - accuracy: 0.9663 - val_loss: 0.2740 - val_accuracy: 0.9583 +Epoch 398/402 +128/128 [==============================] - 41s 320ms/step - loss: 0.1099 - accuracy: 0.9692 - val_loss: 0.1655 - val_accuracy: 0.9583 +Epoch 399/402 +128/128 [==============================] - 42s 328ms/step - loss: 0.0830 - accuracy: 0.9790 - val_loss: 0.3718 - val_accuracy: 0.9215 +Epoch 400/402 +128/128 [==============================] - 43s 335ms/step - loss: 0.0508 - accuracy: 0.9863 - val_loss: 0.2091 - val_accuracy: 0.9647 +Epoch 401/402 +128/128 [==============================] - 46s 357ms/step - loss: 0.0562 - accuracy: 0.9858 - val_loss: 0.2725 - val_accuracy: 0.9599 +Epoch 402/402 +128/128 [==============================] - 46s 356ms/step - loss: 0.0382 - accuracy: 0.9922 - val_loss: 0.2737 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2736 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 348.32 sec +Time taken for epoch(SUBo): 267.55 sec +Time taken for epoch(OTHERo): 80.77 sec +<---------------------------------------|Epoch [67] END|---------------------------------------> + +Epoch: 68/486 (TSEC: 402) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0083]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 403/408 +128/128 [==============================] - 51s 356ms/step - loss: 0.1363 - accuracy: 0.9629 - val_loss: 0.1557 - val_accuracy: 0.9503 +Epoch 404/408 +128/128 [==============================] - 46s 356ms/step - loss: 0.1076 - accuracy: 0.9663 - val_loss: 0.4810 - val_accuracy: 0.9295 +Epoch 405/408 +128/128 [==============================] - 46s 355ms/step - loss: 0.0883 - accuracy: 0.9736 - val_loss: 0.2352 - val_accuracy: 0.9423 +Epoch 406/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0575 - accuracy: 0.9873 - val_loss: 0.2934 - val_accuracy: 0.9423 +Epoch 407/408 +128/128 [==============================] - 45s 354ms/step - loss: 0.0805 - accuracy: 0.9858 - val_loss: 0.2385 - val_accuracy: 0.9423 +Epoch 408/408 +128/128 [==============================] - 42s 327ms/step - loss: 0.0450 - accuracy: 0.9927 - val_loss: 0.2983 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.2983 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 374.47 sec +Time taken for epoch(SUBo): 276.39 sec +Time taken for epoch(OTHERo): 98.08 sec +<---------------------------------------|Epoch [68] END|---------------------------------------> + +Epoch: 69/486 (TSEC: 408) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00824]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 409/414 +128/128 [==============================] - 48s 339ms/step - loss: 0.1201 - accuracy: 0.9639 - val_loss: 0.1735 - val_accuracy: 0.9487 +Epoch 410/414 +128/128 [==============================] - 41s 322ms/step - loss: 0.1116 - accuracy: 0.9663 - val_loss: 0.2800 - val_accuracy: 0.9343 +Epoch 411/414 +128/128 [==============================] - 43s 334ms/step - loss: 0.0779 - accuracy: 0.9800 - val_loss: 0.1806 - val_accuracy: 0.9551 +Epoch 412/414 +128/128 [==============================] - 44s 341ms/step - loss: 0.0535 - accuracy: 0.9849 - val_loss: 0.2363 - val_accuracy: 0.9567 +Epoch 413/414 +128/128 [==============================] - 42s 329ms/step - loss: 0.0321 - accuracy: 0.9946 - val_loss: 0.3598 - val_accuracy: 0.9407 +Epoch 414/414 +128/128 [==============================] - 41s 321ms/step - loss: 0.0318 - accuracy: 0.9946 - val_loss: 0.3477 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3477 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 343.05 sec +Time taken for epoch(SUBo): 260.05 sec +Time taken for epoch(OTHERo): 83.00 sec +<---------------------------------------|Epoch [69] END|---------------------------------------> + +Epoch: 70/486 (TSEC: 414) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00818]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 415/420 +128/128 [==============================] - 50s 354ms/step - loss: 0.1226 - accuracy: 0.9692 - val_loss: 0.2330 - val_accuracy: 0.9455 +Epoch 416/420 +128/128 [==============================] - 42s 328ms/step - loss: 0.0977 - accuracy: 0.9741 - val_loss: 0.3240 - val_accuracy: 0.9407 +Epoch 417/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0766 - accuracy: 0.9844 - val_loss: 0.4363 - val_accuracy: 0.9455 +Epoch 418/420 +128/128 [==============================] - 42s 329ms/step - loss: 0.0709 - accuracy: 0.9849 - val_loss: 0.5340 - val_accuracy: 0.9263 +Epoch 419/420 +128/128 [==============================] - 43s 332ms/step - loss: 0.0520 - accuracy: 0.9888 - val_loss: 0.3766 - val_accuracy: 0.9295 +Epoch 420/420 +128/128 [==============================] - 42s 327ms/step - loss: 0.0447 - accuracy: 0.9917 - val_loss: 0.4541 - val_accuracy: 0.9167 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9167 +Model Test loss: 0.4541 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 342.13 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 79.85 sec +<---------------------------------------|Epoch [70] END|---------------------------------------> + +Epoch: 71/486 (TSEC: 420) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00812]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 421/426 +128/128 [==============================] - 48s 345ms/step - loss: 0.1389 - accuracy: 0.9541 - val_loss: 0.1589 - val_accuracy: 0.9615 +Epoch 422/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 0.1548 - val_accuracy: 0.9567 +Epoch 423/426 +128/128 [==============================] - 42s 326ms/step - loss: 0.0688 - accuracy: 0.9824 - val_loss: 0.3999 - val_accuracy: 0.9199 +Epoch 424/426 +128/128 [==============================] - 42s 330ms/step - loss: 0.0491 - accuracy: 0.9858 - val_loss: 0.1772 - val_accuracy: 0.9631 +Epoch 425/426 +128/128 [==============================] - 42s 329ms/step - loss: 0.0537 - accuracy: 0.9893 - val_loss: 0.2680 - val_accuracy: 0.9599 +Epoch 426/426 +128/128 [==============================] - 42s 332ms/step - loss: 0.0307 - accuracy: 0.9946 - val_loss: 0.2110 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2110 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Model loss did not improve from 0.15437141060829163. Not saving model. +Time taken for epoch(FULL): 341.68 sec +Time taken for epoch(SUBo): 260.39 sec +Time taken for epoch(OTHERo): 81.29 sec +<---------------------------------------|Epoch [71] END|---------------------------------------> + +Epoch: 72/486 (TSEC: 426) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00806]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 427/432 +128/128 [==============================] - 49s 346ms/step - loss: 0.1171 - accuracy: 0.9702 - val_loss: 0.1643 - val_accuracy: 0.9567 +Epoch 428/432 +128/128 [==============================] - 42s 326ms/step - loss: 0.0970 - accuracy: 0.9678 - val_loss: 0.1691 - val_accuracy: 0.9535 +Epoch 429/432 +128/128 [==============================] - 43s 337ms/step - loss: 0.0772 - accuracy: 0.9829 - val_loss: 0.1528 - val_accuracy: 0.9631 +Epoch 430/432 +128/128 [==============================] - 42s 325ms/step - loss: 0.0572 - accuracy: 0.9873 - val_loss: 0.1517 - val_accuracy: 0.9583 +Epoch 431/432 +128/128 [==============================] - 42s 327ms/step - loss: 0.0287 - accuracy: 0.9946 - val_loss: 0.1846 - val_accuracy: 0.9599 +Epoch 432/432 +128/128 [==============================] - 47s 364ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.2424 - val_accuracy: 0.9439 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-429-0.9631.h5... +Model Test acc: 0.9615 +Model Test loss: 0.1528 +Model accuracy did not improve from 0.9679487347602844. Not saving model. +Improved model loss from 0.15437141060829163 to 0.15280155837535858. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 353.28 sec +Time taken for epoch(SUBo): 265.48 sec +Time taken for epoch(OTHERo): 87.80 sec +<---------------------------------------|Epoch [72] END|---------------------------------------> + +Epoch: 73/486 (TSEC: 432) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.008]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 433/438 +128/128 [==============================] - 55s 389ms/step - loss: 0.1001 - accuracy: 0.9717 - val_loss: 0.2313 - val_accuracy: 0.9375 +Epoch 434/438 +128/128 [==============================] - 48s 373ms/step - loss: 0.0852 - accuracy: 0.9741 - val_loss: 0.1675 - val_accuracy: 0.9712 +Epoch 435/438 +128/128 [==============================] - 46s 358ms/step - loss: 0.0816 - accuracy: 0.9775 - val_loss: 0.3503 - val_accuracy: 0.9343 +Epoch 436/438 +128/128 [==============================] - 46s 362ms/step - loss: 0.0668 - accuracy: 0.9844 - val_loss: 0.2109 - val_accuracy: 0.9567 +Epoch 437/438 +128/128 [==============================] - 46s 360ms/step - loss: 0.0448 - accuracy: 0.9912 - val_loss: 0.2236 - val_accuracy: 0.9535 +Epoch 438/438 +128/128 [==============================] - 46s 361ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 0.1904 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-434-0.9712.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1676 +Improved model accuracy from 0.9679487347602844 to 0.9695512652397156. Saving model. +Saving full model H5 format... +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.79 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 111.40 sec +<---------------------------------------|Epoch [73] END|---------------------------------------> + +Epoch: 74/486 (TSEC: 438) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00794]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 439/444 +128/128 [==============================] - 56s 388ms/step - loss: 0.1390 - accuracy: 0.9634 - val_loss: 0.1585 - val_accuracy: 0.9696 +Epoch 440/444 +128/128 [==============================] - 46s 362ms/step - loss: 0.0973 - accuracy: 0.9731 - val_loss: 0.2705 - val_accuracy: 0.9663 +Epoch 441/444 +128/128 [==============================] - 46s 360ms/step - loss: 0.0823 - accuracy: 0.9810 - val_loss: 0.2023 - val_accuracy: 0.9615 +Epoch 442/444 +128/128 [==============================] - 47s 362ms/step - loss: 0.0481 - accuracy: 0.9902 - val_loss: 0.2984 - val_accuracy: 0.9455 +Epoch 443/444 +128/128 [==============================] - 46s 356ms/step - loss: 0.0412 - accuracy: 0.9907 - val_loss: 0.1783 - val_accuracy: 0.9663 +Epoch 444/444 +128/128 [==============================] - 47s 367ms/step - loss: 0.0401 - accuracy: 0.9902 - val_loss: 0.3061 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.3061 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.10 sec +Time taken for epoch(SUBo): 288.78 sec +Time taken for epoch(OTHERo): 108.32 sec +<---------------------------------------|Epoch [74] END|---------------------------------------> + +Epoch: 75/486 (TSEC: 444) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00788]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 445/450 +128/128 [==============================] - 56s 390ms/step - loss: 0.1181 - accuracy: 0.9683 - val_loss: 0.2149 - val_accuracy: 0.9647 +Epoch 446/450 +128/128 [==============================] - 45s 355ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1517 - val_accuracy: 0.9647 +Epoch 447/450 +128/128 [==============================] - 47s 363ms/step - loss: 0.0781 - accuracy: 0.9790 - val_loss: 0.1497 - val_accuracy: 0.9631 +Epoch 448/450 +128/128 [==============================] - 46s 362ms/step - loss: 0.0539 - accuracy: 0.9883 - val_loss: 0.3015 - val_accuracy: 0.9407 +Epoch 449/450 +128/128 [==============================] - 47s 367ms/step - loss: 0.0463 - accuracy: 0.9897 - val_loss: 0.2271 - val_accuracy: 0.9551 +Epoch 450/450 +128/128 [==============================] - 47s 366ms/step - loss: 0.0366 - accuracy: 0.9927 - val_loss: 0.2163 - val_accuracy: 0.9551 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-445-0.9647.h5... +Model Test acc: 0.9647 +Model Test loss: 0.2149 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 397.95 sec +Time taken for epoch(SUBo): 289.40 sec +Time taken for epoch(OTHERo): 108.55 sec +<---------------------------------------|Epoch [75] END|---------------------------------------> + +Epoch: 76/486 (TSEC: 450) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00782]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 451/456 +128/128 [==============================] - 55s 386ms/step - loss: 0.0990 - accuracy: 0.9727 - val_loss: 0.1456 - val_accuracy: 0.9599 +Epoch 452/456 +128/128 [==============================] - 46s 360ms/step - loss: 0.1054 - accuracy: 0.9736 - val_loss: 0.2077 - val_accuracy: 0.9567 +Epoch 453/456 +128/128 [==============================] - 47s 362ms/step - loss: 0.0790 - accuracy: 0.9780 - val_loss: 0.2244 - val_accuracy: 0.9551 +Epoch 454/456 +128/128 [==============================] - 48s 374ms/step - loss: 0.0667 - accuracy: 0.9863 - val_loss: 0.1664 - val_accuracy: 0.9679 +Epoch 455/456 +128/128 [==============================] - 47s 366ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.1729 - val_accuracy: 0.9679 +Epoch 456/456 +128/128 [==============================] - 46s 362ms/step - loss: 0.0379 - accuracy: 0.9927 - val_loss: 0.1848 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-454-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1664 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 400.35 sec +Time taken for epoch(SUBo): 290.41 sec +Time taken for epoch(OTHERo): 109.94 sec +<---------------------------------------|Epoch [76] END|---------------------------------------> + +Epoch: 77/486 (TSEC: 456) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00776]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 457/462 +128/128 [==============================] - 55s 383ms/step - loss: 0.1390 - accuracy: 0.9595 - val_loss: 0.1381 - val_accuracy: 0.9551 +Epoch 458/462 +128/128 [==============================] - 48s 373ms/step - loss: 0.1183 - accuracy: 0.9634 - val_loss: 0.1549 - val_accuracy: 0.9696 +Epoch 459/462 +128/128 [==============================] - 46s 362ms/step - loss: 0.0797 - accuracy: 0.9814 - val_loss: 0.1383 - val_accuracy: 0.9663 +Epoch 460/462 +128/128 [==============================] - 46s 359ms/step - loss: 0.0546 - accuracy: 0.9849 - val_loss: 0.2555 - val_accuracy: 0.9583 +Epoch 461/462 +128/128 [==============================] - 47s 364ms/step - loss: 0.0470 - accuracy: 0.9878 - val_loss: 0.3076 - val_accuracy: 0.9519 +Epoch 462/462 +128/128 [==============================] - 47s 363ms/step - loss: 0.0309 - accuracy: 0.9932 - val_loss: 0.2161 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-458-0.9696.h5... +Model Test acc: 0.9696 +Model Test loss: 0.1549 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.15280155837535858. Not saving model. +Time taken for epoch(FULL): 394.70 sec +Time taken for epoch(SUBo): 289.87 sec +Time taken for epoch(OTHERo): 104.83 sec +<---------------------------------------|Epoch [77] END|---------------------------------------> + +Epoch: 78/486 (TSEC: 462) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0077]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 463/468 +128/128 [==============================] - 56s 388ms/step - loss: 0.1240 - accuracy: 0.9663 - val_loss: 0.1783 - val_accuracy: 0.9647 +Epoch 464/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.1061 - accuracy: 0.9717 - val_loss: 0.1403 - val_accuracy: 0.9631 +Epoch 465/468 +128/128 [==============================] - 46s 362ms/step - loss: 0.1005 - accuracy: 0.9761 - val_loss: 0.1963 - val_accuracy: 0.9551 +Epoch 466/468 +128/128 [==============================] - 46s 358ms/step - loss: 0.0686 - accuracy: 0.9844 - val_loss: 0.2210 - val_accuracy: 0.9503 +Epoch 467/468 +128/128 [==============================] - 48s 373ms/step - loss: 0.0445 - accuracy: 0.9897 - val_loss: 0.1364 - val_accuracy: 0.9679 +Epoch 468/468 +128/128 [==============================] - 47s 362ms/step - loss: 0.0433 - accuracy: 0.9902 - val_loss: 0.1595 - val_accuracy: 0.9663 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-467-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1365 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.15280155837535858 to 0.13646124303340912. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 398.75 sec +Time taken for epoch(SUBo): 289.42 sec +Time taken for epoch(OTHERo): 109.33 sec +<---------------------------------------|Epoch [78] END|---------------------------------------> + +Epoch: 79/486 (TSEC: 468) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00764]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 469/474 +128/128 [==============================] - 55s 388ms/step - loss: 0.1236 - accuracy: 0.9634 - val_loss: 0.2019 - val_accuracy: 0.9535 +Epoch 470/474 +128/128 [==============================] - 48s 370ms/step - loss: 0.1163 - accuracy: 0.9639 - val_loss: 0.4542 - val_accuracy: 0.9327 +Epoch 471/474 +128/128 [==============================] - 47s 364ms/step - loss: 0.0889 - accuracy: 0.9829 - val_loss: 0.3764 - val_accuracy: 0.9359 +Epoch 472/474 +128/128 [==============================] - 46s 359ms/step - loss: 0.0747 - accuracy: 0.9868 - val_loss: 0.2739 - val_accuracy: 0.9535 +Epoch 473/474 +128/128 [==============================] - 48s 372ms/step - loss: 0.0530 - accuracy: 0.9912 - val_loss: 0.2042 - val_accuracy: 0.9599 +Epoch 474/474 +128/128 [==============================] - 46s 361ms/step - loss: 0.0402 - accuracy: 0.9917 - val_loss: 0.2347 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2348 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 395.44 sec +Time taken for epoch(SUBo): 291.06 sec +Time taken for epoch(OTHERo): 104.39 sec +<---------------------------------------|Epoch [79] END|---------------------------------------> + +Epoch: 80/486 (TSEC: 474) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00758]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 475/480 +128/128 [==============================] - 56s 390ms/step - loss: 0.0992 - accuracy: 0.9697 - val_loss: 0.2736 - val_accuracy: 0.9519 +Epoch 476/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0677 - accuracy: 0.9844 - val_loss: 0.2986 - val_accuracy: 0.9423 +Epoch 477/480 +128/128 [==============================] - 47s 365ms/step - loss: 0.0500 - accuracy: 0.9868 - val_loss: 0.3489 - val_accuracy: 0.9247 +Epoch 478/480 +128/128 [==============================] - 48s 377ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.2738 - val_accuracy: 0.9599 +Epoch 479/480 +128/128 [==============================] - 48s 379ms/step - loss: 0.0386 - accuracy: 0.9917 - val_loss: 0.2269 - val_accuracy: 0.9647 +Epoch 480/480 +128/128 [==============================] - 46s 358ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2441 - val_accuracy: 0.9583 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9583 +Model Test loss: 0.2441 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 399.87 sec +Time taken for epoch(SUBo): 293.34 sec +Time taken for epoch(OTHERo): 106.54 sec +<---------------------------------------|Epoch [80] END|---------------------------------------> + +Epoch: 81/486 (TSEC: 480) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00752]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 481/486 +128/128 [==============================] - 50s 348ms/step - loss: 0.1021 - accuracy: 0.9736 - val_loss: 0.3309 - val_accuracy: 0.9551 +Epoch 482/486 +128/128 [==============================] - 42s 322ms/step - loss: 0.0918 - accuracy: 0.9722 - val_loss: 0.1656 - val_accuracy: 0.9503 +Epoch 483/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0780 - accuracy: 0.9761 - val_loss: 0.3643 - val_accuracy: 0.9423 +Epoch 484/486 +128/128 [==============================] - 41s 321ms/step - loss: 0.0535 - accuracy: 0.9873 - val_loss: 0.5132 - val_accuracy: 0.9311 +Epoch 485/486 +128/128 [==============================] - 42s 324ms/step - loss: 0.0435 - accuracy: 0.9912 - val_loss: 0.4104 - val_accuracy: 0.9375 +Epoch 486/486 +128/128 [==============================] - 41s 322ms/step - loss: 0.0304 - accuracy: 0.9946 - val_loss: 0.3567 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3567 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.57 sec +Time taken for epoch(SUBo): 258.36 sec +Time taken for epoch(OTHERo): 102.21 sec +<---------------------------------------|Epoch [81] END|---------------------------------------> + +Epoch: 82/486 (TSEC: 486) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00746]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 487/492 +128/128 [==============================] - 48s 339ms/step - loss: 0.1181 - accuracy: 0.9644 - val_loss: 0.3261 - val_accuracy: 0.9343 +Epoch 488/492 +128/128 [==============================] - 42s 328ms/step - loss: 0.1203 - accuracy: 0.9668 - val_loss: 0.1990 - val_accuracy: 0.9375 +Epoch 489/492 +128/128 [==============================] - 41s 320ms/step - loss: 0.0787 - accuracy: 0.9780 - val_loss: 0.5460 - val_accuracy: 0.9071 +Epoch 490/492 +128/128 [==============================] - 41s 321ms/step - loss: 0.0567 - accuracy: 0.9897 - val_loss: 0.4894 - val_accuracy: 0.9135 +Epoch 491/492 +128/128 [==============================] - 42s 327ms/step - loss: 0.0534 - accuracy: 0.9849 - val_loss: 0.2948 - val_accuracy: 0.9503 +Epoch 492/492 +128/128 [==============================] - 42s 324ms/step - loss: 0.0316 - accuracy: 0.9951 - val_loss: 0.2877 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2877 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 338.30 sec +Time taken for epoch(SUBo): 256.81 sec +Time taken for epoch(OTHERo): 81.49 sec +<---------------------------------------|Epoch [82] END|---------------------------------------> + +Epoch: 83/486 (TSEC: 492) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0074]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 493/498 +128/128 [==============================] - 48s 342ms/step - loss: 0.1130 - accuracy: 0.9668 - val_loss: 0.2289 - val_accuracy: 0.9503 +Epoch 494/498 +128/128 [==============================] - 41s 321ms/step - loss: 0.0878 - accuracy: 0.9736 - val_loss: 0.3001 - val_accuracy: 0.9359 +Epoch 495/498 +128/128 [==============================] - 42s 330ms/step - loss: 0.0704 - accuracy: 0.9790 - val_loss: 0.2279 - val_accuracy: 0.9551 +Epoch 496/498 +128/128 [==============================] - 42s 329ms/step - loss: 0.0593 - accuracy: 0.9878 - val_loss: 0.3802 - val_accuracy: 0.9343 +Epoch 497/498 +128/128 [==============================] - 43s 331ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 0.3153 - val_accuracy: 0.9391 +Epoch 498/498 +128/128 [==============================] - 43s 334ms/step - loss: 0.0315 - accuracy: 0.9932 - val_loss: 0.3007 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3008 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 341.92 sec +Time taken for epoch(SUBo): 260.54 sec +Time taken for epoch(OTHERo): 81.38 sec +<---------------------------------------|Epoch [83] END|---------------------------------------> + +Epoch: 84/486 (TSEC: 498) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00734]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 499/504 +128/128 [==============================] - 57s 400ms/step - loss: 0.1055 - accuracy: 0.9678 - val_loss: 0.2486 - val_accuracy: 0.9247 +Epoch 500/504 +128/128 [==============================] - 47s 364ms/step - loss: 0.0761 - accuracy: 0.9766 - val_loss: 0.7516 - val_accuracy: 0.9103 +Epoch 501/504 +128/128 [==============================] - 48s 375ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.4233 - val_accuracy: 0.9263 +Epoch 502/504 +128/128 [==============================] - 49s 379ms/step - loss: 0.0310 - accuracy: 0.9902 - val_loss: 0.4898 - val_accuracy: 0.9343 +Epoch 503/504 +128/128 [==============================] - 48s 372ms/step - loss: 0.0374 - accuracy: 0.9937 - val_loss: 0.2883 - val_accuracy: 0.9359 +Epoch 504/504 +128/128 [==============================] - 47s 367ms/step - loss: 0.0299 - accuracy: 0.9951 - val_loss: 0.3369 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3369 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 401.59 sec +Time taken for epoch(SUBo): 296.36 sec +Time taken for epoch(OTHERo): 105.23 sec +<---------------------------------------|Epoch [84] END|---------------------------------------> + +Epoch: 85/486 (TSEC: 504) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00728]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 505/510 +128/128 [==============================] - 56s 388ms/step - loss: 0.1190 - accuracy: 0.9668 - val_loss: 0.2573 - val_accuracy: 0.9343 +Epoch 506/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.2088 - val_accuracy: 0.9487 +Epoch 507/510 +128/128 [==============================] - 44s 340ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.1526 - val_accuracy: 0.9535 +Epoch 508/510 +128/128 [==============================] - 43s 339ms/step - loss: 0.0554 - accuracy: 0.9878 - val_loss: 0.1452 - val_accuracy: 0.9631 +Epoch 509/510 +128/128 [==============================] - 42s 329ms/step - loss: 0.0350 - accuracy: 0.9927 - val_loss: 0.2356 - val_accuracy: 0.9519 +Epoch 510/510 +128/128 [==============================] - 42s 328ms/step - loss: 0.0263 - accuracy: 0.9951 - val_loss: 0.2356 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.2355 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 378.93 sec +Time taken for epoch(SUBo): 271.88 sec +Time taken for epoch(OTHERo): 107.05 sec +<---------------------------------------|Epoch [85] END|---------------------------------------> + +Epoch: 86/486 (TSEC: 510) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00722]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 511/516 +128/128 [==============================] - 50s 355ms/step - loss: 0.1288 - accuracy: 0.9653 - val_loss: 0.2051 - val_accuracy: 0.9455 +Epoch 512/516 +128/128 [==============================] - 44s 339ms/step - loss: 0.0972 - accuracy: 0.9736 - val_loss: 0.1744 - val_accuracy: 0.9567 +Epoch 513/516 +128/128 [==============================] - 43s 333ms/step - loss: 0.0873 - accuracy: 0.9761 - val_loss: 0.3731 - val_accuracy: 0.9279 +Epoch 514/516 +128/128 [==============================] - 42s 328ms/step - loss: 0.0441 - accuracy: 0.9907 - val_loss: 0.2860 - val_accuracy: 0.9423 +Epoch 515/516 +128/128 [==============================] - 43s 331ms/step - loss: 0.0419 - accuracy: 0.9893 - val_loss: 0.2127 - val_accuracy: 0.9567 +Epoch 516/516 +128/128 [==============================] - 42s 330ms/step - loss: 0.0388 - accuracy: 0.9917 - val_loss: 0.2163 - val_accuracy: 0.9567 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9567 +Model Test loss: 0.2163 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 348.35 sec +Time taken for epoch(SUBo): 264.53 sec +Time taken for epoch(OTHERo): 83.82 sec +<---------------------------------------|Epoch [86] END|---------------------------------------> + +Epoch: 87/486 (TSEC: 516) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00716]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 517/522 +128/128 [==============================] - 50s 353ms/step - loss: 0.0925 - accuracy: 0.9751 - val_loss: 0.3125 - val_accuracy: 0.9327 +Epoch 518/522 +128/128 [==============================] - 44s 342ms/step - loss: 0.0803 - accuracy: 0.9761 - val_loss: 0.3269 - val_accuracy: 0.9375 +Epoch 519/522 +128/128 [==============================] - 42s 329ms/step - loss: 0.0505 - accuracy: 0.9863 - val_loss: 0.5778 - val_accuracy: 0.9327 +Epoch 520/522 +128/128 [==============================] - 43s 331ms/step - loss: 0.0537 - accuracy: 0.9888 - val_loss: 0.3902 - val_accuracy: 0.9215 +Epoch 521/522 +128/128 [==============================] - 43s 338ms/step - loss: 0.0521 - accuracy: 0.9878 - val_loss: 0.3016 - val_accuracy: 0.9535 +Epoch 522/522 +128/128 [==============================] - 42s 328ms/step - loss: 0.0288 - accuracy: 0.9946 - val_loss: 0.3130 - val_accuracy: 0.9519 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9519 +Model Test loss: 0.3130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.32 sec +Time taken for epoch(SUBo): 265.09 sec +Time taken for epoch(OTHERo): 84.23 sec +<---------------------------------------|Epoch [87] END|---------------------------------------> + +Epoch: 88/486 (TSEC: 522) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0071]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 523/528 +128/128 [==============================] - 49s 345ms/step - loss: 0.1157 - accuracy: 0.9648 - val_loss: 0.4114 - val_accuracy: 0.9471 +Epoch 524/528 +128/128 [==============================] - 43s 336ms/step - loss: 0.0814 - accuracy: 0.9722 - val_loss: 0.2807 - val_accuracy: 0.9503 +Epoch 525/528 +128/128 [==============================] - 42s 326ms/step - loss: 0.0653 - accuracy: 0.9854 - val_loss: 0.2715 - val_accuracy: 0.9471 +Epoch 526/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.3749 - val_accuracy: 0.9439 +Epoch 527/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0390 - accuracy: 0.9907 - val_loss: 0.3434 - val_accuracy: 0.9455 +Epoch 528/528 +128/128 [==============================] - 42s 327ms/step - loss: 0.0319 - accuracy: 0.9932 - val_loss: 0.3755 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3755 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.31 sec +Time taken for epoch(SUBo): 260.67 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [88] END|---------------------------------------> + +Epoch: 89/486 (TSEC: 528) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00704]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 529/534 +128/128 [==============================] - 49s 347ms/step - loss: 0.0911 - accuracy: 0.9756 - val_loss: 0.2770 - val_accuracy: 0.9487 +Epoch 530/534 +128/128 [==============================] - 43s 335ms/step - loss: 0.0782 - accuracy: 0.9756 - val_loss: 0.1748 - val_accuracy: 0.9615 +Epoch 531/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0676 - accuracy: 0.9819 - val_loss: 0.1458 - val_accuracy: 0.9599 +Epoch 532/534 +128/128 [==============================] - 43s 336ms/step - loss: 0.0746 - accuracy: 0.9805 - val_loss: 0.1397 - val_accuracy: 0.9631 +Epoch 533/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0371 - accuracy: 0.9927 - val_loss: 0.1476 - val_accuracy: 0.9615 +Epoch 534/534 +128/128 [==============================] - 42s 326ms/step - loss: 0.0324 - accuracy: 0.9932 - val_loss: 0.1451 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1451 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 344.88 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 83.03 sec +<---------------------------------------|Epoch [89] END|---------------------------------------> + +Epoch: 90/486 (TSEC: 534) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00698]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 535/540 +128/128 [==============================] - 54s 389ms/step - loss: 0.1021 - accuracy: 0.9712 - val_loss: 0.2036 - val_accuracy: 0.9615 +Epoch 536/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0805 - accuracy: 0.9775 - val_loss: 0.1570 - val_accuracy: 0.9551 +Epoch 537/540 +128/128 [==============================] - 47s 363ms/step - loss: 0.0695 - accuracy: 0.9839 - val_loss: 0.3015 - val_accuracy: 0.9471 +Epoch 538/540 +128/128 [==============================] - 47s 364ms/step - loss: 0.0550 - accuracy: 0.9907 - val_loss: 0.2314 - val_accuracy: 0.9519 +Epoch 539/540 +128/128 [==============================] - 47s 365ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2381 - val_accuracy: 0.9567 +Epoch 540/540 +128/128 [==============================] - 48s 372ms/step - loss: 0.0442 - accuracy: 0.9932 - val_loss: 0.2261 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2261 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 376.02 sec +Time taken for epoch(SUBo): 290.31 sec +Time taken for epoch(OTHERo): 85.71 sec +<---------------------------------------|Epoch [90] END|---------------------------------------> + +Epoch: 91/486 (TSEC: 540) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00692]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 541/546 +128/128 [==============================] - 57s 396ms/step - loss: 0.1000 - accuracy: 0.9663 - val_loss: 0.3696 - val_accuracy: 0.9263 +Epoch 542/546 +128/128 [==============================] - 48s 378ms/step - loss: 0.0823 - accuracy: 0.9775 - val_loss: 0.2302 - val_accuracy: 0.9487 +Epoch 543/546 +128/128 [==============================] - 47s 369ms/step - loss: 0.0578 - accuracy: 0.9863 - val_loss: 0.2219 - val_accuracy: 0.9439 +Epoch 544/546 +128/128 [==============================] - 47s 364ms/step - loss: 0.0585 - accuracy: 0.9863 - val_loss: 0.3012 - val_accuracy: 0.9423 +Epoch 545/546 +128/128 [==============================] - 47s 366ms/step - loss: 0.0437 - accuracy: 0.9902 - val_loss: 0.2474 - val_accuracy: 0.9471 +Epoch 546/546 +128/128 [==============================] - 46s 362ms/step - loss: 0.0295 - accuracy: 0.9937 - val_loss: 0.2810 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.2810 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 409.06 sec +Time taken for epoch(SUBo): 293.27 sec +Time taken for epoch(OTHERo): 115.79 sec +<---------------------------------------|Epoch [91] END|---------------------------------------> + +Epoch: 92/486 (TSEC: 546) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00686]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 547/552 +128/128 [==============================] - 56s 390ms/step - loss: 0.1045 - accuracy: 0.9692 - val_loss: 0.2284 - val_accuracy: 0.9439 +Epoch 548/552 +128/128 [==============================] - 48s 375ms/step - loss: 0.0943 - accuracy: 0.9731 - val_loss: 0.1996 - val_accuracy: 0.9471 +Epoch 549/552 +128/128 [==============================] - 47s 367ms/step - loss: 0.0772 - accuracy: 0.9824 - val_loss: 0.5513 - val_accuracy: 0.9215 +Epoch 550/552 +128/128 [==============================] - 46s 362ms/step - loss: 0.0680 - accuracy: 0.9800 - val_loss: 0.3947 - val_accuracy: 0.9391 +Epoch 551/552 +128/128 [==============================] - 49s 379ms/step - loss: 0.0417 - accuracy: 0.9912 - val_loss: 0.2647 - val_accuracy: 0.9503 +Epoch 552/552 +128/128 [==============================] - 43s 334ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.2734 - val_accuracy: 0.9487 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9487 +Model Test loss: 0.2734 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 402.95 sec +Time taken for epoch(SUBo): 289.90 sec +Time taken for epoch(OTHERo): 113.04 sec +<---------------------------------------|Epoch [92] END|---------------------------------------> + +Epoch: 93/486 (TSEC: 552) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0068]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 553/558 +128/128 [==============================] - 49s 345ms/step - loss: 0.0998 - accuracy: 0.9717 - val_loss: 0.3897 - val_accuracy: 0.9407 +Epoch 554/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.1178 - accuracy: 0.9648 - val_loss: 0.7295 - val_accuracy: 0.9103 +Epoch 555/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0852 - accuracy: 0.9829 - val_loss: 0.3859 - val_accuracy: 0.9343 +Epoch 556/558 +128/128 [==============================] - 42s 326ms/step - loss: 0.0480 - accuracy: 0.9932 - val_loss: 0.4026 - val_accuracy: 0.9327 +Epoch 557/558 +128/128 [==============================] - 41s 323ms/step - loss: 0.0356 - accuracy: 0.9946 - val_loss: 0.4769 - val_accuracy: 0.9295 +Epoch 558/558 +128/128 [==============================] - 42s 323ms/step - loss: 0.0462 - accuracy: 0.9941 - val_loss: 0.4314 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4314 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 343.82 sec +Time taken for epoch(SUBo): 258.19 sec +Time taken for epoch(OTHERo): 85.63 sec +<---------------------------------------|Epoch [93] END|---------------------------------------> + +Epoch: 94/486 (TSEC: 558) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00674]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 559/564 +128/128 [==============================] - 49s 350ms/step - loss: 0.1437 - accuracy: 0.9619 - val_loss: 0.3620 - val_accuracy: 0.9231 +Epoch 560/564 +128/128 [==============================] - 43s 338ms/step - loss: 0.1225 - accuracy: 0.9644 - val_loss: 0.2005 - val_accuracy: 0.9519 +Epoch 561/564 +128/128 [==============================] - 42s 326ms/step - loss: 0.0842 - accuracy: 0.9731 - val_loss: 0.2442 - val_accuracy: 0.9455 +Epoch 562/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0519 - accuracy: 0.9883 - val_loss: 0.2336 - val_accuracy: 0.9503 +Epoch 563/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0724 - accuracy: 0.9849 - val_loss: 0.2655 - val_accuracy: 0.9359 +Epoch 564/564 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9897 - val_loss: 0.2974 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2974 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.85 sec +Time taken for epoch(SUBo): 261.88 sec +Time taken for epoch(OTHERo): 85.97 sec +<---------------------------------------|Epoch [94] END|---------------------------------------> + +Epoch: 95/486 (TSEC: 564) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00668]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 565/570 +128/128 [==============================] - 49s 345ms/step - loss: 0.1133 - accuracy: 0.9624 - val_loss: 0.2351 - val_accuracy: 0.9455 +Epoch 566/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.2868 - val_accuracy: 0.9279 +Epoch 567/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0650 - accuracy: 0.9849 - val_loss: 0.4724 - val_accuracy: 0.9183 +Epoch 568/570 +128/128 [==============================] - 43s 333ms/step - loss: 0.0524 - accuracy: 0.9863 - val_loss: 0.2410 - val_accuracy: 0.9503 +Epoch 569/570 +128/128 [==============================] - 42s 326ms/step - loss: 0.0283 - accuracy: 0.9941 - val_loss: 0.3503 - val_accuracy: 0.9391 +Epoch 570/570 +128/128 [==============================] - 42s 327ms/step - loss: 0.0269 - accuracy: 0.9922 - val_loss: 0.4469 - val_accuracy: 0.9231 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.4469 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 349.57 sec +Time taken for epoch(SUBo): 260.42 sec +Time taken for epoch(OTHERo): 89.15 sec +<---------------------------------------|Epoch [95] END|---------------------------------------> + +Epoch: 96/486 (TSEC: 570) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h14_m33_s33 +Setting training OneCycleLr::maxlr to [0.00662]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 571/576 +128/128 [==============================] - 49s 346ms/step - loss: 0.1014 - accuracy: 0.9683 - val_loss: 0.3923 - val_accuracy: 0.9247 +Epoch 572/576 +128/128 [==============================] - 42s 327ms/step - loss: 0.0886 - accuracy: 0.9751 - val_loss: 0.4301 - val_accuracy: 0.8958 +Epoch 573/576 +128/128 [==============================] - 43s 336ms/step - loss: 0.0618 - accuracy: 0.9849 - val_loss: 0.2419 - val_accuracy: 0.9455 +Epoch 574/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0496 - accuracy: 0.9888 - val_loss: 0.2643 - val_accuracy: 0.9343 +Epoch 575/576 +128/128 [==============================] - 42s 329ms/step - loss: 0.0247 - accuracy: 0.9976 - val_loss: 0.3082 - val_accuracy: 0.9391 +Epoch 576/576 +128/128 [==============================] - 42s 328ms/step - loss: 0.0486 - accuracy: 0.9922 - val_loss: 0.3027 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3027 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 360.90 sec +Time taken for epoch(SUBo): 261.28 sec +Time taken for epoch(OTHERo): 99.62 sec +<---------------------------------------|Epoch [96] END|---------------------------------------> + +Epoch: 97/486 (TSEC: 576) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00656]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 577/582 +128/128 [==============================] - 49s 344ms/step - loss: 0.1249 - accuracy: 0.9692 - val_loss: 0.3547 - val_accuracy: 0.9295 +Epoch 578/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.1017 - accuracy: 0.9673 - val_loss: 0.4032 - val_accuracy: 0.9375 +Epoch 579/582 +128/128 [==============================] - 43s 336ms/step - loss: 0.0819 - accuracy: 0.9795 - val_loss: 0.2126 - val_accuracy: 0.9535 +Epoch 580/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0547 - accuracy: 0.9878 - val_loss: 0.3177 - val_accuracy: 0.9487 +Epoch 581/582 +128/128 [==============================] - 42s 328ms/step - loss: 0.0372 - accuracy: 0.9946 - val_loss: 0.3847 - val_accuracy: 0.9359 +Epoch 582/582 +128/128 [==============================] - 42s 326ms/step - loss: 0.0351 - accuracy: 0.9961 - val_loss: 0.3619 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3618 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.27 sec +Time taken for epoch(SUBo): 261.85 sec +Time taken for epoch(OTHERo): 84.42 sec +<---------------------------------------|Epoch [97] END|---------------------------------------> + +Epoch: 98/486 (TSEC: 582) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0065]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 583/588 +128/128 [==============================] - 49s 347ms/step - loss: 0.1029 - accuracy: 0.9712 - val_loss: 0.3526 - val_accuracy: 0.9295 +Epoch 584/588 +128/128 [==============================] - 43s 333ms/step - loss: 0.0843 - accuracy: 0.9731 - val_loss: 0.2799 - val_accuracy: 0.9423 +Epoch 585/588 +128/128 [==============================] - 43s 334ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.2782 - val_accuracy: 0.9455 +Epoch 586/588 +128/128 [==============================] - 43s 336ms/step - loss: 0.0295 - accuracy: 0.9951 - val_loss: 0.2428 - val_accuracy: 0.9535 +Epoch 587/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0440 - accuracy: 0.9932 - val_loss: 0.3428 - val_accuracy: 0.9503 +Epoch 588/588 +128/128 [==============================] - 42s 327ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.3557 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.3557 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.51 sec +Time taken for epoch(SUBo): 262.33 sec +Time taken for epoch(OTHERo): 83.18 sec +<---------------------------------------|Epoch [98] END|---------------------------------------> + +Epoch: 99/486 (TSEC: 588) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00644]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 589/594 +128/128 [==============================] - 49s 346ms/step - loss: 0.1360 - accuracy: 0.9619 - val_loss: 0.2512 - val_accuracy: 0.9423 +Epoch 590/594 +128/128 [==============================] - 42s 328ms/step - loss: 0.1001 - accuracy: 0.9736 - val_loss: 0.3333 - val_accuracy: 0.9423 +Epoch 591/594 +128/128 [==============================] - 42s 326ms/step - loss: 0.0671 - accuracy: 0.9844 - val_loss: 0.3686 - val_accuracy: 0.9375 +Epoch 592/594 +128/128 [==============================] - 43s 334ms/step - loss: 0.0472 - accuracy: 0.9873 - val_loss: 0.2774 - val_accuracy: 0.9455 +Epoch 593/594 +128/128 [==============================] - 43s 336ms/step - loss: 0.0326 - accuracy: 0.9941 - val_loss: 0.3143 - val_accuracy: 0.9471 +Epoch 594/594 +128/128 [==============================] - 43s 331ms/step - loss: 0.0460 - accuracy: 0.9917 - val_loss: 0.3592 - val_accuracy: 0.9391 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9391 +Model Test loss: 0.3592 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.37 sec +Time taken for epoch(SUBo): 262.28 sec +Time taken for epoch(OTHERo): 85.09 sec +<---------------------------------------|Epoch [99] END|---------------------------------------> + +Epoch: 100/486 (TSEC: 594) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00638]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 595/600 +128/128 [==============================] - 49s 345ms/step - loss: 0.1055 - accuracy: 0.9702 - val_loss: 0.4399 - val_accuracy: 0.9407 +Epoch 596/600 +128/128 [==============================] - 42s 327ms/step - loss: 0.0850 - accuracy: 0.9771 - val_loss: 0.3725 - val_accuracy: 0.9359 +Epoch 597/600 +128/128 [==============================] - 42s 326ms/step - loss: 0.0574 - accuracy: 0.9849 - val_loss: 0.3704 - val_accuracy: 0.9311 +Epoch 598/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0535 - accuracy: 0.9883 - val_loss: 0.2328 - val_accuracy: 0.9439 +Epoch 599/600 +128/128 [==============================] - 43s 335ms/step - loss: 0.0262 - accuracy: 0.9961 - val_loss: 0.2658 - val_accuracy: 0.9455 +Epoch 600/600 +128/128 [==============================] - 43s 336ms/step - loss: 0.0221 - accuracy: 0.9966 - val_loss: 0.3042 - val_accuracy: 0.9471 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9471 +Model Test loss: 0.3042 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 345.54 sec +Time taken for epoch(SUBo): 263.28 sec +Time taken for epoch(OTHERo): 82.26 sec +<---------------------------------------|Epoch [100] END|---------------------------------------> + +Epoch: 101/486 (TSEC: 600) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00632]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 601/606 +128/128 [==============================] - 49s 346ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1876 - val_accuracy: 0.9503 +Epoch 602/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0868 - accuracy: 0.9751 - val_loss: 0.2915 - val_accuracy: 0.9311 +Epoch 603/606 +128/128 [==============================] - 42s 326ms/step - loss: 0.0694 - accuracy: 0.9824 - val_loss: 0.3071 - val_accuracy: 0.9487 +Epoch 604/606 +128/128 [==============================] - 42s 327ms/step - loss: 0.0484 - accuracy: 0.9893 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 605/606 +128/128 [==============================] - 43s 337ms/step - loss: 0.0338 - accuracy: 0.9941 - val_loss: 0.1841 - val_accuracy: 0.9583 +Epoch 606/606 +128/128 [==============================] - 43s 335ms/step - loss: 0.0495 - accuracy: 0.9912 - val_loss: 0.1756 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.1757 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 347.57 sec +Time taken for epoch(SUBo): 261.73 sec +Time taken for epoch(OTHERo): 85.84 sec +<---------------------------------------|Epoch [101] END|---------------------------------------> + +Epoch: 102/486 (TSEC: 606) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00626]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 607/612 +128/128 [==============================] - 49s 349ms/step - loss: 0.0822 - accuracy: 0.9795 - val_loss: 0.2293 - val_accuracy: 0.9471 +Epoch 608/612 +128/128 [==============================] - 43s 333ms/step - loss: 0.0747 - accuracy: 0.9746 - val_loss: 0.2679 - val_accuracy: 0.9423 +Epoch 609/612 +128/128 [==============================] - 43s 336ms/step - loss: 0.0469 - accuracy: 0.9849 - val_loss: 0.4591 - val_accuracy: 0.9247 +Epoch 610/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0353 - accuracy: 0.9922 - val_loss: 0.4351 - val_accuracy: 0.9103 +Epoch 611/612 +128/128 [==============================] - 43s 331ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.5212 - val_accuracy: 0.9215 +Epoch 612/612 +128/128 [==============================] - 42s 331ms/step - loss: 0.0188 - accuracy: 0.9971 - val_loss: 0.4658 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.4659 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 350.48 sec +Time taken for epoch(SUBo): 263.62 sec +Time taken for epoch(OTHERo): 86.85 sec +<---------------------------------------|Epoch [102] END|---------------------------------------> + +Epoch: 103/486 (TSEC: 612) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0062]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 613/618 +128/128 [==============================] - 51s 358ms/step - loss: 0.1201 - accuracy: 0.9663 - val_loss: 0.3077 - val_accuracy: 0.9231 +Epoch 614/618 +128/128 [==============================] - 44s 340ms/step - loss: 0.0837 - accuracy: 0.9756 - val_loss: 0.2011 - val_accuracy: 0.9519 +Epoch 615/618 +128/128 [==============================] - 43s 335ms/step - loss: 0.0621 - accuracy: 0.9829 - val_loss: 0.2583 - val_accuracy: 0.9327 +Epoch 616/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0479 - accuracy: 0.9893 - val_loss: 0.2363 - val_accuracy: 0.9503 +Epoch 617/618 +128/128 [==============================] - 42s 329ms/step - loss: 0.0483 - accuracy: 0.9922 - val_loss: 0.3363 - val_accuracy: 0.9407 +Epoch 618/618 +128/128 [==============================] - 42s 328ms/step - loss: 0.0310 - accuracy: 0.9932 - val_loss: 0.3278 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3278 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 356.91 sec +Time taken for epoch(SUBo): 264.67 sec +Time taken for epoch(OTHERo): 92.23 sec +<---------------------------------------|Epoch [103] END|---------------------------------------> + +Epoch: 104/486 (TSEC: 618) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00614]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 619/624 +128/128 [==============================] - 49s 348ms/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.2832 - val_accuracy: 0.9407 +Epoch 620/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0596 - accuracy: 0.9819 - val_loss: 0.4066 - val_accuracy: 0.9087 +Epoch 621/624 +128/128 [==============================] - 42s 328ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.6121 - val_accuracy: 0.8926 +Epoch 622/624 +128/128 [==============================] - 42s 327ms/step - loss: 0.0442 - accuracy: 0.9902 - val_loss: 0.3556 - val_accuracy: 0.9327 +Epoch 623/624 +128/128 [==============================] - 42s 330ms/step - loss: 0.0280 - accuracy: 0.9937 - val_loss: 0.3831 - val_accuracy: 0.9359 +Epoch 624/624 +128/128 [==============================] - 42s 329ms/step - loss: 0.0178 - accuracy: 0.9980 - val_loss: 0.4054 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.4053 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 346.90 sec +Time taken for epoch(SUBo): 260.79 sec +Time taken for epoch(OTHERo): 86.11 sec +<---------------------------------------|Epoch [104] END|---------------------------------------> + +Epoch: 105/486 (TSEC: 624) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00608]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 625/630 +128/128 [==============================] - 49s 347ms/step - loss: 0.0906 - accuracy: 0.9746 - val_loss: 0.1581 - val_accuracy: 0.9551 +Epoch 626/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0754 - accuracy: 0.9785 - val_loss: 0.2239 - val_accuracy: 0.9471 +Epoch 627/630 +128/128 [==============================] - 42s 330ms/step - loss: 0.0570 - accuracy: 0.9844 - val_loss: 0.3508 - val_accuracy: 0.9423 +Epoch 628/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0397 - accuracy: 0.9912 - val_loss: 0.2305 - val_accuracy: 0.9567 +Epoch 629/630 +128/128 [==============================] - 43s 337ms/step - loss: 0.0239 - accuracy: 0.9941 - val_loss: 0.2097 - val_accuracy: 0.9615 +Epoch 630/630 +128/128 [==============================] - 43s 339ms/step - loss: 0.0178 - accuracy: 0.9966 - val_loss: 0.2148 - val_accuracy: 0.9631 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9631 +Model Test loss: 0.2148 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.13646124303340912. Not saving model. +Time taken for epoch(FULL): 353.04 sec +Time taken for epoch(SUBo): 264.40 sec +Time taken for epoch(OTHERo): 88.64 sec +<---------------------------------------|Epoch [105] END|---------------------------------------> + +Epoch: 106/486 (TSEC: 630) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00602]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 631/636 +128/128 [==============================] - 49s 349ms/step - loss: 0.1236 - accuracy: 0.9702 - val_loss: 0.1612 - val_accuracy: 0.9631 +Epoch 632/636 +128/128 [==============================] - 44s 343ms/step - loss: 0.0991 - accuracy: 0.9731 - val_loss: 0.1188 - val_accuracy: 0.9679 +Epoch 633/636 +128/128 [==============================] - 42s 327ms/step - loss: 0.0779 - accuracy: 0.9790 - val_loss: 0.2146 - val_accuracy: 0.9519 +Epoch 634/636 +128/128 [==============================] - 42s 329ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.1536 - val_accuracy: 0.9663 +Epoch 635/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0356 - accuracy: 0.9941 - val_loss: 0.1870 - val_accuracy: 0.9583 +Epoch 636/636 +128/128 [==============================] - 42s 330ms/step - loss: 0.0419 - accuracy: 0.9927 - val_loss: 0.1689 - val_accuracy: 0.9647 +Subset training done. +Loading the best weights... +Loading weights from file cache\model_SUB_checkpoint-632-0.9679.h5... +Model Test acc: 0.9679 +Model Test loss: 0.1188 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Improved model loss from 0.13646124303340912 to 0.11880630999803543. Saving model. +Saving full model H5 format... +Time taken for epoch(FULL): 356.65 sec +Time taken for epoch(SUBo): 263.16 sec +Time taken for epoch(OTHERo): 93.49 sec +<---------------------------------------|Epoch [106] END|---------------------------------------> + +Epoch: 107/486 (TSEC: 636) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00596]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 637/642 +128/128 [==============================] - 50s 352ms/step - loss: 0.0939 - accuracy: 0.9692 - val_loss: 0.1498 - val_accuracy: 0.9647 +Epoch 638/642 +128/128 [==============================] - 42s 327ms/step - loss: 0.0891 - accuracy: 0.9727 - val_loss: 0.2134 - val_accuracy: 0.9439 +Epoch 639/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0668 - accuracy: 0.9814 - val_loss: 0.2525 - val_accuracy: 0.9487 +Epoch 640/642 +128/128 [==============================] - 42s 326ms/step - loss: 0.0550 - accuracy: 0.9854 - val_loss: 0.1864 - val_accuracy: 0.9535 +Epoch 641/642 +128/128 [==============================] - 42s 328ms/step - loss: 0.0366 - accuracy: 0.9912 - val_loss: 0.2646 - val_accuracy: 0.9439 +Epoch 642/642 +128/128 [==============================] - 42s 329ms/step - loss: 0.0240 - accuracy: 0.9946 - val_loss: 0.2388 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2388 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 353.97 sec +Time taken for epoch(SUBo): 260.86 sec +Time taken for epoch(OTHERo): 93.11 sec +<---------------------------------------|Epoch [107] END|---------------------------------------> + +Epoch: 108/486 (TSEC: 642) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0059]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 643/648 +128/128 [==============================] - 49s 346ms/step - loss: 0.0979 - accuracy: 0.9702 - val_loss: 0.1803 - val_accuracy: 0.9583 +Epoch 644/648 +128/128 [==============================] - 42s 329ms/step - loss: 0.0813 - accuracy: 0.9731 - val_loss: 0.3182 - val_accuracy: 0.9455 +Epoch 645/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.1875 - val_accuracy: 0.9391 +Epoch 646/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0485 - accuracy: 0.9883 - val_loss: 0.3757 - val_accuracy: 0.9423 +Epoch 647/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0386 - accuracy: 0.9897 - val_loss: 0.2920 - val_accuracy: 0.9423 +Epoch 648/648 +128/128 [==============================] - 42s 328ms/step - loss: 0.0364 - accuracy: 0.9937 - val_loss: 0.2612 - val_accuracy: 0.9455 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9455 +Model Test loss: 0.2612 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 351.69 sec +Time taken for epoch(SUBo): 260.60 sec +Time taken for epoch(OTHERo): 91.10 sec +<---------------------------------------|Epoch [108] END|---------------------------------------> + +Epoch: 109/486 (TSEC: 648) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00584]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 649/654 +128/128 [==============================] - 49s 346ms/step - loss: 0.1093 - accuracy: 0.9717 - val_loss: 0.1765 - val_accuracy: 0.9439 +Epoch 650/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0902 - accuracy: 0.9717 - val_loss: 0.2196 - val_accuracy: 0.9407 +Epoch 651/654 +128/128 [==============================] - 42s 327ms/step - loss: 0.0493 - accuracy: 0.9863 - val_loss: 0.3312 - val_accuracy: 0.9359 +Epoch 652/654 +128/128 [==============================] - 42s 326ms/step - loss: 0.0455 - accuracy: 0.9873 - val_loss: 0.2006 - val_accuracy: 0.9423 +Epoch 653/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0234 - accuracy: 0.9956 - val_loss: 0.3040 - val_accuracy: 0.9359 +Epoch 654/654 +128/128 [==============================] - 42s 328ms/step - loss: 0.0216 - accuracy: 0.9961 - val_loss: 0.3569 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.3569 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 346.32 sec +Time taken for epoch(SUBo): 259.69 sec +Time taken for epoch(OTHERo): 86.63 sec +<---------------------------------------|Epoch [109] END|---------------------------------------> + +Epoch: 110/486 (TSEC: 654) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00578]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 655/660 +128/128 [==============================] - 49s 347ms/step - loss: 0.0857 - accuracy: 0.9756 - val_loss: 0.2740 - val_accuracy: 0.9471 +Epoch 656/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0733 - accuracy: 0.9775 - val_loss: 0.3784 - val_accuracy: 0.9295 +Epoch 657/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0496 - accuracy: 0.9878 - val_loss: 0.3583 - val_accuracy: 0.9327 +Epoch 658/660 +128/128 [==============================] - 43s 334ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.3505 - val_accuracy: 0.9503 +Epoch 659/660 +128/128 [==============================] - 42s 327ms/step - loss: 0.0246 - accuracy: 0.9946 - val_loss: 0.4279 - val_accuracy: 0.9423 +Epoch 660/660 +128/128 [==============================] - 42s 328ms/step - loss: 0.0183 - accuracy: 0.9971 - val_loss: 0.3958 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3959 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 347.66 sec +Time taken for epoch(SUBo): 261.10 sec +Time taken for epoch(OTHERo): 86.56 sec +<---------------------------------------|Epoch [110] END|---------------------------------------> + +Epoch: 111/486 (TSEC: 660) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00572]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 661/666 +128/128 [==============================] - 49s 347ms/step - loss: 0.0916 - accuracy: 0.9756 - val_loss: 0.4056 - val_accuracy: 0.9471 +Epoch 662/666 +128/128 [==============================] - 47s 367ms/step - loss: 0.0709 - accuracy: 0.9795 - val_loss: 0.3773 - val_accuracy: 0.9439 +Epoch 663/666 +128/128 [==============================] - 48s 377ms/step - loss: 0.0633 - accuracy: 0.9805 - val_loss: 0.2007 - val_accuracy: 0.9679 +Epoch 664/666 +128/128 [==============================] - 47s 366ms/step - loss: 0.0413 - accuracy: 0.9888 - val_loss: 0.2294 - val_accuracy: 0.9583 +Epoch 665/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0291 - accuracy: 0.9946 - val_loss: 0.2969 - val_accuracy: 0.9535 +Epoch 666/666 +128/128 [==============================] - 47s 369ms/step - loss: 0.0205 - accuracy: 0.9971 - val_loss: 0.2614 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2614 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 374.77 sec +Time taken for epoch(SUBo): 287.07 sec +Time taken for epoch(OTHERo): 87.70 sec +<---------------------------------------|Epoch [111] END|---------------------------------------> + +Epoch: 112/486 (TSEC: 666) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00566]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 667/672 +128/128 [==============================] - 56s 394ms/step - loss: 0.1063 - accuracy: 0.9746 - val_loss: 0.3539 - val_accuracy: 0.9135 +Epoch 668/672 +128/128 [==============================] - 48s 376ms/step - loss: 0.0799 - accuracy: 0.9800 - val_loss: 0.2126 - val_accuracy: 0.9471 +Epoch 669/672 +128/128 [==============================] - 47s 368ms/step - loss: 0.0645 - accuracy: 0.9858 - val_loss: 0.3283 - val_accuracy: 0.9471 +Epoch 670/672 +128/128 [==============================] - 48s 371ms/step - loss: 0.0539 - accuracy: 0.9868 - val_loss: 0.2291 - val_accuracy: 0.9519 +Epoch 671/672 +128/128 [==============================] - 47s 369ms/step - loss: 0.0484 - accuracy: 0.9902 - val_loss: 0.2691 - val_accuracy: 0.9503 +Epoch 672/672 +128/128 [==============================] - 47s 366ms/step - loss: 0.0324 - accuracy: 0.9946 - val_loss: 0.2773 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.2773 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.29 sec +Time taken for epoch(SUBo): 294.69 sec +Time taken for epoch(OTHERo): 108.60 sec +<---------------------------------------|Epoch [112] END|---------------------------------------> + +Epoch: 113/486 (TSEC: 672) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0056]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 673/678 +128/128 [==============================] - 56s 393ms/step - loss: 0.0941 - accuracy: 0.9722 - val_loss: 0.2479 - val_accuracy: 0.9487 +Epoch 674/678 +128/128 [==============================] - 47s 363ms/step - loss: 0.0673 - accuracy: 0.9839 - val_loss: 0.3646 - val_accuracy: 0.9439 +Epoch 675/678 +128/128 [==============================] - 46s 362ms/step - loss: 0.0504 - accuracy: 0.9849 - val_loss: 0.2309 - val_accuracy: 0.9471 +Epoch 676/678 +128/128 [==============================] - 47s 366ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.2600 - val_accuracy: 0.9455 +Epoch 677/678 +128/128 [==============================] - 47s 365ms/step - loss: 0.0303 - accuracy: 0.9932 - val_loss: 0.3197 - val_accuracy: 0.9423 +Epoch 678/678 +128/128 [==============================] - 47s 364ms/step - loss: 0.0243 - accuracy: 0.9951 - val_loss: 0.3138 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3138 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 405.22 sec +Time taken for epoch(SUBo): 290.78 sec +Time taken for epoch(OTHERo): 114.43 sec +<---------------------------------------|Epoch [113] END|---------------------------------------> + +Epoch: 114/486 (TSEC: 678) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00554]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 679/684 +128/128 [==============================] - 56s 391ms/step - loss: 0.0845 - accuracy: 0.9756 - val_loss: 0.4135 - val_accuracy: 0.9279 +Epoch 680/684 +128/128 [==============================] - 48s 376ms/step - loss: 0.0718 - accuracy: 0.9761 - val_loss: 0.3313 - val_accuracy: 0.9375 +Epoch 681/684 +128/128 [==============================] - 49s 381ms/step - loss: 0.0580 - accuracy: 0.9839 - val_loss: 0.1788 - val_accuracy: 0.9647 +Epoch 682/684 +128/128 [==============================] - 47s 367ms/step - loss: 0.0432 - accuracy: 0.9912 - val_loss: 0.2599 - val_accuracy: 0.9423 +Epoch 683/684 +128/128 [==============================] - 47s 366ms/step - loss: 0.0255 - accuracy: 0.9941 - val_loss: 0.2072 - val_accuracy: 0.9615 +Epoch 684/684 +128/128 [==============================] - 47s 365ms/step - loss: 0.0233 - accuracy: 0.9956 - val_loss: 0.2130 - val_accuracy: 0.9615 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9615 +Model Test loss: 0.2130 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.12 sec +Time taken for epoch(SUBo): 294.80 sec +Time taken for epoch(OTHERo): 117.31 sec +<---------------------------------------|Epoch [114] END|---------------------------------------> + +Epoch: 115/486 (TSEC: 684) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00548]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 685/690 +128/128 [==============================] - 57s 397ms/step - loss: 0.0945 - accuracy: 0.9751 - val_loss: 0.2236 - val_accuracy: 0.9519 +Epoch 686/690 +128/128 [==============================] - 47s 363ms/step - loss: 0.0812 - accuracy: 0.9756 - val_loss: 0.4273 - val_accuracy: 0.9215 +Epoch 687/690 +128/128 [==============================] - 47s 366ms/step - loss: 0.0638 - accuracy: 0.9810 - val_loss: 0.3771 - val_accuracy: 0.9343 +Epoch 688/690 +128/128 [==============================] - 46s 361ms/step - loss: 0.0366 - accuracy: 0.9917 - val_loss: 0.3390 - val_accuracy: 0.9359 +Epoch 689/690 +128/128 [==============================] - 47s 362ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.3944 - val_accuracy: 0.9359 +Epoch 690/690 +128/128 [==============================] - 48s 371ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.4240 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.4240 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.16 sec +Time taken for epoch(SUBo): 291.71 sec +Time taken for epoch(OTHERo): 110.46 sec +<---------------------------------------|Epoch [115] END|---------------------------------------> + +Epoch: 116/486 (TSEC: 690) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00542]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 691/696 +128/128 [==============================] - 57s 397ms/step - loss: 0.1036 - accuracy: 0.9692 - val_loss: 0.3733 - val_accuracy: 0.9263 +Epoch 692/696 +128/128 [==============================] - 48s 375ms/step - loss: 0.0871 - accuracy: 0.9775 - val_loss: 0.3946 - val_accuracy: 0.9375 +Epoch 693/696 +128/128 [==============================] - 47s 368ms/step - loss: 0.0470 - accuracy: 0.9849 - val_loss: 0.3098 - val_accuracy: 0.9375 +Epoch 694/696 +128/128 [==============================] - 47s 366ms/step - loss: 0.0438 - accuracy: 0.9907 - val_loss: 0.3894 - val_accuracy: 0.9359 +Epoch 695/696 +128/128 [==============================] - 48s 371ms/step - loss: 0.0243 - accuracy: 0.9961 - val_loss: 0.3683 - val_accuracy: 0.9375 +Epoch 696/696 +128/128 [==============================] - 47s 369ms/step - loss: 0.0235 - accuracy: 0.9937 - val_loss: 0.3796 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3796 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.58 sec +Time taken for epoch(SUBo): 295.23 sec +Time taken for epoch(OTHERo): 113.35 sec +<---------------------------------------|Epoch [116] END|---------------------------------------> + +Epoch: 117/486 (TSEC: 696) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00536]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 697/702 +128/128 [==============================] - 57s 398ms/step - loss: 0.0823 - accuracy: 0.9736 - val_loss: 0.4011 - val_accuracy: 0.9375 +Epoch 698/702 +128/128 [==============================] - 47s 365ms/step - loss: 0.0490 - accuracy: 0.9873 - val_loss: 0.3466 - val_accuracy: 0.9375 +Epoch 699/702 +128/128 [==============================] - 48s 373ms/step - loss: 0.0544 - accuracy: 0.9858 - val_loss: 0.2979 - val_accuracy: 0.9487 +Epoch 700/702 +128/128 [==============================] - 48s 377ms/step - loss: 0.0407 - accuracy: 0.9907 - val_loss: 0.3367 - val_accuracy: 0.9519 +Epoch 701/702 +128/128 [==============================] - 47s 368ms/step - loss: 0.0546 - accuracy: 0.9907 - val_loss: 0.4376 - val_accuracy: 0.9295 +Epoch 702/702 +128/128 [==============================] - 48s 370ms/step - loss: 0.0275 - accuracy: 0.9956 - val_loss: 0.3449 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3449 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.03 sec +Time taken for epoch(SUBo): 295.99 sec +Time taken for epoch(OTHERo): 115.05 sec +<---------------------------------------|Epoch [117] END|---------------------------------------> + +Epoch: 118/486 (TSEC: 702) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0053]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 703/708 +128/128 [==============================] - 57s 395ms/step - loss: 0.1021 - accuracy: 0.9683 - val_loss: 0.1755 - val_accuracy: 0.9503 +Epoch 704/708 +128/128 [==============================] - 48s 376ms/step - loss: 0.1012 - accuracy: 0.9722 - val_loss: 0.1605 - val_accuracy: 0.9615 +Epoch 705/708 +128/128 [==============================] - 47s 365ms/step - loss: 0.0648 - accuracy: 0.9844 - val_loss: 0.2334 - val_accuracy: 0.9487 +Epoch 706/708 +128/128 [==============================] - 47s 368ms/step - loss: 0.0439 - accuracy: 0.9897 - val_loss: 0.2403 - val_accuracy: 0.9503 +Epoch 707/708 +128/128 [==============================] - 47s 369ms/step - loss: 0.0369 - accuracy: 0.9917 - val_loss: 0.2302 - val_accuracy: 0.9519 +Epoch 708/708 +128/128 [==============================] - 48s 377ms/step - loss: 0.0319 - accuracy: 0.9922 - val_loss: 0.2279 - val_accuracy: 0.9503 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9503 +Model Test loss: 0.2279 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.63 sec +Time taken for epoch(SUBo): 296.34 sec +Time taken for epoch(OTHERo): 117.29 sec +<---------------------------------------|Epoch [118] END|---------------------------------------> + +Epoch: 119/486 (TSEC: 708) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00524]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 709/714 +128/128 [==============================] - 56s 391ms/step - loss: 0.0966 - accuracy: 0.9741 - val_loss: 0.2344 - val_accuracy: 0.9455 +Epoch 710/714 +128/128 [==============================] - 48s 370ms/step - loss: 0.0834 - accuracy: 0.9766 - val_loss: 0.4004 - val_accuracy: 0.9295 +Epoch 711/714 +128/128 [==============================] - 47s 367ms/step - loss: 0.0532 - accuracy: 0.9888 - val_loss: 0.2622 - val_accuracy: 0.9439 +Epoch 712/714 +128/128 [==============================] - 48s 374ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.2558 - val_accuracy: 0.9471 +Epoch 713/714 +128/128 [==============================] - 47s 370ms/step - loss: 0.0331 - accuracy: 0.9941 - val_loss: 0.3737 - val_accuracy: 0.9375 +Epoch 714/714 +128/128 [==============================] - 47s 369ms/step - loss: 0.0253 - accuracy: 0.9941 - val_loss: 0.3194 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3194 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.60 sec +Time taken for epoch(SUBo): 294.03 sec +Time taken for epoch(OTHERo): 114.57 sec +<---------------------------------------|Epoch [119] END|---------------------------------------> + +Epoch: 120/486 (TSEC: 714) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00518]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 715/720 +128/128 [==============================] - 56s 391ms/step - loss: 0.0911 - accuracy: 0.9771 - val_loss: 0.3415 - val_accuracy: 0.9327 +Epoch 716/720 +128/128 [==============================] - 49s 379ms/step - loss: 0.0827 - accuracy: 0.9775 - val_loss: 0.3602 - val_accuracy: 0.9423 +Epoch 717/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0548 - accuracy: 0.9873 - val_loss: 0.3977 - val_accuracy: 0.9391 +Epoch 718/720 +128/128 [==============================] - 49s 383ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.3429 - val_accuracy: 0.9439 +Epoch 719/720 +128/128 [==============================] - 47s 367ms/step - loss: 0.0286 - accuracy: 0.9941 - val_loss: 0.4900 - val_accuracy: 0.9343 +Epoch 720/720 +128/128 [==============================] - 47s 366ms/step - loss: 0.0246 - accuracy: 0.9976 - val_loss: 0.5142 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.5143 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 408.26 sec +Time taken for epoch(SUBo): 295.66 sec +Time taken for epoch(OTHERo): 112.60 sec +<---------------------------------------|Epoch [120] END|---------------------------------------> + +Epoch: 121/486 (TSEC: 720) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00512]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 721/726 +128/128 [==============================] - 56s 393ms/step - loss: 0.1019 - accuracy: 0.9746 - val_loss: 0.3720 - val_accuracy: 0.9391 +Epoch 722/726 +128/128 [==============================] - 47s 369ms/step - loss: 0.0798 - accuracy: 0.9790 - val_loss: 0.3212 - val_accuracy: 0.9359 +Epoch 723/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0722 - accuracy: 0.9829 - val_loss: 0.4118 - val_accuracy: 0.9199 +Epoch 724/726 +128/128 [==============================] - 49s 378ms/step - loss: 0.0358 - accuracy: 0.9941 - val_loss: 0.3097 - val_accuracy: 0.9407 +Epoch 725/726 +128/128 [==============================] - 47s 368ms/step - loss: 0.0383 - accuracy: 0.9941 - val_loss: 0.3610 - val_accuracy: 0.9311 +Epoch 726/726 +128/128 [==============================] - 48s 370ms/step - loss: 0.0263 - accuracy: 0.9956 - val_loss: 0.4176 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9231 +Model Test loss: 0.4177 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.06 sec +Time taken for epoch(SUBo): 295.42 sec +Time taken for epoch(OTHERo): 118.64 sec +<---------------------------------------|Epoch [121] END|---------------------------------------> + +Epoch: 122/486 (TSEC: 726) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00506]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 727/732 +128/128 [==============================] - 56s 394ms/step - loss: 0.0832 - accuracy: 0.9761 - val_loss: 0.2602 - val_accuracy: 0.9359 +Epoch 728/732 +128/128 [==============================] - 48s 372ms/step - loss: 0.0566 - accuracy: 0.9854 - val_loss: 0.4209 - val_accuracy: 0.9295 +Epoch 729/732 +128/128 [==============================] - 48s 371ms/step - loss: 0.0450 - accuracy: 0.9863 - val_loss: 0.3616 - val_accuracy: 0.9327 +Epoch 730/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0411 - accuracy: 0.9917 - val_loss: 0.4043 - val_accuracy: 0.9311 +Epoch 731/732 +128/128 [==============================] - 47s 365ms/step - loss: 0.0323 - accuracy: 0.9937 - val_loss: 0.4829 - val_accuracy: 0.9279 +Epoch 732/732 +128/128 [==============================] - 47s 368ms/step - loss: 0.0219 - accuracy: 0.9946 - val_loss: 0.4436 - val_accuracy: 0.9327 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9327 +Model Test loss: 0.4436 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.37 sec +Time taken for epoch(SUBo): 293.85 sec +Time taken for epoch(OTHERo): 117.52 sec +<---------------------------------------|Epoch [122] END|---------------------------------------> + +Epoch: 123/486 (TSEC: 732) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.005]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 733/738 +128/128 [==============================] - 57s 401ms/step - loss: 0.0974 - accuracy: 0.9727 - val_loss: 0.3062 - val_accuracy: 0.9455 +Epoch 734/738 +128/128 [==============================] - 48s 373ms/step - loss: 0.0968 - accuracy: 0.9751 - val_loss: 0.2282 - val_accuracy: 0.9343 +Epoch 735/738 +128/128 [==============================] - 47s 369ms/step - loss: 0.0650 - accuracy: 0.9854 - val_loss: 0.3177 - val_accuracy: 0.9407 +Epoch 736/738 +128/128 [==============================] - 47s 363ms/step - loss: 0.0531 - accuracy: 0.9878 - val_loss: 0.3416 - val_accuracy: 0.9407 +Epoch 737/738 +128/128 [==============================] - 48s 371ms/step - loss: 0.0395 - accuracy: 0.9907 - val_loss: 0.4159 - val_accuracy: 0.9279 +Epoch 738/738 +128/128 [==============================] - 47s 365ms/step - loss: 0.0327 - accuracy: 0.9927 - val_loss: 0.4303 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4303 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 412.96 sec +Time taken for epoch(SUBo): 294.39 sec +Time taken for epoch(OTHERo): 118.57 sec +<---------------------------------------|Epoch [123] END|---------------------------------------> + +Epoch: 124/486 (TSEC: 738) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00494]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 739/744 +128/128 [==============================] - 57s 399ms/step - loss: 0.0994 - accuracy: 0.9707 - val_loss: 0.4480 - val_accuracy: 0.9231 +Epoch 740/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0825 - accuracy: 0.9746 - val_loss: 0.7219 - val_accuracy: 0.8974 +Epoch 741/744 +128/128 [==============================] - 48s 378ms/step - loss: 0.0606 - accuracy: 0.9854 - val_loss: 0.4926 - val_accuracy: 0.9327 +Epoch 742/744 +128/128 [==============================] - 48s 376ms/step - loss: 0.0377 - accuracy: 0.9917 - val_loss: 0.3512 - val_accuracy: 0.9439 +Epoch 743/744 +128/128 [==============================] - 48s 372ms/step - loss: 0.0278 - accuracy: 0.9946 - val_loss: 0.4617 - val_accuracy: 0.9327 +Epoch 744/744 +128/128 [==============================] - 48s 373ms/step - loss: 0.0331 - accuracy: 0.9946 - val_loss: 0.4234 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4234 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.23 sec +Time taken for epoch(SUBo): 298.41 sec +Time taken for epoch(OTHERo): 114.83 sec +<---------------------------------------|Epoch [124] END|---------------------------------------> + +Epoch: 125/486 (TSEC: 744) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00488]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 745/750 +128/128 [==============================] - 57s 398ms/step - loss: 0.0909 - accuracy: 0.9727 - val_loss: 0.2446 - val_accuracy: 0.9455 +Epoch 746/750 +128/128 [==============================] - 47s 368ms/step - loss: 0.0559 - accuracy: 0.9844 - val_loss: 0.3933 - val_accuracy: 0.9327 +Epoch 747/750 +128/128 [==============================] - 47s 364ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.2643 - val_accuracy: 0.9439 +Epoch 748/750 +128/128 [==============================] - 48s 374ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 0.3470 - val_accuracy: 0.9359 +Epoch 749/750 +128/128 [==============================] - 46s 362ms/step - loss: 0.0195 - accuracy: 0.9966 - val_loss: 0.4570 - val_accuracy: 0.9343 +Epoch 750/750 +128/128 [==============================] - 47s 369ms/step - loss: 0.0383 - accuracy: 0.9922 - val_loss: 0.3677 - val_accuracy: 0.9423 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9423 +Model Test loss: 0.3677 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.29 sec +Time taken for epoch(SUBo): 293.29 sec +Time taken for epoch(OTHERo): 119.99 sec +<---------------------------------------|Epoch [125] END|---------------------------------------> + +Epoch: 126/486 (TSEC: 750) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00482]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 751/756 +128/128 [==============================] - 56s 393ms/step - loss: 0.0741 - accuracy: 0.9800 - val_loss: 0.2877 - val_accuracy: 0.9375 +Epoch 752/756 +128/128 [==============================] - 48s 373ms/step - loss: 0.0630 - accuracy: 0.9819 - val_loss: 0.3119 - val_accuracy: 0.9455 +Epoch 753/756 +128/128 [==============================] - 47s 367ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.3229 - val_accuracy: 0.9359 +Epoch 754/756 +128/128 [==============================] - 47s 364ms/step - loss: 0.0393 - accuracy: 0.9888 - val_loss: 0.3004 - val_accuracy: 0.9391 +Epoch 755/756 +128/128 [==============================] - 47s 369ms/step - loss: 0.0258 - accuracy: 0.9956 - val_loss: 0.3147 - val_accuracy: 0.9423 +Epoch 756/756 +128/128 [==============================] - 47s 370ms/step - loss: 0.0414 - accuracy: 0.9922 - val_loss: 0.3409 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3409 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.45 sec +Time taken for epoch(SUBo): 293.26 sec +Time taken for epoch(OTHERo): 110.19 sec +<---------------------------------------|Epoch [126] END|---------------------------------------> + +Epoch: 127/486 (TSEC: 756) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00476]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 757/762 +128/128 [==============================] - 55s 388ms/step - loss: 0.0936 - accuracy: 0.9722 - val_loss: 0.2701 - val_accuracy: 0.9375 +Epoch 758/762 +128/128 [==============================] - 48s 377ms/step - loss: 0.0766 - accuracy: 0.9800 - val_loss: 0.1688 - val_accuracy: 0.9599 +Epoch 759/762 +128/128 [==============================] - 47s 364ms/step - loss: 0.0538 - accuracy: 0.9878 - val_loss: 0.2163 - val_accuracy: 0.9391 +Epoch 760/762 +128/128 [==============================] - 47s 368ms/step - loss: 0.0424 - accuracy: 0.9902 - val_loss: 0.3268 - val_accuracy: 0.9391 +Epoch 761/762 +128/128 [==============================] - 47s 367ms/step - loss: 0.0391 - accuracy: 0.9922 - val_loss: 0.3866 - val_accuracy: 0.9359 +Epoch 762/762 +128/128 [==============================] - 47s 363ms/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.3632 - val_accuracy: 0.9359 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9359 +Model Test loss: 0.3632 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 403.89 sec +Time taken for epoch(SUBo): 291.93 sec +Time taken for epoch(OTHERo): 111.96 sec +<---------------------------------------|Epoch [127] END|---------------------------------------> + +Epoch: 128/486 (TSEC: 762) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +└───Shuffling data... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +- Debug DP Sample dir: Samples/TSR_SUB_400_y2023_m12_d26-h17_m57_s00 +Setting training OneCycleLr::maxlr to [0.0047]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 763/768 +128/128 [==============================] - 56s 392ms/step - loss: 0.0821 - accuracy: 0.9780 - val_loss: 0.2490 - val_accuracy: 0.9423 +Epoch 764/768 +128/128 [==============================] - 47s 363ms/step - loss: 0.0554 - accuracy: 0.9883 - val_loss: 0.3137 - val_accuracy: 0.9343 +Epoch 765/768 +128/128 [==============================] - 48s 370ms/step - loss: 0.0518 - accuracy: 0.9849 - val_loss: 0.2723 - val_accuracy: 0.9375 +Epoch 766/768 +128/128 [==============================] - 48s 375ms/step - loss: 0.0469 - accuracy: 0.9902 - val_loss: 0.2368 - val_accuracy: 0.9503 +Epoch 767/768 +128/128 [==============================] - 45s 352ms/step - loss: 0.0232 - accuracy: 0.9971 - val_loss: 0.2619 - val_accuracy: 0.9391 +Epoch 768/768 +128/128 [==============================] - 47s 364ms/step - loss: 0.0239 - accuracy: 0.9946 - val_loss: 0.3065 - val_accuracy: 0.9343 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9343 +Model Test loss: 0.3065 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 425.95 sec +Time taken for epoch(SUBo): 291.59 sec +Time taken for epoch(OTHERo): 134.36 sec +<---------------------------------------|Epoch [128] END|---------------------------------------> + +Epoch: 129/486 (TSEC: 768) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00464]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 769/774 +128/128 [==============================] - 54s 383ms/step - loss: 0.0953 - accuracy: 0.9746 - val_loss: 0.2683 - val_accuracy: 0.9343 +Epoch 770/774 +128/128 [==============================] - 48s 379ms/step - loss: 0.0731 - accuracy: 0.9800 - val_loss: 0.2576 - val_accuracy: 0.9439 +Epoch 771/774 +128/128 [==============================] - 43s 337ms/step - loss: 0.0510 - accuracy: 0.9863 - val_loss: 0.2335 - val_accuracy: 0.9487 +Epoch 772/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0347 - accuracy: 0.9932 - val_loss: 0.2515 - val_accuracy: 0.9503 +Epoch 773/774 +128/128 [==============================] - 49s 381ms/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 0.2658 - val_accuracy: 0.9519 +Epoch 774/774 +128/128 [==============================] - 48s 377ms/step - loss: 0.0371 - accuracy: 0.9932 - val_loss: 0.2221 - val_accuracy: 0.9599 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9599 +Model Test loss: 0.2221 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.23 sec +Time taken for epoch(SUBo): 293.03 sec +Time taken for epoch(OTHERo): 109.20 sec +<---------------------------------------|Epoch [129] END|---------------------------------------> + +Epoch: 130/486 (TSEC: 774) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00458]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 775/780 +128/128 [==============================] - 57s 397ms/step - loss: 0.0820 - accuracy: 0.9751 - val_loss: 0.1833 - val_accuracy: 0.9487 +Epoch 776/780 +128/128 [==============================] - 49s 379ms/step - loss: 0.0594 - accuracy: 0.9858 - val_loss: 0.2153 - val_accuracy: 0.9535 +Epoch 777/780 +128/128 [==============================] - 47s 365ms/step - loss: 0.0447 - accuracy: 0.9888 - val_loss: 0.3316 - val_accuracy: 0.9327 +Epoch 778/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0428 - accuracy: 0.9897 - val_loss: 0.3064 - val_accuracy: 0.9455 +Epoch 779/780 +128/128 [==============================] - 47s 364ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 0.3133 - val_accuracy: 0.9423 +Epoch 780/780 +128/128 [==============================] - 47s 369ms/step - loss: 0.0244 - accuracy: 0.9941 - val_loss: 0.3314 - val_accuracy: 0.9439 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9439 +Model Test loss: 0.3315 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 402.71 sec +Time taken for epoch(SUBo): 293.90 sec +Time taken for epoch(OTHERo): 108.81 sec +<---------------------------------------|Epoch [130] END|---------------------------------------> + +Epoch: 131/486 (TSEC: 780) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00452]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 781/786 +128/128 [==============================] - 59s 407ms/step - loss: 0.0771 - accuracy: 0.9785 - val_loss: 0.3851 - val_accuracy: 0.9279 +Epoch 782/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0645 - accuracy: 0.9805 - val_loss: 0.4293 - val_accuracy: 0.9247 +Epoch 783/786 +128/128 [==============================] - 49s 380ms/step - loss: 0.0452 - accuracy: 0.9854 - val_loss: 0.3073 - val_accuracy: 0.9391 +Epoch 784/786 +128/128 [==============================] - 48s 373ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.4917 - val_accuracy: 0.9359 +Epoch 785/786 +128/128 [==============================] - 49s 379ms/step - loss: 0.0430 - accuracy: 0.9893 - val_loss: 0.5807 - val_accuracy: 0.9231 +Epoch 786/786 +128/128 [==============================] - 48s 371ms/step - loss: 0.0315 - accuracy: 0.9937 - val_loss: 0.5020 - val_accuracy: 0.9263 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9263 +Model Test loss: 0.5019 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 424.42 sec +Time taken for epoch(SUBo): 300.59 sec +Time taken for epoch(OTHERo): 123.83 sec +<---------------------------------------|Epoch [131] END|---------------------------------------> + +Epoch: 132/486 (TSEC: 786) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00446]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 787/792 +128/128 [==============================] - 57s 395ms/step - loss: 0.0796 - accuracy: 0.9771 - val_loss: 0.5783 - val_accuracy: 0.9247 +Epoch 788/792 +128/128 [==============================] - 49s 382ms/step - loss: 0.0667 - accuracy: 0.9805 - val_loss: 0.4861 - val_accuracy: 0.9263 +Epoch 789/792 +128/128 [==============================] - 49s 378ms/step - loss: 0.0621 - accuracy: 0.9819 - val_loss: 0.7508 - val_accuracy: 0.8990 +Epoch 790/792 +128/128 [==============================] - 48s 373ms/step - loss: 0.0435 - accuracy: 0.9873 - val_loss: 0.4205 - val_accuracy: 0.9215 +Epoch 791/792 +128/128 [==============================] - 48s 374ms/step - loss: 0.0335 - accuracy: 0.9941 - val_loss: 0.4631 - val_accuracy: 0.9231 +Epoch 792/792 +128/128 [==============================] - 48s 377ms/step - loss: 0.0225 - accuracy: 0.9956 - val_loss: 0.5336 - val_accuracy: 0.9215 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9215 +Model Test loss: 0.5337 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.90 sec +Time taken for epoch(SUBo): 299.61 sec +Time taken for epoch(OTHERo): 121.28 sec +<---------------------------------------|Epoch [132] END|---------------------------------------> + +Epoch: 133/486 (TSEC: 792) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0044]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 793/798 +128/128 [==============================] - 56s 388ms/step - loss: 0.0802 - accuracy: 0.9746 - val_loss: 0.5169 - val_accuracy: 0.9231 +Epoch 794/798 +128/128 [==============================] - 48s 377ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.3563 - val_accuracy: 0.9375 +Epoch 795/798 +128/128 [==============================] - 49s 384ms/step - loss: 0.0468 - accuracy: 0.9858 - val_loss: 0.3155 - val_accuracy: 0.9487 +Epoch 796/798 +128/128 [==============================] - 47s 365ms/step - loss: 0.0313 - accuracy: 0.9927 - val_loss: 0.4853 - val_accuracy: 0.9311 +Epoch 797/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0304 - accuracy: 0.9917 - val_loss: 0.4469 - val_accuracy: 0.9311 +Epoch 798/798 +128/128 [==============================] - 48s 374ms/step - loss: 0.0231 - accuracy: 0.9946 - val_loss: 0.5005 - val_accuracy: 0.9311 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9311 +Model Test loss: 0.5005 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.52 sec +Time taken for epoch(SUBo): 296.92 sec +Time taken for epoch(OTHERo): 120.59 sec +<---------------------------------------|Epoch [133] END|---------------------------------------> + +Epoch: 134/486 (TSEC: 798) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00434]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 799/804 +128/128 [==============================] - 57s 396ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.5825 - val_accuracy: 0.9151 +Epoch 800/804 +128/128 [==============================] - 48s 375ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.5426 - val_accuracy: 0.9071 +Epoch 801/804 +128/128 [==============================] - 50s 389ms/step - loss: 0.0392 - accuracy: 0.9888 - val_loss: 0.4001 - val_accuracy: 0.9295 +Epoch 802/804 +128/128 [==============================] - 48s 372ms/step - loss: 0.0282 - accuracy: 0.9902 - val_loss: 0.6380 - val_accuracy: 0.9231 +Epoch 803/804 +128/128 [==============================] - 47s 368ms/step - loss: 0.0266 - accuracy: 0.9951 - val_loss: 0.5224 - val_accuracy: 0.9151 +Epoch 804/804 +128/128 [==============================] - 47s 369ms/step - loss: 0.0168 - accuracy: 0.9966 - val_loss: 0.5460 - val_accuracy: 0.9151 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9151 +Model Test loss: 0.5460 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.80 sec +Time taken for epoch(SUBo): 297.98 sec +Time taken for epoch(OTHERo): 122.82 sec +<---------------------------------------|Epoch [134] END|---------------------------------------> + +Epoch: 135/486 (TSEC: 804) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00428]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 805/810 +128/128 [==============================] - 57s 396ms/step - loss: 0.0857 - accuracy: 0.9746 - val_loss: 0.6123 - val_accuracy: 0.9103 +Epoch 806/810 +128/128 [==============================] - 49s 380ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.4536 - val_accuracy: 0.9167 +Epoch 807/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0642 - accuracy: 0.9858 - val_loss: 0.6232 - val_accuracy: 0.9087 +Epoch 808/810 +128/128 [==============================] - 48s 374ms/step - loss: 0.0377 - accuracy: 0.9912 - val_loss: 0.5339 - val_accuracy: 0.9103 +Epoch 809/810 +128/128 [==============================] - 47s 370ms/step - loss: 0.0241 - accuracy: 0.9951 - val_loss: 0.5463 - val_accuracy: 0.9103 +Epoch 810/810 +128/128 [==============================] - 48s 370ms/step - loss: 0.0257 - accuracy: 0.9946 - val_loss: 0.5751 - val_accuracy: 0.9103 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9103 +Model Test loss: 0.5751 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 414.70 sec +Time taken for epoch(SUBo): 297.58 sec +Time taken for epoch(OTHERo): 117.13 sec +<---------------------------------------|Epoch [135] END|---------------------------------------> + +Epoch: 136/486 (TSEC: 810) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00422]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 811/816 +128/128 [==============================] - 57s 401ms/step - loss: 0.0885 - accuracy: 0.9761 - val_loss: 0.4876 - val_accuracy: 0.9327 +Epoch 812/816 +128/128 [==============================] - 50s 388ms/step - loss: 0.0674 - accuracy: 0.9819 - val_loss: 0.5588 - val_accuracy: 0.9359 +Epoch 813/816 +128/128 [==============================] - 48s 374ms/step - loss: 0.0593 - accuracy: 0.9824 - val_loss: 0.4268 - val_accuracy: 0.9375 +Epoch 814/816 +128/128 [==============================] - 49s 382ms/step - loss: 0.0509 - accuracy: 0.9907 - val_loss: 0.2625 - val_accuracy: 0.9423 +Epoch 815/816 +128/128 [==============================] - 47s 369ms/step - loss: 0.0282 - accuracy: 0.9932 - val_loss: 0.3490 - val_accuracy: 0.9407 +Epoch 816/816 +128/128 [==============================] - 48s 371ms/step - loss: 0.0244 - accuracy: 0.9961 - val_loss: 0.3819 - val_accuracy: 0.9375 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9375 +Model Test loss: 0.3819 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.58 sec +Time taken for epoch(SUBo): 300.30 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [136] END|---------------------------------------> + +Epoch: 137/486 (TSEC: 816) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00416]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 817/822 +128/128 [==============================] - 56s 393ms/step - loss: 0.0697 - accuracy: 0.9780 - val_loss: 0.3293 - val_accuracy: 0.9375 +Epoch 818/822 +128/128 [==============================] - 47s 367ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.6277 - val_accuracy: 0.9295 +Epoch 819/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0356 - accuracy: 0.9902 - val_loss: 0.4455 - val_accuracy: 0.9375 +Epoch 820/822 +128/128 [==============================] - 48s 376ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 0.4327 - val_accuracy: 0.9391 +Epoch 821/822 +128/128 [==============================] - 49s 381ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.4351 - val_accuracy: 0.9407 +Epoch 822/822 +128/128 [==============================] - 48s 372ms/step - loss: 0.0177 - accuracy: 0.9941 - val_loss: 0.4433 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.4434 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.54 sec +Time taken for epoch(SUBo): 297.62 sec +Time taken for epoch(OTHERo): 118.92 sec +<---------------------------------------|Epoch [137] END|---------------------------------------> + +Epoch: 138/486 (TSEC: 822) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0041]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 823/828 +128/128 [==============================] - 56s 396ms/step - loss: 0.0897 - accuracy: 0.9771 - val_loss: 0.3267 - val_accuracy: 0.9359 +Epoch 824/828 +128/128 [==============================] - 48s 371ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.4046 - val_accuracy: 0.9263 +Epoch 825/828 +128/128 [==============================] - 49s 380ms/step - loss: 0.0522 - accuracy: 0.9844 - val_loss: 0.3246 - val_accuracy: 0.9407 +Epoch 826/828 +128/128 [==============================] - 48s 374ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.4802 - val_accuracy: 0.9167 +Epoch 827/828 +128/128 [==============================] - 48s 376ms/step - loss: 0.0273 - accuracy: 0.9937 - val_loss: 0.4348 - val_accuracy: 0.9295 +Epoch 828/828 +128/128 [==============================] - 48s 373ms/step - loss: 0.0193 - accuracy: 0.9961 - val_loss: 0.4551 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.4551 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.46 sec +Time taken for epoch(SUBo): 297.55 sec +Time taken for epoch(OTHERo): 117.91 sec +<---------------------------------------|Epoch [138] END|---------------------------------------> + +Epoch: 139/486 (TSEC: 828) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00404]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 829/834 +128/128 [==============================] - 57s 398ms/step - loss: 0.0977 - accuracy: 0.9766 - val_loss: 0.4017 - val_accuracy: 0.9263 +Epoch 830/834 +128/128 [==============================] - 50s 387ms/step - loss: 0.0733 - accuracy: 0.9800 - val_loss: 0.3346 - val_accuracy: 0.9375 +Epoch 831/834 +128/128 [==============================] - 47s 365ms/step - loss: 0.0504 - accuracy: 0.9863 - val_loss: 0.4922 - val_accuracy: 0.9231 +Epoch 832/834 +128/128 [==============================] - 47s 366ms/step - loss: 0.0298 - accuracy: 0.9937 - val_loss: 0.4437 - val_accuracy: 0.9375 +Epoch 833/834 +128/128 [==============================] - 47s 364ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.4766 - val_accuracy: 0.9359 +Epoch 834/834 +128/128 [==============================] - 48s 374ms/step - loss: 0.0414 - accuracy: 0.9937 - val_loss: 0.5236 - val_accuracy: 0.9295 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9295 +Model Test loss: 0.5237 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 418.66 sec +Time taken for epoch(SUBo): 295.90 sec +Time taken for epoch(OTHERo): 122.76 sec +<---------------------------------------|Epoch [139] END|---------------------------------------> + +Epoch: 140/486 (TSEC: 834) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00398]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 835/840 +128/128 [==============================] - 58s 407ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.4351 - val_accuracy: 0.9375 +Epoch 836/840 +128/128 [==============================] - 48s 375ms/step - loss: 0.0682 - accuracy: 0.9790 - val_loss: 0.6343 - val_accuracy: 0.9151 +Epoch 837/840 +128/128 [==============================] - 49s 377ms/step - loss: 0.0516 - accuracy: 0.9873 - val_loss: 0.4780 - val_accuracy: 0.9183 +Epoch 838/840 +128/128 [==============================] - 47s 367ms/step - loss: 0.0423 - accuracy: 0.9897 - val_loss: 0.4968 - val_accuracy: 0.9247 +Epoch 839/840 +128/128 [==============================] - 47s 364ms/step - loss: 0.0273 - accuracy: 0.9927 - val_loss: 0.5763 - val_accuracy: 0.9199 +Epoch 840/840 +128/128 [==============================] - 48s 378ms/step - loss: 0.0457 - accuracy: 0.9888 - val_loss: 0.5711 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5710 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 420.43 sec +Time taken for epoch(SUBo): 298.12 sec +Time taken for epoch(OTHERo): 122.31 sec +<---------------------------------------|Epoch [140] END|---------------------------------------> + +Epoch: 141/486 (TSEC: 840) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00392]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 841/846 +128/128 [==============================] - 57s 398ms/step - loss: 0.0625 - accuracy: 0.9824 - val_loss: 0.5867 - val_accuracy: 0.9183 +Epoch 842/846 +128/128 [==============================] - 49s 383ms/step - loss: 0.0476 - accuracy: 0.9893 - val_loss: 0.5093 - val_accuracy: 0.9231 +Epoch 843/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0368 - accuracy: 0.9912 - val_loss: 0.5003 - val_accuracy: 0.9231 +Epoch 844/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0285 - accuracy: 0.9941 - val_loss: 0.5661 - val_accuracy: 0.9231 +Epoch 845/846 +128/128 [==============================] - 48s 370ms/step - loss: 0.0194 - accuracy: 0.9941 - val_loss: 0.6070 - val_accuracy: 0.9199 +Epoch 846/846 +128/128 [==============================] - 49s 378ms/step - loss: 0.0181 - accuracy: 0.9976 - val_loss: 0.5128 - val_accuracy: 0.9247 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9247 +Model Test loss: 0.5128 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 423.15 sec +Time taken for epoch(SUBo): 298.17 sec +Time taken for epoch(OTHERo): 124.98 sec +<---------------------------------------|Epoch [141] END|---------------------------------------> + +Epoch: 142/486 (TSEC: 846) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00386]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 847/852 +128/128 [==============================] - 56s 394ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.6443 - val_accuracy: 0.9215 +Epoch 848/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0741 - accuracy: 0.9790 - val_loss: 0.5882 - val_accuracy: 0.9247 +Epoch 849/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0500 - accuracy: 0.9849 - val_loss: 0.3507 - val_accuracy: 0.9359 +Epoch 850/852 +128/128 [==============================] - 49s 384ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.4941 - val_accuracy: 0.9311 +Epoch 851/852 +128/128 [==============================] - 48s 375ms/step - loss: 0.0462 - accuracy: 0.9907 - val_loss: 0.4965 - val_accuracy: 0.9295 +Epoch 852/852 +128/128 [==============================] - 48s 377ms/step - loss: 0.0282 - accuracy: 0.9951 - val_loss: 0.5102 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.5103 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.49 sec +Time taken for epoch(SUBo): 301.87 sec +Time taken for epoch(OTHERo): 114.61 sec +<---------------------------------------|Epoch [142] END|---------------------------------------> + +Epoch: 143/486 (TSEC: 852) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0038]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 853/858 +128/128 [==============================] - 57s 402ms/step - loss: 0.0791 - accuracy: 0.9771 - val_loss: 0.4857 - val_accuracy: 0.9135 +Epoch 854/858 +128/128 [==============================] - 49s 379ms/step - loss: 0.0536 - accuracy: 0.9849 - val_loss: 0.3757 - val_accuracy: 0.9263 +Epoch 855/858 +128/128 [==============================] - 47s 367ms/step - loss: 0.0389 - accuracy: 0.9878 - val_loss: 0.6769 - val_accuracy: 0.9151 +Epoch 856/858 +128/128 [==============================] - 47s 369ms/step - loss: 0.0402 - accuracy: 0.9888 - val_loss: 0.6208 - val_accuracy: 0.9183 +Epoch 857/858 +128/128 [==============================] - 48s 371ms/step - loss: 0.0406 - accuracy: 0.9922 - val_loss: 0.8169 - val_accuracy: 0.9038 +Epoch 858/858 +128/128 [==============================] - 47s 363ms/step - loss: 0.0237 - accuracy: 0.9937 - val_loss: 0.7814 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.7814 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.74 sec +Time taken for epoch(SUBo): 295.81 sec +Time taken for epoch(OTHERo): 113.94 sec +<---------------------------------------|Epoch [143] END|---------------------------------------> + +Epoch: 144/486 (TSEC: 858) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00374]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 859/864 +128/128 [==============================] - 56s 395ms/step - loss: 0.0950 - accuracy: 0.9751 - val_loss: 0.3909 - val_accuracy: 0.9359 +Epoch 860/864 +128/128 [==============================] - 49s 380ms/step - loss: 0.0660 - accuracy: 0.9819 - val_loss: 0.3311 - val_accuracy: 0.9391 +Epoch 861/864 +128/128 [==============================] - 47s 368ms/step - loss: 0.0500 - accuracy: 0.9863 - val_loss: 0.5487 - val_accuracy: 0.9343 +Epoch 862/864 +128/128 [==============================] - 48s 377ms/step - loss: 0.0394 - accuracy: 0.9912 - val_loss: 0.3179 - val_accuracy: 0.9423 +Epoch 863/864 +128/128 [==============================] - 47s 364ms/step - loss: 0.0271 - accuracy: 0.9937 - val_loss: 0.3828 - val_accuracy: 0.9391 +Epoch 864/864 +128/128 [==============================] - 47s 366ms/step - loss: 0.0312 - accuracy: 0.9937 - val_loss: 0.3838 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3838 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.79 sec +Time taken for epoch(SUBo): 295.17 sec +Time taken for epoch(OTHERo): 118.61 sec +<---------------------------------------|Epoch [144] END|---------------------------------------> + +Epoch: 145/486 (TSEC: 864) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00368]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 865/870 +128/128 [==============================] - 56s 394ms/step - loss: 0.0786 - accuracy: 0.9741 - val_loss: 0.3169 - val_accuracy: 0.9439 +Epoch 866/870 +128/128 [==============================] - 49s 378ms/step - loss: 0.0708 - accuracy: 0.9771 - val_loss: 0.1666 - val_accuracy: 0.9487 +Epoch 867/870 +128/128 [==============================] - 48s 371ms/step - loss: 0.0560 - accuracy: 0.9839 - val_loss: 0.3721 - val_accuracy: 0.9359 +Epoch 868/870 +128/128 [==============================] - 47s 369ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 0.3189 - val_accuracy: 0.9439 +Epoch 869/870 +128/128 [==============================] - 48s 373ms/step - loss: 0.0253 - accuracy: 0.9946 - val_loss: 0.3500 - val_accuracy: 0.9439 +Epoch 870/870 +128/128 [==============================] - 47s 366ms/step - loss: 0.0239 - accuracy: 0.9966 - val_loss: 0.3788 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3789 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 413.68 sec +Time taken for epoch(SUBo): 295.62 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [145] END|---------------------------------------> + +Epoch: 146/486 (TSEC: 870) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00362]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 871/876 +128/128 [==============================] - 57s 397ms/step - loss: 0.0636 - accuracy: 0.9780 - val_loss: 0.5716 - val_accuracy: 0.9103 +Epoch 872/876 +128/128 [==============================] - 49s 384ms/step - loss: 0.0695 - accuracy: 0.9751 - val_loss: 0.6019 - val_accuracy: 0.9135 +Epoch 873/876 +128/128 [==============================] - 48s 376ms/step - loss: 0.0519 - accuracy: 0.9863 - val_loss: 0.4120 - val_accuracy: 0.9279 +Epoch 874/876 +128/128 [==============================] - 47s 369ms/step - loss: 0.0409 - accuracy: 0.9912 - val_loss: 0.5322 - val_accuracy: 0.9022 +Epoch 875/876 +128/128 [==============================] - 47s 368ms/step - loss: 0.0261 - accuracy: 0.9951 - val_loss: 0.5225 - val_accuracy: 0.9103 +Epoch 876/876 +128/128 [==============================] - 49s 379ms/step - loss: 0.0162 - accuracy: 0.9971 - val_loss: 0.5834 - val_accuracy: 0.9071 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9071 +Model Test loss: 0.5834 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.30 sec +Time taken for epoch(SUBo): 298.45 sec +Time taken for epoch(OTHERo): 116.86 sec +<---------------------------------------|Epoch [146] END|---------------------------------------> + +Epoch: 147/486 (TSEC: 876) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00356]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 877/882 +128/128 [==============================] - 57s 397ms/step - loss: 0.0758 - accuracy: 0.9785 - val_loss: 0.4339 - val_accuracy: 0.9215 +Epoch 878/882 +128/128 [==============================] - 49s 380ms/step - loss: 0.0705 - accuracy: 0.9800 - val_loss: 0.2700 - val_accuracy: 0.9439 +Epoch 879/882 +128/128 [==============================] - 49s 383ms/step - loss: 0.0507 - accuracy: 0.9878 - val_loss: 0.3516 - val_accuracy: 0.9455 +Epoch 880/882 +128/128 [==============================] - 47s 368ms/step - loss: 0.0384 - accuracy: 0.9907 - val_loss: 0.4651 - val_accuracy: 0.9231 +Epoch 881/882 +128/128 [==============================] - 47s 365ms/step - loss: 0.0262 - accuracy: 0.9941 - val_loss: 0.3920 - val_accuracy: 0.9279 +Epoch 882/882 +128/128 [==============================] - 48s 370ms/step - loss: 0.0289 - accuracy: 0.9937 - val_loss: 0.3896 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.3896 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 417.42 sec +Time taken for epoch(SUBo): 297.44 sec +Time taken for epoch(OTHERo): 119.98 sec +<---------------------------------------|Epoch [147] END|---------------------------------------> + +Epoch: 148/486 (TSEC: 882) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.0035]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 883/888 +128/128 [==============================] - 55s 386ms/step - loss: 0.0721 - accuracy: 0.9790 - val_loss: 0.4513 - val_accuracy: 0.9167 +Epoch 884/888 +128/128 [==============================] - 48s 377ms/step - loss: 0.0612 - accuracy: 0.9805 - val_loss: 0.4768 - val_accuracy: 0.9183 +Epoch 885/888 +128/128 [==============================] - 47s 370ms/step - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.6870 - val_accuracy: 0.9071 +Epoch 886/888 +128/128 [==============================] - 47s 363ms/step - loss: 0.0322 - accuracy: 0.9922 - val_loss: 0.4509 - val_accuracy: 0.9183 +Epoch 887/888 +128/128 [==============================] - 48s 372ms/step - loss: 0.0341 - accuracy: 0.9907 - val_loss: 0.5670 - val_accuracy: 0.9199 +Epoch 888/888 +128/128 [==============================] - 47s 366ms/step - loss: 0.0192 - accuracy: 0.9976 - val_loss: 0.5340 - val_accuracy: 0.9199 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9199 +Model Test loss: 0.5339 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 411.09 sec +Time taken for epoch(SUBo): 293.02 sec +Time taken for epoch(OTHERo): 118.07 sec +<---------------------------------------|Epoch [148] END|---------------------------------------> + +Epoch: 149/486 (TSEC: 888) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00344]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 889/894 +128/128 [==============================] - 57s 402ms/step - loss: 0.0743 - accuracy: 0.9766 - val_loss: 0.6388 - val_accuracy: 0.9135 +Epoch 890/894 +128/128 [==============================] - 48s 376ms/step - loss: 0.0847 - accuracy: 0.9756 - val_loss: 0.7614 - val_accuracy: 0.9231 +Epoch 891/894 +128/128 [==============================] - 48s 373ms/step - loss: 0.0802 - accuracy: 0.9858 - val_loss: 0.3683 - val_accuracy: 0.9263 +Epoch 892/894 +128/128 [==============================] - 48s 369ms/step - loss: 0.0589 - accuracy: 0.9868 - val_loss: 0.4356 - val_accuracy: 0.9231 +Epoch 893/894 +128/128 [==============================] - 47s 370ms/step - loss: 0.0423 - accuracy: 0.9912 - val_loss: 0.4433 - val_accuracy: 0.9231 +Epoch 894/894 +128/128 [==============================] - 49s 383ms/step - loss: 0.0304 - accuracy: 0.9961 - val_loss: 0.4328 - val_accuracy: 0.9279 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9279 +Model Test loss: 0.4329 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 415.69 sec +Time taken for epoch(SUBo): 298.62 sec +Time taken for epoch(OTHERo): 117.07 sec +<---------------------------------------|Epoch [149] END|---------------------------------------> + +Epoch: 150/486 (TSEC: 894) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00338]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 895/900 +128/128 [==============================] - 56s 395ms/step - loss: 0.0767 - accuracy: 0.9824 - val_loss: 0.3973 - val_accuracy: 0.9231 +Epoch 896/900 +128/128 [==============================] - 46s 362ms/step - loss: 0.0629 - accuracy: 0.9819 - val_loss: 0.5775 - val_accuracy: 0.9103 +Epoch 897/900 +128/128 [==============================] - 47s 364ms/step - loss: 0.0448 - accuracy: 0.9897 - val_loss: 0.5619 - val_accuracy: 0.9006 +Epoch 898/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0353 - accuracy: 0.9927 - val_loss: 0.5996 - val_accuracy: 0.9071 +Epoch 899/900 +128/128 [==============================] - 47s 366ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.6023 - val_accuracy: 0.9054 +Epoch 900/900 +128/128 [==============================] - 48s 372ms/step - loss: 0.0183 - accuracy: 0.9980 - val_loss: 0.6034 - val_accuracy: 0.9087 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9087 +Model Test loss: 0.6034 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 409.43 sec +Time taken for epoch(SUBo): 292.15 sec +Time taken for epoch(OTHERo): 117.28 sec +<---------------------------------------|Epoch [150] END|---------------------------------------> + +Epoch: 151/486 (TSEC: 900) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00332]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 901/906 +128/128 [==============================] - 56s 392ms/step - loss: 0.1011 - accuracy: 0.9717 - val_loss: 0.3600 - val_accuracy: 0.9151 +Epoch 902/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0829 - accuracy: 0.9775 - val_loss: 0.4419 - val_accuracy: 0.9151 +Epoch 903/906 +128/128 [==============================] - 49s 378ms/step - loss: 0.0494 - accuracy: 0.9863 - val_loss: 0.3478 - val_accuracy: 0.9407 +Epoch 904/906 +128/128 [==============================] - 49s 382ms/step - loss: 0.0401 - accuracy: 0.9907 - val_loss: 0.3143 - val_accuracy: 0.9519 +Epoch 905/906 +128/128 [==============================] - 47s 369ms/step - loss: 0.0412 - accuracy: 0.9893 - val_loss: 0.2893 - val_accuracy: 0.9455 +Epoch 906/906 +128/128 [==============================] - 47s 365ms/step - loss: 0.0317 - accuracy: 0.9917 - val_loss: 0.3160 - val_accuracy: 0.9407 +Subset training done. +Not loading weights[BSR:acc{95.6756}, loss{0.0111}|BTR:acc{97.5646}, loss{0.0020}] +Model Test acc: 0.9407 +Model Test loss: 0.3160 +Model accuracy did not improve from 0.9695512652397156. Not saving model. +Model loss did not improve from 0.11880630999803543. Not saving model. +Time taken for epoch(FULL): 416.64 sec +Time taken for epoch(SUBo): 296.21 sec +Time taken for epoch(OTHERo): 120.43 sec +<---------------------------------------|Epoch [151] END|---------------------------------------> + +Epoch: 152/486 (TSEC: 906) | [Fine tuning] +Taking a subset of [|2048|AdvSubset:True]... +Preparing train data... +- Augmenting Image Data... +- Normalizing Image Data... +Setting training OneCycleLr::maxlr to [0.00326]... +Setting training subset epoch.c to [6]... +Training on subset... +Epoch 907/912 +128/128 [==============================] - 56s 393ms/step - loss: 0.0702 - accuracy: 0.9829 - val_loss: 0.3160 - val_accuracy: 0.9439 +Epoch 908/912 +128/128 [==============================] - 47s 366ms/step - loss: 0.0554 - accuracy: 0.9849 - val_loss: 0.4468 - val_accuracy: 0.9407 +Epoch 909/912 +128/128 [==============================] - 48s 370ms/step - loss: 0.0424 - accuracy: 0.9878 - val_loss: 0.3548 - val_accuracy: 0.9407 +Epoch 910/912 +128/128 [==============================] - 47s 368ms/step - loss: 0.0385 - accuracy: 0.9922 - val_loss: 0.4653 - val_accuracy: 0.9311 +Epoch 911/912 + 78/128 [=================>............] - ETA: 13s - loss: 0.0232 - accuracy: 0.9936 +KeyboardInterrupt. +Training done. + diff --git a/backup/V6/Model_T&T.ipynb b/backup/V6/Model_T&T.ipynb index a712b56..08c08b6 100644 --- a/backup/V6/Model_T&T.ipynb +++ b/backup/V6/Model_T&T.ipynb @@ -1,21143 +1,21143 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# keras/TF model\n", - "
\n",
-    " Copyright (c) 2023 Aydin Hamedi\n",
-    " \n",
-    " This software is released under the MIT License.\n",
-    " https://opensource.org/licenses/MIT\n",
-    "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pre Conf" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:27:44.939427800Z", - "start_time": "2023-12-28T02:27:44.923095500Z" - }, - "notebookRunGroups": { - "groupValue": "21" - } - }, - "outputs": [], - "source": [ - "CPU_only = False # True to Force TF to use the cpu" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pylibs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:27:47.128539500Z", - "start_time": "2023-12-28T02:27:44.940432900Z" - }, - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import time\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", - "if CPU_only:\n", - " os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n", - "import cv2\n", - "import glob \n", - "import keras\n", - "import pprint\n", - "import random\n", - "import shutil\n", - "import gzip\n", - "import glob\n", - "import pickle\n", - "import datetime\n", - "import subprocess\n", - "import gpu_control\n", - "import numpy as np\n", - "import pandas as pd\n", - "from tqdm import tqdm\n", - "import seaborn as sns\n", - "from hyperas import optim\n", - "# import tensorflow_addons as tfa\n", - "from keras_adabound import AdaBound\n", - "from importlib import reload\n", - "from keras.losses import categorical_crossentropy\n", - "import tensorflow as tf\n", - "from keras.models import Model\n", - "from scipy.ndimage import zoom\n", - "import matplotlib.pyplot as plt\n", - "from model_profiler import model_profiler\n", - "from keras_gradient_noise import add_gradient_noise\n", - "from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax\n", - "# from tensorflow_addons.optimizers import Yogi\n", - "from adabelief_tf import AdaBeliefOptimizer\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from imblearn.over_sampling import SMOTE\n", - "from keras.regularizers import l2\n", - "from keras.models import load_model\n", - "from matplotlib import pyplot as plt\n", - "from PIL import Image, ImageDraw, ImageFont\n", - "from keras import Sequential\n", - "from random import randint, choice, shuffle\n", - "from keras.callbacks import EarlyStopping\n", - "from keras.callbacks import TensorBoard\n", - "from keras.utils import to_categorical\n", - "from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler\n", - "from sklearn.model_selection import train_test_split\n", - "from keras.preprocessing.image import ImageDataGenerator\n", - "from keras.layers import Conv2D,\\\n", - " MaxPooling2D,\\\n", - " Flatten,\\\n", - " Dense,\\\n", - " Dropout,\\\n", - " BatchNormalization,\\\n", - " SeparableConv2D,\\\n", - " Input, Concatenate,\\\n", - " GlobalAveragePooling2D,\\\n", - " CuDNNLSTM, concatenate,\\\n", - " Reshape, Multiply, \\\n", - " Conv1D, MaxPooling1D\n", - "# Utils\n", - "from Utils.one_cycle import OneCycleLr\n", - "from Utils.lr_find import LrFinder\n", - "from Utils.print_color_V2_NEW import print_Color_V2\n", - "from Utils.print_color_V1_OLD import print_Color\n", - "from Utils.Other import *\n", - "# Other\n", - "tf.get_logger().setLevel('ERROR')\n", - "physical_devices = tf.config.list_physical_devices('GPU')\n", - "for gpu_instance in physical_devices:\n", - " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conf\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data processing conf" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:27:47.139048Z", - "start_time": "2023-12-28T02:27:47.116546100Z" - }, - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [], - "source": [ - "# Directory paths# Directory paths for training, test and validation image data\n", - "train_dir = 'Database\\\\Train\\\\Data\\\\train'\n", - "test_dir = 'Database\\\\Train\\\\Data\\\\test'\n", - "validation_dir = 'Database\\\\Train\\\\Data\\\\val'\n", - "img_res = [224, 224, 3]\n", - "# img_res = [324, 324, 3]\n", - "# img_res = [224, 224, 3]\n", - "# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!)\n", - "interpolation_order_IFG = 2\n", - "categorical_IMP = True\n", - "Make_EV_DATA = False\n", - "R_fill_mode = True\n", - "add_img_grain = True\n", - "Save_TS = True\n", - "Use_SMOTE = False # (⚠️Beta⚠️)\n", - "ADBD = 0\n", - "OP_HDC = False\n", - "SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1\n", - "LNTS = 0\n", - "Debug_OUT = False\n", - "adjust_brightness_Mode = True\n", - "RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️)\n", - "scale_data_NP_M = False # (⚠️deprecated⚠️)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:27:48.287855100Z", - "start_time": "2023-12-28T02:27:48.252944800Z" - }, - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "SAVE_TYPE = 'H5'\n", - "Use_mixed_float16 = False\n", - "#Other\n", - "if Use_mixed_float16:\n", - " tf.keras.mixed_precision.set_global_policy('mixed_float16')\n", - "else:\n", - " tf.keras.mixed_precision.set_global_policy('float32')\n", - " \n", - "print(tf.keras.mixed_precision.global_policy())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## data processing \n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:31:27.059139500Z", - "start_time": "2023-12-28T02:27:50.219209700Z" - }, - "notebookRunGroups": { - "groupValue": "12" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mUsing Def IDG...\u001b[0m\n", - "Found 23681 images belonging to 2 classes.\n", - "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", - "\u001b[0;33mMaking categorical data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mNormalizing image data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n", - "\u001b[0;33mSetting LNTS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", - "\u001b[0;33mshuffling data...\u001b[0m\n", - "\u001b[0;33mSaving TS...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d01-h22_m20_s41\u001b[0m\n", - "\u001b[0;32mDone.\u001b[0m\n" - ] - } - ], - "source": [ - "#Z_SCORE_normalize\n", - "def Z_SCORE_normalize(arr):\n", - " arr = arr.astype('float32')\n", - " mean = np.mean(arr)\n", - " std_dev = np.std(arr)\n", - " arr = (arr - mean) / std_dev\n", - " return arr\n", - "#normalize_TO_RANGE\n", - "def normalize_TO_RANGE(arr, min_val, max_val):\n", - " arr = arr.astype('float32')\n", - " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", - " arr = arr * (max_val - min_val) + min_val\n", - " return arr\n", - "#scale_data\n", - "def scale_data_NP(data):\n", - " if scale_data_NP_M:\n", - " data = data.astype('float32')\n", - " data = (data - 127.5) / 127.5\n", - " return data\n", - " else:\n", - " return data / 255\n", - "#add_image_grain\n", - "def add_image_grain(image, intensity = 0.01):\n", - " # Generate random noise array\n", - " noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8)\n", - "\n", - " # Scale the noise array\n", - " scaled_noise = (noise * intensity).astype(np.float32)\n", - " # Add the noise to the image\n", - " noisy_image = cv2.add(image, scaled_noise)\n", - "\n", - " return noisy_image\n", - "#apply_clahe_rgb_array\n", - "def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)):\n", - " # Create a CLAHE object\n", - " clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n", - " \n", - " # Iterate over each image in the array\n", - " for i in range(len(images)):\n", - " # Split the image into color channels\n", - " b, g, r = cv2.split(images[i])\n", - " \n", - " # Convert the channels to the appropriate format\n", - " b = cv2.convertScaleAbs(b)\n", - " g = cv2.convertScaleAbs(g)\n", - " r = cv2.convertScaleAbs(r)\n", - " \n", - " # Apply adaptive histogram equalization to each channel\n", - " equalized_b = clahe.apply(b)\n", - " equalized_g = clahe.apply(g)\n", - " equalized_r = clahe.apply(r)\n", - "\n", - " # Merge the equalized channels back into an image\n", - " equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r))\n", - "\n", - " # Replace the original image with the equalized image in the array\n", - " images[i] = equalized_image\n", - "\n", - " return images\n", - "#noise_func\n", - "def noise_func(image):\n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.05, 0.05)\n", - " intensityL1 = random.uniform(-0.04, 0.04)\n", - " else:\n", - " intensityL2 = random.uniform(-0.06, 0.06)\n", - " intensityL1 = random.uniform(-0.04, 0.04)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 64)\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image.shape[0], block_size_L2):\n", - " for j in range(0, image.shape[1], block_size_L2):\n", - " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image = new_image \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image.shape[0], block_size_L1):\n", - " for j in range(0, image.shape[1], block_size_L1):\n", - " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026\n", - " new_image = add_image_grain(new_image, intensity=intensity)\n", - " return new_image\n", - "#shuffle_data\n", - "def shuffle_data(x, y):\n", - " indices = np.arange(x.shape[0])\n", - " np.random.shuffle(indices)\n", - " x = x[indices]\n", - " y = y[indices]\n", - " return x, y\n", - "#save_images_to_dir\n", - "def save_images_to_dir(images, labels, dir_path):\n", - " # create the directory if it doesn't exist\n", - " if not os.path.exists(dir_path):\n", - " os.makedirs(dir_path)\n", - " # iterate over the images and labels\n", - " for i, (image, label) in enumerate(zip(images, labels)):\n", - " # get the class label\n", - " class_label = np.argmax(label)\n", - " # create the file path\n", - " file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png')\n", - " # save the image to the file path\n", - " plt.imsave(file_path, image.squeeze())\n", - " # compress the directory\n", - " shutil.make_archive(dir_path, 'gztar', dir_path)\n", - " # remove the original directory\n", - " shutil.rmtree(dir_path)\n", - "#Debug_img_Save\n", - "def Debug_img_Save(img, id = 'DEF'): \n", - " SITD = np.random.choice(img.shape[0], size=400, replace=False)\n", - " S_dir = f'Samples\\\\Debug\\\\{id}\\\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", - " save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir)\n", - "# Create an ImageDataGenerator for the training set\n", - "if OP_HDC:\n", - " print_Color('Using OP_HDC IDG...', ['yellow'])\n", - " train_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.24, \n", - " shear_range=0.22,\n", - " width_shift_range=0.21,\n", - " brightness_range=(0.86, 1.1),\n", - " height_shift_range=0.21,\n", - " channel_shift_range=100,\n", - " featurewise_center=False,\n", - " featurewise_std_normalization=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " fill_mode='nearest', # constant\n", - " preprocessing_function=noise_func\n", - " )\n", - "else:\n", - " print_Color('Using Def IDG...', ['yellow'])\n", - " train_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.26, \n", - " shear_range=0.25,\n", - " width_shift_range=0.25,\n", - " brightness_range=(0.78, 1.1),\n", - " height_shift_range=0.25,\n", - " channel_shift_range=100,\n", - " featurewise_center=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " featurewise_std_normalization=False,\n", - " fill_mode='nearest', # constant\n", - " preprocessing_function=noise_func\n", - " )\n", - "train_datagen_SM = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " vertical_flip=False,\n", - " rotation_range=20,\n", - " zoom_range=0.07, \n", - " shear_range=0.07,\n", - " width_shift_range=0.07,\n", - " brightness_range=(0.99, 1.01),\n", - " height_shift_range=0.07,\n", - " channel_shift_range=0,\n", - " featurewise_center=False,\n", - " interpolation_order=interpolation_order_IFG,\n", - " featurewise_std_normalization=False\n", - ")\n", - "# Create an iterator for the training set\n", - "train_generator_SM = train_datagen_SM.flow_from_directory(\n", - " train_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]),\n", - " class_mode='binary')\n", - "# Create an ImageDataGenerator for the validation set (OP)\n", - "if Make_EV_DATA:\n", - " val_datagen = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " zoom_range = 0.01, \n", - " width_shift_range=0.01, \n", - " interpolation_order=interpolation_order_IFG,\n", - " height_shift_range=0.01)\n", - "\n", - " # Create an iterator for the validation set\n", - " val_generator = val_datagen.flow_from_directory(\n", - " validation_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]),\n", - " class_mode='binary',\n", - " color_mode='rgb')\n", - "\n", - " # Create an ImageDataGenerator for the test set\n", - " test_datagen = ImageDataGenerator(\n", - " horizontal_flip=False,\n", - " zoom_range = 0.01, \n", - " width_shift_range=0.01, \n", - " interpolation_order=interpolation_order_IFG,\n", - " height_shift_range=0.01)\n", - "\n", - " # Create an iterator for the test set\n", - " test_generator = test_datagen.flow_from_directory(\n", - " test_dir,\n", - " target_size=(img_res[0], img_res[1]),\n", - " batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]),\n", - " class_mode='binary',\n", - " color_mode='rgb')\n", - "# Load all images and labels into memory\n", - "print_Color('Loading all images and labels into memory...', ['yellow'])\n", - "x_train, y_train = next(iter(train_generator_SM))\n", - "if Make_EV_DATA:\n", - " x_val, y_val = next(iter(val_generator))\n", - " x_test, y_test = next(iter(test_generator))\n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG\n", - "# fit parameters from data\n", - "# train_datagen.fit(x_train)\n", - "#to_categorical (TEMP)\n", - "if categorical_IMP:\n", - " print_Color('Making categorical data...', ['yellow'])\n", - " y_train = to_categorical(y_train, num_classes=2)\n", - " if Make_EV_DATA:\n", - " y_val = to_categorical(y_val, num_classes=2)\n", - " y_test = to_categorical(y_test, num_classes=2)\n", - "# Use_SMOTE\n", - "if Use_SMOTE:\n", - " print_Color('SMOTE...', ['yellow'])\n", - " # Convert y_train from one-hot encoding to label encoding\n", - " y_train_label_encoded = np.argmax(y_train, axis=1)\n", - "\n", - " # Print the original label distribution\n", - " unique, counts = np.unique(y_train_label_encoded, return_counts=True)\n", - " print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True)\n", - "\n", - " # Use SMOTE to oversample the minority class\n", - " smote = SMOTE(random_state=42)\n", - " x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded)\n", - "\n", - " # Print the resampled label distribution\n", - " unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True)\n", - " print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True)\n", - "\n", - " # Reshape x_train_res back to the original x_train shape\n", - " x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3])\n", - "\n", - " # Convert y_train_res from label encoding back to one-hot encoding\n", - " y_train_res = to_categorical(y_train_res_label_encoded)\n", - "\n", - " # Calculate the ratio of two labels after resampling\n", - " pneumonia_count = np.sum(y_train_res[:, 1])\n", - " total_count = y_train_res.shape[0]\n", - " label_ratio_res = pneumonia_count / total_count\n", - " label_ratio_percentage_res = label_ratio_res * 100\n", - "\n", - " # Replace the original data with the resampled data\n", - " x_train = x_train_res\n", - " y_train = y_train_res\n", - "\n", - " # Delete the resampled data to free up memory\n", - " del x_train_res, y_train_res_label_encoded, y_train_res\n", - "# Generating augmented data\n", - "print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...',\n", - " ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'],\n", - " advanced_mode=True)\n", - "if ADBD > 0:\n", - " for i in range(ADBD):\n", - " # ADB_clip_limit Scheduler>>>\n", - " if i == 0:\n", - " ADB_clip_limit = 0.8\n", - " else:\n", - " #V1>>>\n", - " CL_SLM = 2.4\n", - " ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05)\n", - " # Try it in win graphing calculator copy and paste:\n", - " # β”Œ-------------┬--┬---------------┐\n", - " # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚\n", - " # β””-------------β”΄--β”΄---------------β”˜\n", - " #V2>>>\n", - " # CL_SLM_2 = 1.4\n", - " # CL_SLM_Start_2 = 2\n", - " # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) \n", - " # Try it in win graphing calculator copy and paste:\n", - " # β”Œ-----------------┬--┬-------------------┐\n", - " # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚\n", - " # β””-----------------β”΄--β”΄-------------------β”˜\n", - " print(f'> Generating ADB[{i+1}/{ADBD}]...')\n", - " # prepare an iterators to scale images\n", - " train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train))\n", - "\n", - " # get augmented data\n", - " x_train_augmented, y_train_augmented = train_iterator.next()\n", - " print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...')\n", - " print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}')\n", - " x_train_augmented = np.clip(x_train_augmented, 0, 255) \n", - " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG\n", - " #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", - " x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss\n", - " print(f'> └───Adding the Generated ADB...')\n", - " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG\n", - " # append augmented data to original data\n", - " x_train = np.concatenate([x_train, x_train_augmented])\n", - " y_train = np.concatenate([y_train, y_train_augmented])\n", - " #free up memory\n", - " del y_train_augmented\n", - " del x_train_augmented\n", - "# normalizing \n", - "print_Color('Normalizing image data...', ['yellow'])\n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG\n", - "x_train = np.clip(x_train, 0, 255)\n", - "if RANGE_NOM:\n", - " x_train = scale_data_NP(x_train)\n", - "y_train = np.array(y_train) \n", - "if Make_EV_DATA:\n", - " x_test = np.clip(x_test, 0, 255) \n", - " x_val = np.clip(x_val, 0, 255) \n", - " if RANGE_NOM:\n", - " x_val = scale_data_NP(x_val)\n", - " y_val = np.array(y_val) \n", - " if RANGE_NOM: \n", - " x_test = scale_data_NP(x_test)\n", - " y_test = np.array(y_test) \n", - "if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG\n", - "# Check the data type of image data\n", - "print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True)\n", - "# Check the range of image data\n", - "print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", - "# Calculate the ratio of two labels\n", - "if categorical_IMP:\n", - " label_sums = np.sum(y_train, axis=0)\n", - " label_ratio = label_sums / (np.sum(y_train) + 1e-10)\n", - " label_ratio_percentage = label_ratio * 100\n", - " print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL',\n", - " ['normal', 'red', 'magenta', 'green'], advanced_mode=True) \n", - "print_Color('Setting LNTS...', ['yellow'])\n", - "# Get the total number of samples in the arrays\n", - "num_samples = x_train.shape[0]\n", - "print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", - "if LNTS != 0:\n", - " print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True)\n", - " print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True)\n", - " # Generate random indices to select LNTS samples\n", - " indices = np.random.choice(num_samples, size=LNTS, replace=False)\n", - " # Select the samples using the generated indices\n", - " x_selected = x_train[indices]\n", - " y_selected = y_train[indices]\n", - " x_train = x_selected\n", - " y_train = y_selected\n", - " #free up memory\n", - " del x_selected\n", - " del y_selected\n", - " del indices\n", - " #Debug\n", - " num_samples = x_train.shape[0]\n", - " print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", - "# Shuffle the training data\n", - "print_Color('shuffling data...', ['yellow'])\n", - "x_train, y_train = shuffle_data(x_train, y_train)\n", - "#save_images_to_dir \n", - "if Save_TS:\n", - " print_Color('Saving TS...', ['yellow'])\n", - " SITD = np.random.choice(num_samples, size=400, replace=False)\n", - " S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True)\n", - " if RANGE_NOM:\n", - " if scale_data_NP_M:\n", - " save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir)\n", - " else:\n", - " save_images_to_dir(x_train[SITD], y_train[SITD], S_dir)\n", - " else:\n", - " save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir)\n", - "print_Color('Done.', ['green'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save EV Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.save(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy', x_val)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy', y_val)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy', x_test)\n", - "np.save(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy', y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load EV Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:31:27.380088800Z", - "start_time": "2023-12-28T02:31:27.270860200Z" - }, - "notebookRunGroups": { - "groupValue": "1" - } - }, - "outputs": [], - "source": [ - "x_val = np.load(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy')\n", - "y_val = np.load(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy')\n", - "x_test = np.load(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy')\n", - "y_test = np.load(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Analyzation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import seaborn as sns\n", - "from scipy.stats import zscore\n", - "\n", - "# Select a subset of your data\n", - "subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels\n", - "subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values\n", - "indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False)\n", - "indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False)\n", - "subset_pixels = x_train[indices_pixels]\n", - "subset_mean = x_train[indices_mean]\n", - "\n", - "# Reshape the data for calculating Z-scores\n", - "reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1])\n", - "reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1])\n", - "\n", - "# Calculate the mean intensity\n", - "mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1)\n", - "mean_intensity_mean = reshaped_data_mean.mean(axis=-1)\n", - "\n", - "# Stack the mean intensity with the reshaped data\n", - "data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)])\n", - "data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)])\n", - "\n", - "# Calculate Z-scores\n", - "z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))\n", - "z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0))\n", - "\n", - "# Identify outliers\n", - "outliers_pixels = np.where(z_scores_pixels > 3)\n", - "outliers_mean = np.where(z_scores_mean > 3)\n", - "\n", - "# Create a 3D scatter plot for RGB channels\n", - "fig = plt.figure(figsize=(10, 20))\n", - "\n", - "# Plot for individual pixels\n", - "ax = fig.add_subplot(211, projection='3d')\n", - "ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1)\n", - "ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red')\n", - "ax.set_title('Z-Score Scatter Plot for Individual Pixels')\n", - "ax.set_xlabel('Red')\n", - "ax.set_ylabel('Green')\n", - "ax.set_zlabel('Blue')\n", - "\n", - "# Plot for mean RGB values\n", - "ax = fig.add_subplot(212, projection='3d')\n", - "ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1)\n", - "ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red')\n", - "ax.set_title('Z-Score Scatter Plot for Mean RGB Values')\n", - "ax.set_xlabel('Red')\n", - "ax.set_ylabel('Green')\n", - "ax.set_zlabel('Blue')\n", - "\n", - "# Density plot of the mean intensity\n", - "plt.figure(figsize=(10, 5))\n", - "sns.kdeplot(data=z_scores_pixels[:, -1], fill=True)\n", - "plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels')\n", - "plt.xlabel('Z-Score')\n", - "\n", - "sns.kdeplot(data=z_scores_mean[:, -1], fill=True)\n", - "plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values')\n", - "plt.xlabel('Z-Score')\n", - "\n", - "# Display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating the model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1\n", - "```\n", - "recommended: ⚠️\n", - "statuses: Ready\n", - "Working: βœ…\n", - "Max fine tuned acc: β‰…95.1\n", - "Max fine tuned acc TLRev2: N/A\n", - "type: transfer learning>>>(EfficientNetB7)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.applications import EfficientNetB7\n", - "\n", - "EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax')\n", - "# define new model\n", - "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.summary()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.1\n", - "```\n", - "recommended: ❌\n", - "statuses: S.Ready (can improve)\n", - "Working: ❌\n", - "Max fine tuned acc: β‰…93.2\n", - "Max fine tuned acc TLRev2: N/A\n", - "type: transfer learning>>>(ConvNeXtLarge)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.applications import ConvNeXtLarge\n", - "\n", - "ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False)\n", - "# define new model\n", - "model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.summary()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "source": [ - "### Rev1.2\n", - "```\n", - "recommended: βœ…\n", - "statuses: Ready\n", - "Working: βœ…\n", - "Max fine tuned acc: 95.3\n", - "Max fine tuned acc TLRev2: 96.96\n", - "type: transfer learning>>>(EfficientNetB7::CCL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-27T17:34:12.077394600Z", - "start_time": "2023-12-27T17:34:05.068171500Z" - }, - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from efficientnet.keras import EfficientNetB7 as KENB7\n", - "# FUNC\n", - "def Eff_B7_NS(freeze_layers):\n", - " base_model = KENB7(input_shape=(\n", - " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) /\n", - " len(base_model.layers)) * 100\n", - " print(\n", - " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(\n", - " inputs=base_model.input, outputs=predictions)\n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " # OPT/compile\n", - " opt = SGD(momentum=0.9, nesterov=False)\n", - " # opt = Nadam()\n", - " # opt = Adamax()\n", - " # opt = RMSprop(momentum=0.9)\n", - " # opt = Adagrad()\n", - " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B7_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.3\n", - "```\n", - "recommended: ❌\n", - "statuses: Test\n", - "Working: βœ…\n", - "Max fine tuned acc: ⚠️\n", - "Max fine tuned acc TLRev2: ⚠️\n", - "type: transfer learning>>>(EfficientNetB7|Xception::CCL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total base_model1 layers: 806\n", - "Total base_model2 layers: 132\n", - "Total model layers: 15\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " efficientnet-b7 (Functional) (None, 7, 7, 2560) 64097680 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| stem_conv (Conv2D) (None, 112, 112, 64 1728 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_bn (BatchNormalization) (None, 112, 112, 64 256 [] Y |\n", - "| ) |\n", - "| |\n", - "| stem_activation (Activation) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1a_bn (BatchNormalization (None, 112, 112, 64 256 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_activation (Activation (None, 112, 112, 64 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1a_se_squeeze (GlobalAver (None, 64) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 [] Y |\n", - "| |\n", - "| block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 [] Y |\n", - "| |\n", - "| block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 [] Y |\n", - "| |\n", - "| block1a_se_excite (Multiply) (None, 112, 112, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1a_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1b_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1b_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1b_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1b_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1b_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1c_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1c_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1c_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1c_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1c_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block1d_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_activation (Activation (None, 112, 112, 32 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block1d_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", - "| |\n", - "| block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", - "| |\n", - "| block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", - "| |\n", - "| block1d_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", - "| lization) ) |\n", - "| |\n", - "| block1d_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1d_add (Add) (None, 112, 112, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 [] Y |\n", - "| 2) |\n", - "| |\n", - "| block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 [] Y |\n", - "| ization) 2) |\n", - "| |\n", - "| block2a_expand_activation (Act (None, 112, 112, 19 0 [] Y |\n", - "| ivation) 2) |\n", - "| |\n", - "| block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2a_bn (BatchNormalization (None, 56, 56, 192) 768 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_activation (Activation (None, 56, 56, 192) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2a_se_squeeze (GlobalAver (None, 192) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 [] Y |\n", - "| |\n", - "| block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 [] Y |\n", - "| |\n", - "| block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 [] Y |\n", - "| |\n", - "| block2a_se_excite (Multiply) (None, 56, 56, 192) 0 [] Y |\n", - "| |\n", - "| block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 [] Y |\n", - "| |\n", - "| block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2b_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2b_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2b_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2b_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2b_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2c_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2c_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2c_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2c_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2c_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2d_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2d_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2d_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2d_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2d_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2e_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2e_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2e_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2e_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2e_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2f_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2f_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2f_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2f_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2f_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block2g_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", - "| D) |\n", - "| |\n", - "| block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2g_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block2g_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", - "| |\n", - "| block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", - "| |\n", - "| block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block2g_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block2g_add (Add) (None, 56, 56, 48) 0 [] Y |\n", - "| |\n", - "| block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", - "| |\n", - "| block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3a_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_activation (Activation (None, 28, 28, 288) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3a_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", - "| |\n", - "| block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", - "| |\n", - "| block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", - "| |\n", - "| block3a_se_excite (Multiply) (None, 28, 28, 288) 0 [] Y |\n", - "| |\n", - "| block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 [] Y |\n", - "| |\n", - "| block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3b_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3b_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3b_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3b_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3b_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3c_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3c_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3c_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3c_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3c_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3d_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3d_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3d_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3d_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3d_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3e_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3e_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3e_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3e_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3e_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3f_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3f_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3f_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3f_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3f_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block3g_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block3g_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block3g_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", - "| |\n", - "| block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", - "| |\n", - "| block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3g_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block3g_add (Add) (None, 28, 28, 80) 0 [] Y |\n", - "| |\n", - "| block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", - "| |\n", - "| block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4a_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_activation (Activation (None, 14, 14, 480) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4a_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", - "| |\n", - "| block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", - "| |\n", - "| block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", - "| |\n", - "| block4a_se_excite (Multiply) (None, 14, 14, 480) 0 [] Y |\n", - "| |\n", - "| block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 [] Y |\n", - "| |\n", - "| block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4b_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4b_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4b_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4b_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4b_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4c_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4c_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4c_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4c_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4c_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4d_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4d_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4d_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4d_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4d_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4e_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4e_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4e_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4e_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4e_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4f_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4f_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4f_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4f_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4f_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4g_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4g_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4g_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4g_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4g_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4h_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4h_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4h_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4h_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4h_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4i_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4i_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4i_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4i_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4i_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block4j_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", - "| D) |\n", - "| |\n", - "| block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block4j_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block4j_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", - "| |\n", - "| block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4j_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block4j_add (Add) (None, 14, 14, 160) 0 [] Y |\n", - "| |\n", - "| block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", - "| |\n", - "| block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block5a_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 [] Y |\n", - "| D) |\n", - "| |\n", - "| block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5a_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", - "| |\n", - "| block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", - "| |\n", - "| block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", - "| |\n", - "| block5a_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", - "| |\n", - "| block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 [] Y |\n", - "| |\n", - "| block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5b_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5b_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5b_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5b_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5b_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5c_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5c_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5c_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5c_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5c_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5d_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5d_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5d_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5d_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5d_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5e_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5e_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5e_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5e_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5e_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5f_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5f_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5f_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5f_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5f_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5g_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5g_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5g_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5g_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5g_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5h_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5h_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5h_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5h_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5h_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5i_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5i_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5i_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5i_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5i_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block5j_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", - "| D) ) |\n", - "| |\n", - "| block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", - "| ) ) |\n", - "| |\n", - "| block5j_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block5j_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", - "| |\n", - "| block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5j_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block5j_add (Add) (None, 14, 14, 224) 0 [] Y |\n", - "| |\n", - "| block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", - "| ization) ) |\n", - "| |\n", - "| block6a_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", - "| ivation) ) |\n", - "| |\n", - "| block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_activation (Activation (None, 7, 7, 1344) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6a_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", - "| |\n", - "| block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", - "| |\n", - "| block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 [] Y |\n", - "| |\n", - "| block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 [] Y |\n", - "| |\n", - "| block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6b_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6b_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6b_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6b_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6c_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6c_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6c_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6c_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6d_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6d_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6d_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6d_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6e_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6e_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6e_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6e_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6f_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6f_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6f_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6f_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6g_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6g_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6g_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6g_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6h_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6h_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6h_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6h_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6i_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6i_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6i_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6i_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6j_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6j_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6j_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6j_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6k_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6k_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6k_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6k_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6l_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6l_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6l_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6l_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block6m_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", - "| D) |\n", - "| |\n", - "| block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block6m_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", - "| |\n", - "| block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6m_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block6m_add (Add) (None, 7, 7, 384) 0 [] Y |\n", - "| |\n", - "| block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", - "| |\n", - "| block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7a_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7a_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", - "| |\n", - "| block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", - "| |\n", - "| block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", - "| |\n", - "| block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 [] Y |\n", - "| |\n", - "| block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7b_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7b_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7b_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7b_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7c_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7c_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7c_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7c_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", - "| |\n", - "| block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", - "| ization) |\n", - "| |\n", - "| block7d_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", - "| ivation) |\n", - "| |\n", - "| block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", - "| D) |\n", - "| |\n", - "| block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block7d_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", - "| agePooling2D) |\n", - "| |\n", - "| block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", - "| |\n", - "| block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", - "| |\n", - "| block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", - "| |\n", - "| block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", - "| |\n", - "| block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7d_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| block7d_add (Add) (None, 7, 7, 640) 0 [] Y |\n", - "| |\n", - "| top_conv (Conv2D) (None, 7, 7, 2560) 1638400 [] Y |\n", - "| |\n", - "| top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 [] Y |\n", - "| |\n", - "| top_activation (Activation) (None, 7, 7, 2560) 0 [] Y |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " xception (Functional) (None, 7, 7, 2048) 20861480 ['input_1[0][0]'] Y \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", - "| )] |\n", - "| |\n", - "| block1_conv1 (Conv2D) (None, 111, 111, 32 864 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv1_bn (BatchNormaliz (None, 111, 111, 32 128 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv1_act (Activation) (None, 111, 111, 32 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2 (Conv2D) (None, 109, 109, 64 18432 [] Y |\n", - "| ) |\n", - "| |\n", - "| block1_conv2_bn (BatchNormaliz (None, 109, 109, 64 256 [] Y |\n", - "| ation) ) |\n", - "| |\n", - "| block1_conv2_act (Activation) (None, 109, 109, 64 0 [] Y |\n", - "| ) |\n", - "| |\n", - "| block2_sepconv1 (SeparableConv (None, 109, 109, 12 8768 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv1_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| block2_sepconv2_act (Activatio (None, 109, 109, 12 0 [] Y |\n", - "| n) 8) |\n", - "| |\n", - "| block2_sepconv2 (SeparableConv (None, 109, 109, 12 17536 [] Y |\n", - "| 2D) 8) |\n", - "| |\n", - "| block2_sepconv2_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", - "| lization) 8) |\n", - "| |\n", - "| conv2d (Conv2D) (None, 55, 55, 128) 8192 [] Y |\n", - "| |\n", - "| block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| batch_normalization (BatchNorm (None, 55, 55, 128) 512 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add (Add) (None, 55, 55, 128) 0 [] Y |\n", - "| |\n", - "| block3_sepconv1_act (Activatio (None, 55, 55, 128) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv1 (SeparableConv (None, 55, 55, 256) 33920 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv1_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block3_sepconv2_act (Activatio (None, 55, 55, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block3_sepconv2 (SeparableConv (None, 55, 55, 256) 67840 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block3_sepconv2_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 [] Y |\n", - "| |\n", - "| block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| batch_normalization_1 (BatchNo (None, 28, 28, 256) 1024 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_1 (Add) (None, 28, 28, 256) 0 [] Y |\n", - "| |\n", - "| block4_sepconv1_act (Activatio (None, 28, 28, 256) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv1 (SeparableConv (None, 28, 28, 728) 188672 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv1_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block4_sepconv2_act (Activatio (None, 28, 28, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block4_sepconv2 (SeparableConv (None, 28, 28, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block4_sepconv2_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 [] Y |\n", - "| |\n", - "| block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| batch_normalization_2 (BatchNo (None, 14, 14, 728) 2912 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_2 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block5_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block5_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block5_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block5_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_3 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block6_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block6_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block6_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block6_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_4 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block7_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block7_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block7_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block7_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_5 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block8_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block8_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block8_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block8_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_6 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block9_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| block9_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", - "| n) |\n", - "| |\n", - "| block9_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", - "| 2D) |\n", - "| |\n", - "| block9_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", - "| lization) |\n", - "| |\n", - "| add_7 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block10_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block10_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block10_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block10_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_8 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block11_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block11_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block11_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block11_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_9 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block12_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block12_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block12_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block12_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| add_10 (Add) (None, 14, 14, 728) 0 [] Y |\n", - "| |\n", - "| block13_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block13_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block13_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block13_sepconv2 (SeparableCon (None, 14, 14, 1024 752024 [] Y |\n", - "| v2D) ) |\n", - "| |\n", - "| block13_sepconv2_bn (BatchNorm (None, 14, 14, 1024 4096 [] Y |\n", - "| alization) ) |\n", - "| |\n", - "| conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 [] Y |\n", - "| |\n", - "| block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 [] Y |\n", - "| rmalization) |\n", - "| |\n", - "| add_11 (Add) (None, 7, 7, 1024) 0 [] Y |\n", - "| |\n", - "| block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 [] Y |\n", - "| on) |\n", - "| |\n", - "| block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 [] Y |\n", - "| v2D) |\n", - "| |\n", - "| block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 [] Y |\n", - "| alization) |\n", - "| |\n", - "| block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 [] Y |\n", - "| on) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (Glob (None, 2560) 0 ['efficientnet-b7[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " global_average_pooling2d_1 (Gl (None, 2048) 0 ['xception[0][0]'] Y \n", - " obalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dense_1 (Dense) (None, 512) 1049088 ['global_average_pooling2d_1[0] Y \n", - " [0]'] \n", - " \n", - " concatenate (Concatenate) (None, 1024) 0 ['dense[0][0]', Y \n", - " 'dense_1[0][0]'] \n", - " \n", - " dense_2 (Dense) (None, 1024) 1049600 ['concatenate[0][0]'] Y \n", - " \n", - " dropout (Dropout) (None, 1024) 0 ['dense_2[0][0]'] Y \n", - " \n", - " batch_normalization_4 (BatchNo (None, 1024) 4096 ['dropout[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_3 (Dense) (None, 512) 524800 ['batch_normalization_4[0][0]'] Y \n", - " \n", - " batch_normalization_5 (BatchNo (None, 512) 2048 ['dense_3[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_4 (Dense) (None, 128) 65664 ['batch_normalization_5[0][0]'] Y \n", - " \n", - " dense_5 (Dense) (None, 2) 258 ['dense_4[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 88,965,946\n", - "Trainable params: 88,597,626\n", - "Non-trainable params: 368,320\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from efficientnet.keras import EfficientNetB7 as KENB7\n", - "from keras.applications.xception import Xception\n", - "\n", - "#FUNC\n", - "def Combo_Model(freeze_layers1, freeze_layers2):\n", - " # Define a common input\n", - " common_input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", - "\n", - " # Base model 1\n", - " base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " # base_model1.load_weights('models\\Ready\\Other\\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True)\n", - " base_model1_out = base_model1(common_input)\n", - " \n", - " # Base model 2\n", - " base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False)\n", - " # base_model1.load_weights('models\\Ready\\Other\\Xception_PRET.h5', by_name=True, skip_mismatch=True)\n", - " base_model2_out = base_model2(common_input)\n", - "\n", - " print('Total base_model1 layers: ', len(base_model1.layers))\n", - " print('Total base_model2 layers: ', len(base_model2.layers))\n", - " \n", - " # Freeze the specified number of layers in both models\n", - " for layer in base_model1.layers[:freeze_layers1]:\n", - " layer.trainable = False\n", - " for layer in base_model2.layers[:freeze_layers2]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest in both models\n", - " for layer in base_model1.layers[freeze_layers1:]:\n", - " layer.trainable = True\n", - " for layer in base_model2.layers[freeze_layers2:]:\n", - " layer.trainable = True\n", - "\n", - " # Combine the output of the two base models\n", - " combined = concatenate([Dense(512,\n", - " activation='relu',\n", - " kernel_regularizer=l2(0.02)\n", - " )(GlobalAveragePooling2D()(base_model1_out)),\n", - " Dense(512,\n", - " activation='relu',\n", - " kernel_regularizer=l2(0.02)\n", - " )(GlobalAveragePooling2D()(base_model2_out))])\n", - "\n", - " # adding CDL\n", - " Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined)\n", - " Dropout_L1 = Dropout(0.4)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " combo_model = Model(inputs=common_input, outputs=predictions) \n", - " print('Total model layers: ', len(combo_model.layers))\n", - " \n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return combo_model\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers_1 = 0\n", - "freeze_layers_2 = 0\n", - "model = Combo_Model(freeze_layers_1, freeze_layers_2)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rev1.4\n", - "```\n", - "recommended: ⚠️\n", - "statuses: Test\n", - "Working: βœ…\n", - "Max fine tuned acc: ⚠️\n", - "Max fine tuned acc TLRev2: β‰…95.64\n", - "type: transfer learning>>>(EfficientNetV2XL)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n", - " ) 'stack_0_block0_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n", - " ) 'stack_0_block1_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n", - " ) 'stack_0_block2_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n", - " ) ) \n", - " \n", - " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n", - " malization) ) ] \n", - " \n", - " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n", - " ation) ) \n", - " \n", - " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n", - " ) 'stack_0_block3_fu_swish[0][0] \n", - " '] \n", - " \n", - " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_1_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n", - " 'stack_1_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n", - " 'stack_1_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n", - " 'stack_1_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n", - " 'stack_1_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n", - " 'stack_1_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n", - " 'stack_1_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_2_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n", - " 'stack_2_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n", - " 'stack_2_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n", - " 'stack_2_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n", - " 'stack_2_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n", - " 'stack_2_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n", - " v2D) 0][0]'] \n", - " \n", - " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n", - " 'stack_2_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " a) [0]'] \n", - " \n", - " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_1[0][0]'] \n", - " \n", - " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_3[0][0]'] \n", - " \n", - " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_3_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_5[0][0]'] \n", - " \n", - " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n", - " 'stack_3_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_7[0][0]'] \n", - " \n", - " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n", - " 'stack_3_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_9[0][0]'] \n", - " \n", - " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n", - " 'stack_3_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_11[0][0]'] \n", - " \n", - " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n", - " 'stack_3_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_13[0][0]'] \n", - " \n", - " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n", - " 'stack_3_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_15[0][0]'] \n", - " \n", - " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n", - " 'stack_3_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_17[0][0]'] \n", - " \n", - " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n", - " 'stack_3_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " bda) [0]'] \n", - " \n", - " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_19[0][0]'] \n", - " \n", - " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n", - " 'stack_3_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_21[0][0]'] \n", - " \n", - " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n", - " 'stack_3_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_23[0][0]'] \n", - " \n", - " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n", - " 'stack_3_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_25[0][0]'] \n", - " \n", - " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n", - " 'stack_3_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_27[0][0]'] \n", - " \n", - " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n", - " 'stack_3_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_29[0][0]'] \n", - " \n", - " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n", - " 'stack_3_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_31[0][0]'] \n", - " \n", - " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n", - " 'stack_3_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_33[0][0]'] \n", - " \n", - " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " onv2D) ) '] \n", - " \n", - " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_35[0][0]'] \n", - " \n", - " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_4_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_37[0][0]'] \n", - " \n", - " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n", - " 'stack_4_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_39[0][0]'] \n", - " \n", - " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n", - " 'stack_4_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_41[0][0]'] \n", - " \n", - " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n", - " 'stack_4_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_43[0][0]'] \n", - " \n", - " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n", - " 'stack_4_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_45[0][0]'] \n", - " \n", - " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n", - " 'stack_4_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_47[0][0]'] \n", - " \n", - " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n", - " 'stack_4_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_49[0][0]'] \n", - " \n", - " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n", - " 'stack_4_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n", - " seConv2D) ) 0][0]'] \n", - " \n", - " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n", - " Normalization) ) \n", - " \n", - " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n", - " tivation) ) '] \n", - " \n", - " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n", - " ) [0]', \n", - " 'activation_51[0][0]'] \n", - " \n", - " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n", - " 'stack_4_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_53[0][0]'] \n", - " \n", - " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n", - " 'stack_4_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_55[0][0]'] \n", - " \n", - " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n", - " 'stack_4_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_57[0][0]'] \n", - " \n", - " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n", - " 'stack_4_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_59[0][0]'] \n", - " \n", - " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n", - " 'stack_4_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_61[0][0]'] \n", - " \n", - " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n", - " 'stack_4_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_63[0][0]'] \n", - " \n", - " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n", - " 'stack_4_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_65[0][0]'] \n", - " \n", - " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n", - " 'stack_4_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_67[0][0]'] \n", - " \n", - " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n", - " 'stack_4_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_69[0][0]'] \n", - " \n", - " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n", - " 'stack_4_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_71[0][0]'] \n", - " \n", - " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n", - " 'stack_4_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_73[0][0]'] \n", - " \n", - " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n", - " 'stack_4_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_75[0][0]'] \n", - " \n", - " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n", - " 'stack_4_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_77[0][0]'] \n", - " \n", - " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n", - " 'stack_4_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n", - " Conv2D) ) \n", - " \n", - " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n", - " tchNormalization) ) 0][0]'] \n", - " \n", - " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n", - " (Activation) ) [0]'] \n", - " \n", - " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n", - " iseConv2D) ) [0][0]'] \n", - " \n", - " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ) ] \n", - " \n", - " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ) ]'] \n", - " \n", - " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n", - " ) ][0]', \n", - " 'activation_79[0][0]'] \n", - " \n", - " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n", - " 'stack_4_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n", - " onv2D) ) \n", - " \n", - " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n", - " chNormalization) ) ][0]'] \n", - " \n", - " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n", - " Activation) ) 0]'] \n", - " \n", - " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_81[0][0]'] \n", - " \n", - " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_83[0][0]'] \n", - " \n", - " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_5_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_85[0][0]'] \n", - " \n", - " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n", - " 'stack_5_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_87[0][0]'] \n", - " \n", - " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n", - " 'stack_5_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_89[0][0]'] \n", - " \n", - " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n", - " 'stack_5_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_91[0][0]'] \n", - " \n", - " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n", - " 'stack_5_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_93[0][0]'] \n", - " \n", - " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n", - " 'stack_5_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_95[0][0]'] \n", - " \n", - " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n", - " 'stack_5_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_97[0][0]'] \n", - " \n", - " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n", - " 'stack_5_block8_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_99[0][0]'] \n", - " \n", - " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n", - " 'stack_5_block9_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_101[0][0]'] \n", - " \n", - " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n", - " 'stack_5_block10_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_103[0][0]'] \n", - " \n", - " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n", - " 'stack_5_block11_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_105[0][0]'] \n", - " \n", - " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n", - " 'stack_5_block12_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_107[0][0]'] \n", - " \n", - " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n", - " 'stack_5_block13_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_109[0][0]'] \n", - " \n", - " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n", - " 'stack_5_block14_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_111[0][0]'] \n", - " \n", - " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n", - " 'stack_5_block15_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_113[0][0]'] \n", - " \n", - " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n", - " 'stack_5_block16_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_115[0][0]'] \n", - " \n", - " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n", - " 'stack_5_block17_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_117[0][0]'] \n", - " \n", - " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n", - " 'stack_5_block18_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_119[0][0]'] \n", - " \n", - " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n", - " 'stack_5_block19_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_121[0][0]'] \n", - " \n", - " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n", - " 'stack_5_block20_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_123[0][0]'] \n", - " \n", - " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n", - " 'stack_5_block21_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_125[0][0]'] \n", - " \n", - " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n", - " 'stack_5_block22_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_127[0][0]'] \n", - " \n", - " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n", - " 'stack_5_block23_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_129[0][0]'] \n", - " \n", - " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n", - " 'stack_5_block24_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_131[0][0]'] \n", - " \n", - " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n", - " 'stack_5_block25_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_133[0][0]'] \n", - " \n", - " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n", - " 'stack_5_block26_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_135[0][0]'] \n", - " \n", - " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n", - " 'stack_5_block27_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_137[0][0]'] \n", - " \n", - " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n", - " 'stack_5_block28_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_139[0][0]'] \n", - " \n", - " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n", - " 'stack_5_block29_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_141[0][0]'] \n", - " \n", - " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n", - " 'stack_5_block30_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n", - " Conv2D) \n", - " \n", - " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n", - " tchNormalization) 0][0]'] \n", - " \n", - " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n", - " (Activation) [0]'] \n", - " \n", - " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n", - " iseConv2D) [0][0]'] \n", - " \n", - " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n", - " hNormalization) ] \n", - " \n", - " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n", - " ctivation) ]'] \n", - " \n", - " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " mbda) ][0]'] \n", - " \n", - " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n", - " v2D) ] \n", - " \n", - " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n", - " 0]'] \n", - " \n", - " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n", - " v2D) \n", - " \n", - " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n", - " 0]'] \n", - " \n", - " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", - " ][0]', \n", - " 'activation_143[0][0]'] \n", - " \n", - " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n", - " nv2D) \n", - " \n", - " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n", - " hNormalization) [0]'] \n", - " \n", - " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n", - " 'stack_5_block31_MB_pw_bn[0][0 \n", - " ]'] \n", - " \n", - " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_145[0][0]'] \n", - " \n", - " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " onv2D) '] \n", - " \n", - " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_147[0][0]'] \n", - " \n", - " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n", - " ', \n", - " 'stack_6_block1_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_149[0][0]'] \n", - " \n", - " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n", - " 'stack_6_block2_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_151[0][0]'] \n", - " \n", - " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n", - " 'stack_6_block3_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_153[0][0]'] \n", - " \n", - " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n", - " 'stack_6_block4_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_155[0][0]'] \n", - " \n", - " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n", - " 'stack_6_block5_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_157[0][0]'] \n", - " \n", - " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n", - " 'stack_6_block6_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n", - " onv2D) \n", - " \n", - " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n", - " chNormalization) ][0]'] \n", - " \n", - " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n", - " Activation) 0]'] \n", - " \n", - " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n", - " seConv2D) 0][0]'] \n", - " \n", - " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n", - " Normalization) \n", - " \n", - " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n", - " tivation) '] \n", - " \n", - " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " mbda) [0]'] \n", - " \n", - " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n", - " 2D) ] \n", - " \n", - " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n", - " ]'] \n", - " \n", - " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n", - " 2D) \n", - " \n", - " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n", - " ]'] \n", - " \n", - " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", - " [0]', \n", - " 'activation_159[0][0]'] \n", - " \n", - " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n", - " v2D) \n", - " \n", - " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n", - " Normalization) 0]'] \n", - " \n", - " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n", - " 'stack_6_block7_MB_pw_bn[0][0] \n", - " '] \n", - " \n", - " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n", - " \n", - " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", - " \n", - " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", - " \n", - " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", - " 2D) \n", - " \n", - " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", - " \n", - " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 207,618,394\n", - "Trainable params: 206,841,370\n", - "Non-trainable params: 777,024\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from keras_efficientnet_v2 import EfficientNetV2XL\n", - "\n", - "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n", - "# define new model\n", - "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", - "\n", - "# compile model\n", - "opt = SGD(momentum=0.9)\n", - "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n", - "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "# opt = Adam()\n", - "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "freeze_layers = 0\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from efficientnet.keras import EfficientNetL2 as KENBL2\n", - "#FUNC\n", - "def Eff_B7_NS(freeze_layers):\n", - " base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n", - " weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n", - " include_top=False,\n", - " drop_connect_rate=0)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n", - " print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", - " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) \n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return model_EfficientNetB7_NS\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B7_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta2" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T02:31:32.994176700Z", - "start_time": "2023-12-28T02:31:27.381088600Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 817\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " conv2d (Conv2D) (None, 7, 7, 64) 163904 ['top_activation[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 64) 0 ['conv2d[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 33280 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 256) 131328 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " batch_normalization_2 (BatchNo (None, 256) 1024 ['dense_2[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_3 (Dense) (None, 128) 32896 ['batch_normalization_2[0][0]'] Y \n", - " \n", - " dense_4 (Dense) (None, 2) 258 ['dense_3[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 64,727,122\n", - "Trainable params: 64,413,842\n", - "Non-trainable params: 313,280\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from efficientnet.keras import EfficientNetB7 as KENB7\n", - "# FUNC\n", - "def Eff_B7_NS(freeze_layers):\n", - " base_model = KENB7(input_shape=(\n", - " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) /\n", - " len(base_model.layers)) * 100\n", - " print(\n", - " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " # base\n", - " base_model_FT_Conv = Conv2D(64, (1, 1))(base_model.output)\n", - " base_model_FT = GlobalAveragePooling2D()(base_model_FT_Conv)\n", - " # L1\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", - " # L2\n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " # L3\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(256, activation='relu')(BatchNorm_L3)\n", - " # L3\n", - " BatchNorm_L4 = BatchNormalization()(Dense_L3)\n", - " Dense_L4 = Dense(128, activation='relu')(BatchNorm_L4)\n", - " # L(end)\n", - " # predictions = Dense(2, activation='softmax')(Dense_L4) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L4)\n", - "\n", - " model_EfficientNetB7_NS = Model(\n", - " inputs=base_model.input, outputs=predictions)\n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " # OPT/compile\n", - " opt = SGD(momentum=0.9, nesterov=False)\n", - " # opt = Nadam()\n", - " # opt = Adamax()\n", - " # opt = RMSprop(momentum=0.9)\n", - " # opt = Adagrad()\n", - " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_B7_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta3" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total model layers: 11\n", - "Model: \"model\"\n", - "____________________________________________________________________________\n", - " Layer (type) Output Shape Param # Trainable \n", - "============================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3)] 0 Y \n", - " \n", - " lambda (Lambda) (None, 224, 224, 3) 0 Y \n", - " \n", - " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n", - " ) 8 \n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n", - "| |\n", - "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n", - "| malization (Normalization) |\n", - "| |\n", - "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n", - "| ntial) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n", - "|| Conv2D) ||\n", - "|| ||\n", - "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n", - "|| orm (LayerNormalization) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n", - "| ambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n", - "| g_block_0 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n", - "|| g_layernorm_0 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n", - "|| g_conv_0 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n", - "| g_block_1 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n", - "|| g_layernorm_1 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n", - "|| g_conv_1 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_3_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_3_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_3_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_3_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_3_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_3_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_3_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n", - "| pLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_4_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_4_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_4_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_4_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_4_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_4_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_4_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_5_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_5_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_5_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_5_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_5_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_5_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_5_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_6_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_6_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_6_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_6_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_6_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_6_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_6_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_7_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_7_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_7_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_7_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_7_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_7_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_7_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_8_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_8_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_8_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_8_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_8_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_8_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_8_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_9_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_9_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_9_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_9_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_9_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_9_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_9_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_10_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_10_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_10_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_10_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_10_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_10_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_10_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_11_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_11_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_11_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_11_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_11_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_11_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_11_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_12_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_12_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_12_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_12_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_12_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_12_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_12_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_13_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_13_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_13_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_13_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_13_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_13_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_13_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_14_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_14_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_14_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_14_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_14_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_14_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_14_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_15_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_15_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_15_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_15_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_15_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_15_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_15_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_16_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_16_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_16_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_16_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_16_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_16_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_16_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_17_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_17_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_17_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_17_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_17_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_17_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_17_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_18_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_18_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_18_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_18_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_18_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_18_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_18_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_19_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_19_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_19_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_19_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_19_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_19_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_19_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_20_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_20_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_20_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_20_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_20_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_20_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_20_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_21_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_21_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_21_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_21_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_21_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_21_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_21_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_22_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_22_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_22_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_22_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_22_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_22_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_22_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_23_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_23_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_23_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_23_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_23_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_23_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_23_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_24_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_24_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_24_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_24_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_24_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_24_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_24_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_25_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_25_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_25_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_25_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_25_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_25_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_25_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", - "| ck_26_depthwise_conv (Conv2 |\n", - "| D) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", - "| ck_26_layernorm (LayerNorma |\n", - "| lization) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", - "| ck_26_pointwise_conv_1 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", - "| ck_26_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", - "| ck_26_pointwise_conv_2 (Den |\n", - "| se) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", - "| ck_26_layer_scale (LayerSca |\n", - "| le) |\n", - "| |\n", - "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", - "| ck_26_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n", - "| g_block_2 (Sequential) |\n", - "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n", - "|| g_layernorm_2 (LayerNormali ||\n", - "|| zation) ||\n", - "|| ||\n", - "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n", - "|| g_conv_2 (Conv2D) ||\n", - "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_0_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_0_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_0_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_0_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_0_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_0_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_0_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_1_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_1_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_1_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_1_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_1_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_1_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_1_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", - "| ck_2_depthwise_conv (Conv2D |\n", - "| ) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", - "| ck_2_layernorm (LayerNormal |\n", - "| ization) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", - "| ck_2_pointwise_conv_1 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", - "| ck_2_gelu (Activation) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", - "| ck_2_pointwise_conv_2 (Dens |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", - "| ck_2_layer_scale (LayerScal |\n", - "| e) |\n", - "| |\n", - "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", - "| ck_2_identity (Activation) |\n", - "| |\n", - "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n", - "| OpLambda) |\n", - "| |\n", - "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n", - "| ormalization) |\n", - "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", - " global_average_pooling2d (G (None, 2048) 0 Y \n", - " lobalAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1049088 Y \n", - " \n", - " dropout (Dropout) (None, 512) 0 Y \n", - " \n", - " batch_normalization (BatchN (None, 512) 2048 Y \n", - " ormalization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 Y \n", - " \n", - " batch_normalization_1 (Batc (None, 512) 2048 Y \n", - " hNormalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 Y \n", - " \n", - "============================================================================\n", - "Total params: 349,529,730\n", - "Trainable params: 349,527,682\n", - "Non-trainable params: 2,048\n", - "____________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from keras.applications import ConvNeXtXLarge\n", - "from keras.layers import Lambda\n", - "#FUNC\n", - "def Eff_B7_NS():\n", - " # Add a Lambda layer at the beginning to scale the input\n", - " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", - " x = Lambda(lambda image: image * 255)(input)\n", - " \n", - " base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x)\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model)\n", - " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) \n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " #OPT/compile\n", - " opt = SGD(momentum=0.9)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "model = Eff_B7_NS()\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### V(T) Beta4" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating the model...\n", - "Total layers in the base model: 806\n", - "Freezing 0 layers in the base model...\n", - "Percentage of the base model that is frozen: 0.00%\n", - "Total model layers: 814\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "from efficientnet.keras import EfficientNetB7 as EFK_GN\n", - "# FUNC\n", - "def Eff_BS_NS(freeze_layers):\n", - " base_model = EFK_GN(input_shape=(\n", - " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", - " print('Total layers in the base model: ', len(base_model.layers))\n", - " print(f'Freezing {freeze_layers} layers in the base model...')\n", - " # Freeze the specified number of layers\n", - " for layer in base_model.layers[:freeze_layers]:\n", - " layer.trainable = False\n", - "\n", - " # Unfreeze the rest\n", - " for layer in base_model.layers[freeze_layers:]:\n", - " layer.trainable = True\n", - "\n", - " # Calculate the percentage of the model that is frozen\n", - " frozen_percentage = ((freeze_layers + 1e-10) /\n", - " len(base_model.layers)) * 100\n", - " print(\n", - " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", - " # adding CDL\n", - " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", - " Dense_L1 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.02))(base_model_FT)\n", - " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", - " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", - " Dense_L2 = Dense(512, activation='relu',\n", - " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", - " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", - " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", - " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", - " predictions = Dense(2, activation='softmax')(Dense_L3)\n", - "\n", - " model_EfficientNetB7_NS = Model(\n", - " inputs=base_model.input, outputs=predictions)\n", - " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", - " # OPT/compile\n", - " opt = SGD(momentum=0.9, nesterov=False)\n", - " # opt = Nadam()\n", - " # opt = Adamax()\n", - " # opt = RMSprop(momentum=0.9)\n", - " # opt = Adagrad()\n", - " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", - " # opt = Yogi()\n", - " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", - "\n", - " return model_EfficientNetB7_NS\n", - "\n", - "print('Creating the model...')\n", - "# Main\n", - "freeze_layers = 0\n", - "model = Eff_BS_NS(freeze_layers)\n", - "model.summary(show_trainable=True, expand_nested=True)\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LR FINDER" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "#CONF/Other\n", - "LRF_OPT = SGD(momentum=0.9)\n", - "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", - "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", - "# Instantiate LrFinder\n", - "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", - "\n", - "# Start range_test\n", - "lr_find.range_test(LRF_dataset)\n", - "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model vis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dot_img_file = 'model_1.png'\n", - "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the full model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92mLoading model done.\n", - "Compiling the AI model...\u001b[0m\n", - "Model: \"model\"\n", - "_____________________________________________________________________________________________________________\n", - " Layer (type) Output Shape Param # Connected to Trainable \n", - "=============================================================================================================\n", - " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", - " )] \n", - " \n", - " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", - " ) \n", - " \n", - " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", - " ) \n", - " \n", - " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", - " \n", - " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", - " \n", - " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", - " \n", - " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", - " ) 'block1a_se_expand[0][0]'] \n", - " \n", - " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", - " \n", - " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", - " \n", - " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", - " \n", - " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", - " ) 'block1b_se_expand[0][0]'] \n", - " \n", - " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", - " ) 'block1a_project_bn[0][0]'] \n", - " \n", - " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", - " \n", - " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", - " \n", - " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", - " \n", - " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", - " ) 'block1c_se_expand[0][0]'] \n", - " \n", - " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", - " ) 'block1b_add[0][0]'] \n", - " \n", - " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", - " D) ) \n", - " \n", - " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", - " \n", - " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", - " \n", - " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", - " \n", - " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", - " ) 'block1d_se_expand[0][0]'] \n", - " \n", - " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", - " lization) ) \n", - " \n", - " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", - " ) 'block1c_add[0][0]'] \n", - " \n", - " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", - " 2) \n", - " \n", - " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", - " ization) 2) \n", - " \n", - " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", - " ivation) 2) \n", - " \n", - " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", - " \n", - " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", - " \n", - " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", - " \n", - " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", - " 'block2a_se_expand[0][0]'] \n", - " \n", - " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", - " \n", - " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", - " \n", - " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", - " \n", - " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", - " \n", - " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", - " \n", - " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", - " 'block2b_se_expand[0][0]'] \n", - " \n", - " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", - " \n", - " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", - " \n", - " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", - " 'block2a_project_bn[0][0]'] \n", - " \n", - " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", - " \n", - " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", - " \n", - " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", - " \n", - " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", - " \n", - " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", - " 'block2c_se_expand[0][0]'] \n", - " \n", - " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", - " \n", - " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", - " \n", - " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", - " 'block2b_add[0][0]'] \n", - " \n", - " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", - " \n", - " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", - " \n", - " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", - " \n", - " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", - " \n", - " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", - " 'block2d_se_expand[0][0]'] \n", - " \n", - " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", - " \n", - " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", - " \n", - " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", - " 'block2c_add[0][0]'] \n", - " \n", - " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", - " \n", - " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", - " \n", - " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", - " \n", - " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", - " \n", - " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", - " 'block2e_se_expand[0][0]'] \n", - " \n", - " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", - " \n", - " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", - " \n", - " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", - " 'block2d_add[0][0]'] \n", - " \n", - " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", - " \n", - " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", - " \n", - " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", - " \n", - " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", - " \n", - " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", - " 'block2f_se_expand[0][0]'] \n", - " \n", - " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", - " \n", - " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", - " \n", - " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", - " 'block2e_add[0][0]'] \n", - " \n", - " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", - " \n", - " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", - " \n", - " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", - " \n", - " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", - " \n", - " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", - " 'block2g_se_expand[0][0]'] \n", - " \n", - " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", - " \n", - " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", - " \n", - " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", - " 'block2f_add[0][0]'] \n", - " \n", - " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", - " \n", - " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", - " \n", - " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", - " \n", - " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", - " \n", - " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", - " 'block3a_se_expand[0][0]'] \n", - " \n", - " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", - " \n", - " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", - " \n", - " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", - " \n", - " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", - " \n", - " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", - " \n", - " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", - " 'block3b_se_expand[0][0]'] \n", - " \n", - " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", - " \n", - " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", - " \n", - " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", - " 'block3a_project_bn[0][0]'] \n", - " \n", - " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", - " \n", - " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", - " \n", - " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", - " \n", - " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", - " \n", - " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", - " 'block3c_se_expand[0][0]'] \n", - " \n", - " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", - " \n", - " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", - " \n", - " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", - " 'block3b_add[0][0]'] \n", - " \n", - " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", - " \n", - " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", - " \n", - " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", - " \n", - " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", - " \n", - " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", - " 'block3d_se_expand[0][0]'] \n", - " \n", - " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", - " \n", - " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", - " \n", - " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", - " 'block3c_add[0][0]'] \n", - " \n", - " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", - " \n", - " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", - " \n", - " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", - " \n", - " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", - " \n", - " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", - " 'block3e_se_expand[0][0]'] \n", - " \n", - " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", - " \n", - " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", - " \n", - " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", - " 'block3d_add[0][0]'] \n", - " \n", - " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", - " \n", - " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", - " \n", - " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", - " \n", - " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", - " \n", - " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", - " 'block3f_se_expand[0][0]'] \n", - " \n", - " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", - " \n", - " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", - " \n", - " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", - " 'block3e_add[0][0]'] \n", - " \n", - " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", - " \n", - " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", - " \n", - " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", - " \n", - " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", - " \n", - " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", - " 'block3g_se_expand[0][0]'] \n", - " \n", - " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", - " \n", - " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", - " \n", - " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", - " 'block3f_add[0][0]'] \n", - " \n", - " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", - " \n", - " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", - " \n", - " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", - " \n", - " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", - " \n", - " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", - " 'block4a_se_expand[0][0]'] \n", - " \n", - " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", - " \n", - " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", - " \n", - " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", - " \n", - " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", - " \n", - " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", - " \n", - " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", - " 'block4b_se_expand[0][0]'] \n", - " \n", - " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", - " \n", - " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", - " \n", - " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", - " 'block4a_project_bn[0][0]'] \n", - " \n", - " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", - " \n", - " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", - " \n", - " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", - " \n", - " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", - " \n", - " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", - " 'block4c_se_expand[0][0]'] \n", - " \n", - " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", - " \n", - " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", - " \n", - " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", - " 'block4b_add[0][0]'] \n", - " \n", - " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", - " \n", - " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", - " \n", - " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", - " \n", - " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", - " \n", - " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", - " 'block4d_se_expand[0][0]'] \n", - " \n", - " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", - " \n", - " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", - " \n", - " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", - " 'block4c_add[0][0]'] \n", - " \n", - " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", - " \n", - " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", - " \n", - " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", - " \n", - " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", - " \n", - " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", - " 'block4e_se_expand[0][0]'] \n", - " \n", - " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", - " \n", - " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", - " \n", - " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", - " 'block4d_add[0][0]'] \n", - " \n", - " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", - " \n", - " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", - " \n", - " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", - " \n", - " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", - " \n", - " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", - " 'block4f_se_expand[0][0]'] \n", - " \n", - " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", - " \n", - " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", - " \n", - " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", - " 'block4e_add[0][0]'] \n", - " \n", - " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", - " \n", - " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", - " \n", - " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", - " \n", - " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", - " \n", - " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", - " 'block4g_se_expand[0][0]'] \n", - " \n", - " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", - " \n", - " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", - " \n", - " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", - " 'block4f_add[0][0]'] \n", - " \n", - " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", - " \n", - " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", - " \n", - " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", - " \n", - " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", - " \n", - " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", - " 'block4h_se_expand[0][0]'] \n", - " \n", - " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", - " \n", - " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", - " \n", - " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", - " 'block4g_add[0][0]'] \n", - " \n", - " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", - " \n", - " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", - " \n", - " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", - " \n", - " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", - " \n", - " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", - " 'block4i_se_expand[0][0]'] \n", - " \n", - " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", - " \n", - " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", - " \n", - " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", - " 'block4h_add[0][0]'] \n", - " \n", - " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", - " \n", - " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", - " \n", - " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", - " \n", - " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", - " \n", - " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", - " 'block4j_se_expand[0][0]'] \n", - " \n", - " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", - " \n", - " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", - " \n", - " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", - " 'block4i_add[0][0]'] \n", - " \n", - " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", - " \n", - " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", - " \n", - " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", - " \n", - " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", - " \n", - " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", - " 'block5a_se_expand[0][0]'] \n", - " \n", - " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", - " \n", - " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", - " \n", - " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", - " \n", - " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", - " \n", - " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", - " ) 'block5b_se_expand[0][0]'] \n", - " \n", - " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", - " \n", - " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", - " \n", - " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", - " 'block5a_project_bn[0][0]'] \n", - " \n", - " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", - " \n", - " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", - " \n", - " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", - " \n", - " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", - " ) 'block5c_se_expand[0][0]'] \n", - " \n", - " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", - " \n", - " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", - " \n", - " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", - " 'block5b_add[0][0]'] \n", - " \n", - " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", - " \n", - " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", - " \n", - " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", - " \n", - " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", - " ) 'block5d_se_expand[0][0]'] \n", - " \n", - " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", - " \n", - " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", - " \n", - " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", - " 'block5c_add[0][0]'] \n", - " \n", - " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", - " \n", - " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", - " \n", - " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", - " \n", - " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", - " ) 'block5e_se_expand[0][0]'] \n", - " \n", - " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", - " \n", - " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", - " \n", - " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", - " 'block5d_add[0][0]'] \n", - " \n", - " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", - " \n", - " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", - " \n", - " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", - " \n", - " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", - " ) 'block5f_se_expand[0][0]'] \n", - " \n", - " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", - " \n", - " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", - " \n", - " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", - " 'block5e_add[0][0]'] \n", - " \n", - " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", - " \n", - " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", - " \n", - " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", - " \n", - " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", - " ) 'block5g_se_expand[0][0]'] \n", - " \n", - " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", - " \n", - " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", - " \n", - " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", - " 'block5f_add[0][0]'] \n", - " \n", - " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", - " \n", - " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", - " \n", - " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", - " \n", - " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", - " ) 'block5h_se_expand[0][0]'] \n", - " \n", - " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", - " \n", - " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", - " \n", - " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", - " 'block5g_add[0][0]'] \n", - " \n", - " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", - " \n", - " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", - " \n", - " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", - " \n", - " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", - " ) 'block5i_se_expand[0][0]'] \n", - " \n", - " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", - " \n", - " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", - " \n", - " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", - " 'block5h_add[0][0]'] \n", - " \n", - " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", - " ) \n", - " \n", - " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", - " D) ) 0]'] \n", - " \n", - " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", - " ) ) \n", - " \n", - " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", - " \n", - " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", - " \n", - " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", - " \n", - " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", - " ) 'block5j_se_expand[0][0]'] \n", - " \n", - " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", - " \n", - " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", - " \n", - " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", - " 'block5i_add[0][0]'] \n", - " \n", - " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", - " ization) ) \n", - " \n", - " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", - " ivation) ) \n", - " \n", - " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", - " \n", - " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", - " \n", - " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", - " \n", - " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", - " 'block6a_se_expand[0][0]'] \n", - " \n", - " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", - " \n", - " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", - " \n", - " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", - " \n", - " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", - " \n", - " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", - " \n", - " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", - " 'block6b_se_expand[0][0]'] \n", - " \n", - " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", - " \n", - " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", - " \n", - " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", - " 'block6a_project_bn[0][0]'] \n", - " \n", - " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", - " \n", - " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", - " \n", - " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", - " \n", - " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", - " \n", - " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", - " 'block6c_se_expand[0][0]'] \n", - " \n", - " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", - " \n", - " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", - " \n", - " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", - " 'block6b_add[0][0]'] \n", - " \n", - " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", - " \n", - " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", - " \n", - " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", - " \n", - " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", - " \n", - " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", - " 'block6d_se_expand[0][0]'] \n", - " \n", - " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", - " \n", - " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", - " \n", - " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", - " 'block6c_add[0][0]'] \n", - " \n", - " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", - " \n", - " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", - " \n", - " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", - " \n", - " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", - " \n", - " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", - " 'block6e_se_expand[0][0]'] \n", - " \n", - " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", - " \n", - " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", - " \n", - " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", - " 'block6d_add[0][0]'] \n", - " \n", - " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", - " \n", - " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", - " \n", - " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", - " \n", - " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", - " \n", - " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", - " 'block6f_se_expand[0][0]'] \n", - " \n", - " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", - " \n", - " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", - " \n", - " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", - " 'block6e_add[0][0]'] \n", - " \n", - " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", - " \n", - " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", - " \n", - " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", - " \n", - " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", - " \n", - " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", - " 'block6g_se_expand[0][0]'] \n", - " \n", - " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", - " \n", - " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", - " \n", - " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", - " 'block6f_add[0][0]'] \n", - " \n", - " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", - " \n", - " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", - " \n", - " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", - " \n", - " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", - " \n", - " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", - " 'block6h_se_expand[0][0]'] \n", - " \n", - " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", - " \n", - " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", - " \n", - " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", - " 'block6g_add[0][0]'] \n", - " \n", - " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", - " \n", - " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", - " \n", - " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", - " \n", - " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", - " \n", - " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", - " 'block6i_se_expand[0][0]'] \n", - " \n", - " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", - " \n", - " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", - " \n", - " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", - " 'block6h_add[0][0]'] \n", - " \n", - " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", - " \n", - " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", - " \n", - " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", - " \n", - " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", - " \n", - " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", - " 'block6j_se_expand[0][0]'] \n", - " \n", - " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", - " \n", - " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", - " \n", - " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", - " 'block6i_add[0][0]'] \n", - " \n", - " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", - " \n", - " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", - " \n", - " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", - " \n", - " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", - " \n", - " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", - " 'block6k_se_expand[0][0]'] \n", - " \n", - " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", - " \n", - " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", - " \n", - " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", - " 'block6j_add[0][0]'] \n", - " \n", - " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", - " \n", - " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", - " \n", - " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", - " \n", - " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", - " \n", - " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", - " 'block6l_se_expand[0][0]'] \n", - " \n", - " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", - " \n", - " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", - " \n", - " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", - " 'block6k_add[0][0]'] \n", - " \n", - " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", - " \n", - " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", - " \n", - " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", - " \n", - " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", - " \n", - " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", - " 'block6m_se_expand[0][0]'] \n", - " \n", - " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", - " \n", - " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", - " \n", - " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", - " 'block6l_add[0][0]'] \n", - " \n", - " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", - " \n", - " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", - " \n", - " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", - " \n", - " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", - " \n", - " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", - " 'block7a_se_expand[0][0]'] \n", - " \n", - " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", - " \n", - " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", - " \n", - " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", - " \n", - " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", - " \n", - " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", - " \n", - " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", - " 'block7b_se_expand[0][0]'] \n", - " \n", - " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", - " \n", - " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", - " \n", - " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", - " 'block7a_project_bn[0][0]'] \n", - " \n", - " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", - " \n", - " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", - " \n", - " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", - " \n", - " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", - " \n", - " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", - " 'block7c_se_expand[0][0]'] \n", - " \n", - " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", - " \n", - " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", - " \n", - " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", - " 'block7b_add[0][0]'] \n", - " \n", - " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", - " \n", - " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", - " ization) \n", - " \n", - " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", - " ivation) \n", - " \n", - " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", - " D) 0]'] \n", - " \n", - " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", - " ) \n", - " \n", - " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", - " agePooling2D) \n", - " \n", - " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", - " \n", - " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", - " \n", - " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", - " \n", - " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", - " 'block7d_se_expand[0][0]'] \n", - " \n", - " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", - " \n", - " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", - " lization) \n", - " \n", - " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", - " \n", - " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", - " 'block7c_add[0][0]'] \n", - " \n", - " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", - " \n", - " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", - " \n", - " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", - " \n", - " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", - " alAveragePooling2D) \n", - " \n", - " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", - " ]'] \n", - " \n", - " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", - " \n", - " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", - " alization) \n", - " \n", - " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", - " \n", - " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", - " rmalization) \n", - " \n", - " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", - " \n", - " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", - " \n", - "=============================================================================================================\n", - "Total params: 65,741,586\n", - "Trainable params: 65,428,818\n", - "Non-trainable params: 312,768\n", - "_____________________________________________________________________________________________________________\n", - "done.\n" - ] - } - ], - "source": [ - "import efficientnet.tfkeras\n", - "# Configuration\n", - "PRMC = False\n", - "freeze_from_opposite = False\n", - "Extra_EXT = '_T'\n", - "freeze_layers = 0 \n", - "randomly_frozen_layers = 0 \n", - "freeze_last_seven = False \n", - "# CEC_opt = Adagrad()\n", - "# CEC_opt = Yogi()\n", - "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", - "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", - "# CEC_opt = Adam()\n", - "# Main\n", - "try:\n", - " if SAVE_TYPE == 'TF':\n", - " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", - " else:\n", - " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", - "except (ImportError, IOError) as e:\n", - " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", - "else:\n", - " print('\\033[92mLoading model done.')\n", - " if not PRMC:\n", - " print('Compiling the AI model...\\033[0m')\n", - " \n", - " for layer in model.layers:\n", - " layer.trainable = True\n", - " \n", - " # Select random layers to freeze\n", - " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", - " \n", - " for i, layer in enumerate(model.layers):\n", - " if i in frozen_layer_indices:\n", - " layer.trainable = False\n", - " else:\n", - " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", - " layer.trainable = False\n", - " elif (not freeze_from_opposite) and i < freeze_layers:\n", - " layer.trainable = False\n", - " else:\n", - " layer.trainable = True\n", - " \n", - " for layer in model.layers[-7:]:\n", - " layer.trainable = not freeze_last_seven\n", - " \n", - " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", - " model.summary(show_trainable=True, expand_nested=True)\n", - " print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading model weights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.load_weights('PAI_model_weights.h5')\n", - "print('done.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reset FC" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "for layer in model.layers[-7:]:\n", - " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", - " weight_initializer = layer.kernel_initializer\n", - " bias_initializer = layer.bias_initializer\n", - "\n", - " old_weights, old_biases = layer.get_weights()\n", - "\n", - " layer.set_weights([\n", - " weight_initializer(shape=old_weights.shape),\n", - " bias_initializer(shape=len(old_biases))\n", - " ])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rev2 (THE BEST)\n", - "```\n", - "Working: βœ…\n", - "Other:\n", - " + Tensorboard works.\n", - " + Perverts overfitting.\n", - " + Lower memory usage.\n", - " - Slow training.\n", - " + Achieving higher acc.\n", - " - Some models dont work.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T07:04:23.573633300Z", - "start_time": "2023-12-28T02:31:32.468641900Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training the model...\n", - "\u001b[0;33m\n", - "Setup Verbose:\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d01-h22_m20_s51]\u001b[0m\u001b[0;36m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", - "\u001b[0;33mSetup Verbose END.\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", - "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d01-h22_m25_s57\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 1/6\n", - " 83/512 [===>..........................] - ETA: 1:14 - loss: 22.1201 - accuracy: 0.9096\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 162s 287ms/step - loss: 16.1137 - accuracy: 0.9294 - val_loss: 7.3855 - val_accuracy: 0.9391\n", - "Epoch 2/6\n", - "512/512 [==============================] - 81s 159ms/step - loss: 3.2671 - accuracy: 0.9207 - val_loss: 1.1589 - val_accuracy: 0.9567\n", - "Epoch 3/6\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.6977 - accuracy: 0.9285 - val_loss: 0.3492 - val_accuracy: 0.9487\n", - "Epoch 4/6\n", - "173/512 [=========>....................] - ETA: 48s - loss: 0.3754 - accuracy: 0.9400\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 141s 276ms/step - loss: 0.3473 - accuracy: 0.9375 - val_loss: 0.3538 - val_accuracy: 0.9359\n", - "Epoch 5/6\n", - "357/512 [===================>..........] - ETA: 22s - loss: 0.2294 - accuracy: 0.9534\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 142s 279ms/step - loss: 0.2169 - accuracy: 0.9573 - val_loss: 0.3157 - val_accuracy: 0.9247\n", - "Epoch 6/6\n", - "361/512 [====================>.........] - ETA: 21s - loss: 0.1484 - accuracy: 0.9768\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 142s 278ms/step - loss: 0.1411 - accuracy: 0.9778 - val_loss: 0.3112 - val_accuracy: 0.9327\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-002-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.1589\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9567307829856873\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m1.15892493724823\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m1075.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m750.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m324.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 7/12\n", - "512/512 [==============================] - 87s 163ms/step - loss: 0.9661 - accuracy: 0.9292 - val_loss: 0.6673 - val_accuracy: 0.9247\n", - "Epoch 8/12\n", - "387/512 [=====================>........] - ETA: 17s - loss: 0.4921 - accuracy: 0.9335\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 143s 279ms/step - loss: 0.4738 - accuracy: 0.9272 - val_loss: 0.3640 - val_accuracy: 0.9439\n", - "Epoch 9/12\n", - "487/512 [===========================>..] - ETA: 3s - loss: 0.3143 - accuracy: 0.9302\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 142s 277ms/step - loss: 0.3151 - accuracy: 0.9304 - val_loss: 0.2449 - val_accuracy: 0.9391\n", - "Epoch 10/12\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2833 - accuracy: 0.9417 - val_loss: 0.4342 - val_accuracy: 0.7885\n", - "Epoch 11/12\n", - "177/512 [=========>....................] - ETA: 47s - loss: 0.2635 - accuracy: 0.9456\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 142s 278ms/step - loss: 0.2145 - accuracy: 0.9580 - val_loss: 0.2247 - val_accuracy: 0.9311\n", - "Epoch 12/12\n", - "229/512 [============>.................] - ETA: 39s - loss: 0.1506 - accuracy: 0.9678\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 142s 277ms/step - loss: 0.1272 - accuracy: 0.9727 - val_loss: 0.2158 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-008-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3640\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m1.15892493724823 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.36395618319511414\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m809.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m738.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 13/18\n", - "512/512 [==============================] - 87s 164ms/step - loss: 0.3633 - accuracy: 0.9158 - val_loss: 0.4034 - val_accuracy: 0.9087\n", - "Epoch 14/18\n", - "453/512 [=========================>....] - ETA: 8s - loss: 0.4066 - accuracy: 0.8949\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 141s 275ms/step - loss: 0.4076 - accuracy: 0.8923 - val_loss: 0.4474 - val_accuracy: 0.7869\n", - "Epoch 15/18\n", - "349/512 [===================>..........] - ETA: 23s - loss: 0.3081 - accuracy: 0.9208\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 141s 276ms/step - loss: 0.3111 - accuracy: 0.9231 - val_loss: 0.2624 - val_accuracy: 0.9263\n", - "Epoch 16/18\n", - "479/512 [===========================>..] - ETA: 4s - loss: 0.2480 - accuracy: 0.9423\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 143s 280ms/step - loss: 0.2471 - accuracy: 0.9431 - val_loss: 0.2339 - val_accuracy: 0.9455\n", - "Epoch 17/18\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2019 - accuracy: 0.9548 - val_loss: 0.1786 - val_accuracy: 0.9439\n", - "Epoch 18/18\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1278 - accuracy: 0.9753 - val_loss: 0.1961 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-018-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1961\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.36395618319511414 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.19614851474761963\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m750.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m678.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 19/24\n", - "512/512 [==============================] - 87s 163ms/step - loss: 0.2687 - accuracy: 0.9233 - val_loss: 0.2169 - val_accuracy: 0.9535\n", - "Epoch 20/24\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.3036 - accuracy: 0.9187 - val_loss: 0.6531 - val_accuracy: 0.7212\n", - "Epoch 21/24\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.3466 - accuracy: 0.9033 - val_loss: 0.3687 - val_accuracy: 0.8958\n", - "Epoch 22/24\n", - "512/512 [==============================] - 79s 154ms/step - loss: 0.2888 - accuracy: 0.9238 - val_loss: 0.2060 - val_accuracy: 0.9503\n", - "Epoch 23/24\n", - "512/512 [==============================] - 80s 157ms/step - loss: 0.1832 - accuracy: 0.9583 - val_loss: 0.1931 - val_accuracy: 0.9471\n", - "Epoch 24/24\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1262 - accuracy: 0.9729 - val_loss: 0.1719 - val_accuracy: 0.9567\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-024-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1719\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.19614851474761963 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.17186278104782104\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 25/30\n", - "512/512 [==============================] - 84s 157ms/step - loss: 0.2452 - accuracy: 0.9280 - val_loss: 0.2506 - val_accuracy: 0.9391\n", - "Epoch 26/30\n", - "512/512 [==============================] - 79s 154ms/step - loss: 0.3161 - accuracy: 0.9199 - val_loss: 0.2607 - val_accuracy: 0.9311\n", - "Epoch 27/30\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2898 - accuracy: 0.9287 - val_loss: 0.3127 - val_accuracy: 0.9183\n", - "Epoch 28/30\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2450 - accuracy: 0.9453 - val_loss: 0.2576 - val_accuracy: 0.9375\n", - "Epoch 29/30\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1752 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9359\n", - "Epoch 30/30\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1121 - accuracy: 0.9785 - val_loss: 0.3048 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-025-0.9391.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2506\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m560.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m490.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 31/36\n", - "512/512 [==============================] - 88s 163ms/step - loss: 0.2783 - accuracy: 0.9263 - val_loss: 0.2717 - val_accuracy: 0.9087\n", - "Epoch 32/36\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2766 - accuracy: 0.9309 - val_loss: 0.2198 - val_accuracy: 0.9455\n", - "Epoch 33/36\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.3029 - accuracy: 0.9172 - val_loss: 0.3857 - val_accuracy: 0.9375\n", - "Epoch 34/36\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2681 - accuracy: 0.9299 - val_loss: 0.2557 - val_accuracy: 0.9279\n", - "Epoch 35/36\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2308 - accuracy: 0.9453 - val_loss: 0.2211 - val_accuracy: 0.9487\n", - "Epoch 36/36\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.1555 - accuracy: 0.9663 - val_loss: 0.2236 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-035-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2211\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m581.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 37/42\n", - "512/512 [==============================] - 86s 162ms/step - loss: 0.2614 - accuracy: 0.9258 - val_loss: 0.2696 - val_accuracy: 0.9455\n", - "Epoch 38/42\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.3130 - accuracy: 0.9199 - val_loss: 0.2501 - val_accuracy: 0.9439\n", - "Epoch 39/42\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2948 - accuracy: 0.9226 - val_loss: 0.4022 - val_accuracy: 0.9215\n", - "Epoch 40/42\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2720 - accuracy: 0.9275 - val_loss: 0.2985 - val_accuracy: 0.9038\n", - "Epoch 41/42\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2256 - accuracy: 0.9458 - val_loss: 0.2789 - val_accuracy: 0.9279\n", - "Epoch 42/42\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1319 - accuracy: 0.9712 - val_loss: 0.3209 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2696\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m565.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m493.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 43/48\n", - "512/512 [==============================] - 87s 163ms/step - loss: 0.2547 - accuracy: 0.9297 - val_loss: 0.2331 - val_accuracy: 0.9455\n", - "Epoch 44/48\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2930 - accuracy: 0.9185 - val_loss: 0.2536 - val_accuracy: 0.9407\n", - "Epoch 45/48\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2826 - accuracy: 0.9258 - val_loss: 0.2272 - val_accuracy: 0.9471\n", - "Epoch 46/48\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2522 - accuracy: 0.9319 - val_loss: 0.1899 - val_accuracy: 0.9439\n", - "Epoch 47/48\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1652 - accuracy: 0.9658 - val_loss: 0.1954 - val_accuracy: 0.9407\n", - "Epoch 48/48\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1035 - accuracy: 0.9795 - val_loss: 0.1904 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-045-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2272\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m571.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 49/54\n", - "512/512 [==============================] - 87s 164ms/step - loss: 0.2935 - accuracy: 0.9138 - val_loss: 0.2586 - val_accuracy: 0.9295\n", - "Epoch 50/54\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.3354 - accuracy: 0.9172 - val_loss: 0.4145 - val_accuracy: 0.9231\n", - "Epoch 51/54\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2955 - accuracy: 0.9294 - val_loss: 0.4671 - val_accuracy: 0.8606\n", - "Epoch 52/54\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2894 - accuracy: 0.9314 - val_loss: 0.2932 - val_accuracy: 0.9535\n", - "Epoch 53/54\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1909 - accuracy: 0.9570 - val_loss: 0.2285 - val_accuracy: 0.9471\n", - "Epoch 54/54\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1304 - accuracy: 0.9736 - val_loss: 0.2813 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-052-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2932\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 55/60\n", - "512/512 [==============================] - 87s 163ms/step - loss: 0.3008 - accuracy: 0.9204 - val_loss: 0.3639 - val_accuracy: 0.9343\n", - "Epoch 56/60\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2970 - accuracy: 0.9231 - val_loss: 0.3717 - val_accuracy: 0.9087\n", - "Epoch 57/60\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2971 - accuracy: 0.9253 - val_loss: 0.2916 - val_accuracy: 0.9343\n", - "Epoch 58/60\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2197 - accuracy: 0.9434 - val_loss: 0.2453 - val_accuracy: 0.9311\n", - "Epoch 59/60\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1865 - accuracy: 0.9531 - val_loss: 0.2109 - val_accuracy: 0.9375\n", - "Epoch 60/60\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1095 - accuracy: 0.9778 - val_loss: 0.2099 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-060-0.9455.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2099\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 61/66\n", - "512/512 [==============================] - 88s 164ms/step - loss: 0.2355 - accuracy: 0.9336 - val_loss: 0.1893 - val_accuracy: 0.9471\n", - "Epoch 62/66\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2743 - accuracy: 0.9297 - val_loss: 0.2586 - val_accuracy: 0.9311\n", - "Epoch 63/66\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2302 - accuracy: 0.9446 - val_loss: 0.2575 - val_accuracy: 0.9375\n", - "Epoch 64/66\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1933 - accuracy: 0.9558 - val_loss: 0.3245 - val_accuracy: 0.9327\n", - "Epoch 65/66\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1570 - accuracy: 0.9714 - val_loss: 0.2169 - val_accuracy: 0.9423\n", - "Epoch 66/66\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1070 - accuracy: 0.9817 - val_loss: 0.2537 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1893\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 67/72\n", - "512/512 [==============================] - 86s 161ms/step - loss: 0.2957 - accuracy: 0.9087 - val_loss: 0.2250 - val_accuracy: 0.9439\n", - "Epoch 68/72\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.3246 - accuracy: 0.9163 - val_loss: 0.3025 - val_accuracy: 0.9343\n", - "Epoch 69/72\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2869 - accuracy: 0.9268 - val_loss: 0.3096 - val_accuracy: 0.9151\n", - "Epoch 70/72\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2740 - accuracy: 0.9292 - val_loss: 0.2916 - val_accuracy: 0.9375\n", - "Epoch 71/72\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.2295 - accuracy: 0.9434 - val_loss: 0.2162 - val_accuracy: 0.9407\n", - "Epoch 72/72\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.1465 - accuracy: 0.9673 - val_loss: 0.2612 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-067-0.9439.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2250\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m502.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 73/78\n", - "512/512 [==============================] - 86s 161ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.3760 - val_accuracy: 0.9359\n", - "Epoch 74/78\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2922 - accuracy: 0.9209 - val_loss: 0.2300 - val_accuracy: 0.9455\n", - "Epoch 75/78\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2253 - accuracy: 0.9451 - val_loss: 0.2119 - val_accuracy: 0.9471\n", - "Epoch 76/78\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2234 - accuracy: 0.9397 - val_loss: 0.3418 - val_accuracy: 0.9407\n", - "Epoch 77/78\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1615 - accuracy: 0.9668 - val_loss: 0.2172 - val_accuracy: 0.9519\n", - "Epoch 78/78\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.1225 - accuracy: 0.9756 - val_loss: 0.1930 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-077-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2172\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m582.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m496.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 79/84\n", - "512/512 [==============================] - 86s 162ms/step - loss: 0.2487 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9519\n", - "Epoch 80/84\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2756 - accuracy: 0.9297 - val_loss: 0.3146 - val_accuracy: 0.8702\n", - "Epoch 81/84\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2448 - accuracy: 0.9370 - val_loss: 0.3448 - val_accuracy: 0.9359\n", - "Epoch 82/84\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2395 - accuracy: 0.9399 - val_loss: 0.3315 - val_accuracy: 0.9263\n", - "Epoch 83/84\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.1599 - accuracy: 0.9663 - val_loss: 0.3752 - val_accuracy: 0.9215\n", - "Epoch 84/84\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1241 - accuracy: 0.9734 - val_loss: 0.3453 - val_accuracy: 0.9295\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1886\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m491.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 85/90\n", - "512/512 [==============================] - 86s 161ms/step - loss: 0.2453 - accuracy: 0.9277 - val_loss: 0.2153 - val_accuracy: 0.9455\n", - "Epoch 86/90\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.3330 - accuracy: 0.9121 - val_loss: 0.2543 - val_accuracy: 0.9391\n", - "Epoch 87/90\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2985 - accuracy: 0.9258 - val_loss: 0.2262 - val_accuracy: 0.9455\n", - "Epoch 88/90\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2552 - accuracy: 0.9348 - val_loss: 0.2511 - val_accuracy: 0.9487\n", - "Epoch 89/90\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2027 - accuracy: 0.9556 - val_loss: 0.2189 - val_accuracy: 0.9439\n", - "Epoch 90/90\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1466 - accuracy: 0.9678 - val_loss: 0.2318 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9503.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2318\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 91/96\n", - "512/512 [==============================] - 86s 162ms/step - loss: 0.2495 - accuracy: 0.9312 - val_loss: 0.2295 - val_accuracy: 0.9535\n", - "Epoch 92/96\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2776 - accuracy: 0.9265 - val_loss: 0.3416 - val_accuracy: 0.8734\n", - "Epoch 93/96\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2656 - accuracy: 0.9316 - val_loss: 0.2431 - val_accuracy: 0.9343\n", - "Epoch 94/96\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.2177 - accuracy: 0.9531 - val_loss: 0.3189 - val_accuracy: 0.9167\n", - "Epoch 95/96\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1752 - accuracy: 0.9580 - val_loss: 0.2598 - val_accuracy: 0.9327\n", - "Epoch 96/96\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.1333 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2295\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 97/102\n", - "512/512 [==============================] - 86s 162ms/step - loss: 0.2493 - accuracy: 0.9272 - val_loss: 0.2420 - val_accuracy: 0.9535\n", - "Epoch 98/102\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2654 - accuracy: 0.9355 - val_loss: 0.2742 - val_accuracy: 0.8830\n", - "Epoch 99/102\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2616 - accuracy: 0.9397 - val_loss: 0.2934 - val_accuracy: 0.9375\n", - "Epoch 100/102\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2273 - accuracy: 0.9475 - val_loss: 0.2164 - val_accuracy: 0.9359\n", - "Epoch 101/102\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1576 - accuracy: 0.9673 - val_loss: 0.1799 - val_accuracy: 0.9471\n", - "Epoch 102/102\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.1021 - accuracy: 0.9819 - val_loss: 0.1920 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-097-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2420\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m577.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m492.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 103/108\n", - "512/512 [==============================] - 86s 162ms/step - loss: 0.2579 - accuracy: 0.9246 - val_loss: 0.3319 - val_accuracy: 0.9247\n", - "Epoch 104/108\n", - "512/512 [==============================] - 81s 158ms/step - loss: 0.2793 - accuracy: 0.9260 - val_loss: 0.3001 - val_accuracy: 0.9151\n", - "Epoch 105/108\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2745 - accuracy: 0.9338 - val_loss: 0.3302 - val_accuracy: 0.9391\n", - "Epoch 106/108\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.2249 - accuracy: 0.9443 - val_loss: 0.3120 - val_accuracy: 0.9215\n", - "Epoch 107/108\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1681 - accuracy: 0.9644 - val_loss: 0.4372 - val_accuracy: 0.9311\n", - "Epoch 108/108\n", - "512/512 [==============================] - 81s 159ms/step - loss: 0.1235 - accuracy: 0.9746 - val_loss: 0.3658 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-105-0.9391.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3302\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m585.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m495.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 109/114\n", - "512/512 [==============================] - 87s 163ms/step - loss: 0.2705 - accuracy: 0.9263 - val_loss: 0.2374 - val_accuracy: 0.9359\n", - "Epoch 110/114\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2861 - accuracy: 0.9246 - val_loss: 0.2402 - val_accuracy: 0.9455\n", - "Epoch 111/114\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2683 - accuracy: 0.9365 - val_loss: 0.2723 - val_accuracy: 0.9359\n", - "Epoch 112/114\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2379 - accuracy: 0.9441 - val_loss: 0.1936 - val_accuracy: 0.9471\n", - "Epoch 113/114\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1877 - accuracy: 0.9580 - val_loss: 0.2324 - val_accuracy: 0.9439\n", - "Epoch 114/114\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.1311 - accuracy: 0.9736 - val_loss: 0.2345 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-112-0.9471.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1936\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m514.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 115/120\n", - "512/512 [==============================] - 91s 170ms/step - loss: 0.2582 - accuracy: 0.9299 - val_loss: 0.3217 - val_accuracy: 0.9311\n", - "Epoch 116/120\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2866 - accuracy: 0.9224 - val_loss: 0.2336 - val_accuracy: 0.9391\n", - "Epoch 117/120\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2524 - accuracy: 0.9424 - val_loss: 0.2395 - val_accuracy: 0.9423\n", - "Epoch 118/120\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2286 - accuracy: 0.9482 - val_loss: 0.2789 - val_accuracy: 0.9551\n", - "Epoch 119/120\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1637 - accuracy: 0.9634 - val_loss: 0.2793 - val_accuracy: 0.9567\n", - "Epoch 120/120\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1067 - accuracy: 0.9800 - val_loss: 0.2853 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-119-0.9567.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2793\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m618.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m521.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 121/126\n", - "512/512 [==============================] - 91s 170ms/step - loss: 0.2549 - accuracy: 0.9287 - val_loss: 0.2443 - val_accuracy: 0.9519\n", - "Epoch 122/126\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2818 - accuracy: 0.9292 - val_loss: 0.2470 - val_accuracy: 0.9519\n", - "Epoch 123/126\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2809 - accuracy: 0.9326 - val_loss: 0.2941 - val_accuracy: 0.9439\n", - "Epoch 124/126\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2328 - accuracy: 0.9436 - val_loss: 0.1614 - val_accuracy: 0.9631\n", - "Epoch 125/126\n", - "512/512 [==============================] - 85s 165ms/step - loss: 0.1889 - accuracy: 0.9556 - val_loss: 0.2098 - val_accuracy: 0.9375\n", - "Epoch 126/126\n", - "512/512 [==============================] - 85s 165ms/step - loss: 0.1269 - accuracy: 0.9751 - val_loss: 0.1830 - val_accuracy: 0.9583\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-124-0.9631.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1614\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9567307829856873 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9631410241127014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.17186278104782104 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1614265888929367\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m622.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m516.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 127/132\n", - "512/512 [==============================] - 92s 171ms/step - loss: 0.2494 - accuracy: 0.9287 - val_loss: 0.3167 - val_accuracy: 0.9535\n", - "Epoch 128/132\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2697 - accuracy: 0.9290 - val_loss: 0.2598 - val_accuracy: 0.9407\n", - "Epoch 129/132\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2950 - accuracy: 0.9292 - val_loss: 0.2314 - val_accuracy: 0.9519\n", - "Epoch 130/132\n", - "512/512 [==============================] - 86s 167ms/step - loss: 0.2274 - accuracy: 0.9497 - val_loss: 0.1929 - val_accuracy: 0.9503\n", - "Epoch 131/132\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.1758 - accuracy: 0.9583 - val_loss: 0.1932 - val_accuracy: 0.9471\n", - "Epoch 132/132\n", - "512/512 [==============================] - 85s 165ms/step - loss: 0.1173 - accuracy: 0.9780 - val_loss: 0.2255 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-127-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3167\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m621.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m518.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 133/138\n", - "512/512 [==============================] - 92s 171ms/step - loss: 0.2436 - accuracy: 0.9304 - val_loss: 0.2788 - val_accuracy: 0.9503\n", - "Epoch 134/138\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2451 - accuracy: 0.9390 - val_loss: 0.2871 - val_accuracy: 0.9487\n", - "Epoch 135/138\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2731 - accuracy: 0.9348 - val_loss: 0.2828 - val_accuracy: 0.9279\n", - "Epoch 136/138\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2215 - accuracy: 0.9497 - val_loss: 0.2114 - val_accuracy: 0.9487\n", - "Epoch 137/138\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.1595 - accuracy: 0.9678 - val_loss: 0.2340 - val_accuracy: 0.9535\n", - "Epoch 138/138\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.1248 - accuracy: 0.9727 - val_loss: 0.2349 - val_accuracy: 0.9535\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-137-0.9535.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2340\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m623.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m520.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 139/144\n", - "512/512 [==============================] - 92s 172ms/step - loss: 0.2535 - accuracy: 0.9307 - val_loss: 0.2297 - val_accuracy: 0.9295\n", - "Epoch 140/144\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2881 - accuracy: 0.9258 - val_loss: 0.2479 - val_accuracy: 0.9487\n", - "Epoch 141/144\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2874 - accuracy: 0.9268 - val_loss: 0.3033 - val_accuracy: 0.9343\n", - "Epoch 142/144\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2872 - accuracy: 0.9246 - val_loss: 0.2649 - val_accuracy: 0.9439\n", - "Epoch 143/144\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2038 - accuracy: 0.9507 - val_loss: 0.2492 - val_accuracy: 0.9199\n", - "Epoch 144/144\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1476 - accuracy: 0.9683 - val_loss: 0.2257 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-140-0.9487.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2479\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m628.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m522.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 145/150\n", - "512/512 [==============================] - 87s 164ms/step - loss: 0.2732 - accuracy: 0.9285 - val_loss: 0.2106 - val_accuracy: 0.9551\n", - "Epoch 146/150\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2830 - accuracy: 0.9233 - val_loss: 0.2477 - val_accuracy: 0.9583\n", - "Epoch 147/150\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2609 - accuracy: 0.9414 - val_loss: 0.2034 - val_accuracy: 0.9535\n", - "Epoch 148/150\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2055 - accuracy: 0.9546 - val_loss: 0.5101 - val_accuracy: 0.8173\n", - "Epoch 149/150\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1713 - accuracy: 0.9634 - val_loss: 0.2369 - val_accuracy: 0.9423\n", - "Epoch 150/150\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1163 - accuracy: 0.9753 - val_loss: 0.2704 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-146-0.9583.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2477\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m610.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m499.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 151/156\n", - "512/512 [==============================] - 88s 164ms/step - loss: 0.3050 - accuracy: 0.9189 - val_loss: 0.2298 - val_accuracy: 0.9263\n", - "Epoch 152/156\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2837 - accuracy: 0.9243 - val_loss: 0.1924 - val_accuracy: 0.9471\n", - "Epoch 153/156\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2482 - accuracy: 0.9419 - val_loss: 0.2488 - val_accuracy: 0.9487\n", - "Epoch 154/156\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2033 - accuracy: 0.9556 - val_loss: 0.2723 - val_accuracy: 0.9519\n", - "Epoch 155/156\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1600 - accuracy: 0.9634 - val_loss: 0.2178 - val_accuracy: 0.9487\n", - "Epoch 156/156\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1309 - accuracy: 0.9705 - val_loss: 0.2520 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0;33mLoading the best weights...\u001b[0m\n", - "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-154-0.9519.h5...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2723\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m502.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 157/162\n", - "512/512 [==============================] - 87s 164ms/step - loss: 0.2530 - accuracy: 0.9314 - val_loss: 0.4695 - val_accuracy: 0.9327\n", - "Epoch 158/162\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2881 - accuracy: 0.9321 - val_loss: 0.3741 - val_accuracy: 0.9359\n", - "Epoch 159/162\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2420 - accuracy: 0.9399 - val_loss: 0.3475 - val_accuracy: 0.9375\n", - "Epoch 160/162\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1993 - accuracy: 0.9568 - val_loss: 0.1921 - val_accuracy: 0.9583\n", - "Epoch 161/162\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1561 - accuracy: 0.9644 - val_loss: 0.2095 - val_accuracy: 0.9471\n", - "Epoch 162/162\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1284 - accuracy: 0.9695 - val_loss: 0.2050 - val_accuracy: 0.9519\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1921}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2051\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m601.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 163/168\n", - "512/512 [==============================] - 87s 164ms/step - loss: 0.2361 - accuracy: 0.9348 - val_loss: 0.2340 - val_accuracy: 0.9439\n", - "Epoch 164/168\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2722 - accuracy: 0.9319 - val_loss: 0.2842 - val_accuracy: 0.9503\n", - "Epoch 165/168\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2600 - accuracy: 0.9399 - val_loss: 0.2952 - val_accuracy: 0.9295\n", - "Epoch 166/168\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.2447 - accuracy: 0.9438 - val_loss: 0.2630 - val_accuracy: 0.9263\n", - "Epoch 167/168\n", - "512/512 [==============================] - 82s 159ms/step - loss: 0.1589 - accuracy: 0.9673 - val_loss: 0.2470 - val_accuracy: 0.9423\n", - "Epoch 168/168\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1163 - accuracy: 0.9783 - val_loss: 0.2212 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2212}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2212\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 169/174\n", - "512/512 [==============================] - 88s 164ms/step - loss: 0.2650 - accuracy: 0.9272 - val_loss: 0.2829 - val_accuracy: 0.9263\n", - "Epoch 170/174\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2693 - accuracy: 0.9373 - val_loss: 0.3897 - val_accuracy: 0.9391\n", - "Epoch 171/174\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2435 - accuracy: 0.9502 - val_loss: 0.3412 - val_accuracy: 0.9231\n", - "Epoch 172/174\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1985 - accuracy: 0.9565 - val_loss: 0.2695 - val_accuracy: 0.9311\n", - "Epoch 173/174\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1515 - accuracy: 0.9680 - val_loss: 0.2574 - val_accuracy: 0.9375\n", - "Epoch 174/174\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1211 - accuracy: 0.9756 - val_loss: 0.2405 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2405\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m603.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 175/180\n", - "512/512 [==============================] - 88s 164ms/step - loss: 0.2395 - accuracy: 0.9290 - val_loss: 0.2182 - val_accuracy: 0.9327\n", - "Epoch 176/180\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2648 - accuracy: 0.9360 - val_loss: 0.3920 - val_accuracy: 0.9151\n", - "Epoch 177/180\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.2603 - accuracy: 0.9399 - val_loss: 0.2183 - val_accuracy: 0.9391\n", - "Epoch 178/180\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2106 - accuracy: 0.9551 - val_loss: 0.2085 - val_accuracy: 0.9455\n", - "Epoch 179/180\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1751 - accuracy: 0.9626 - val_loss: 0.2304 - val_accuracy: 0.9455\n", - "Epoch 180/180\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.1163 - accuracy: 0.9780 - val_loss: 0.2240 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.2085}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2240\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m604.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 181/186\n", - "512/512 [==============================] - 92s 171ms/step - loss: 0.2400 - accuracy: 0.9346 - val_loss: 0.2400 - val_accuracy: 0.9343\n", - "Epoch 182/186\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2529 - accuracy: 0.9395 - val_loss: 0.2746 - val_accuracy: 0.9295\n", - "Epoch 183/186\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2448 - accuracy: 0.9421 - val_loss: 0.2956 - val_accuracy: 0.9503\n", - "Epoch 184/186\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2068 - accuracy: 0.9539 - val_loss: 0.2435 - val_accuracy: 0.9439\n", - "Epoch 185/186\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.1489 - accuracy: 0.9680 - val_loss: 0.2623 - val_accuracy: 0.9455\n", - "Epoch 186/186\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1060 - accuracy: 0.9778 - val_loss: 0.2387 - val_accuracy: 0.9455\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2387}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2387\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m626.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m521.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33m└───Shuffling data...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 187/192\n", - "512/512 [==============================] - 92s 172ms/step - loss: 0.2255 - accuracy: 0.9358 - val_loss: 0.2049 - val_accuracy: 0.9359\n", - "Epoch 188/192\n", - "512/512 [==============================] - 87s 171ms/step - loss: 0.2452 - accuracy: 0.9355 - val_loss: 0.2099 - val_accuracy: 0.9471\n", - "Epoch 189/192\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2326 - accuracy: 0.9512 - val_loss: 0.2640 - val_accuracy: 0.9455\n", - "Epoch 190/192\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.2011 - accuracy: 0.9595 - val_loss: 0.2538 - val_accuracy: 0.9471\n", - "Epoch 191/192\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.2197 - accuracy: 0.9609 - val_loss: 0.2370 - val_accuracy: 0.9519\n", - "Epoch 192/192\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.1511 - accuracy: 0.9775 - val_loss: 0.2349 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.2049}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2349\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m650.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m526.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 193/198\n", - "173/512 [=========>....................] - ETA: 49s - loss: 0.2603 - accuracy: 0.9364\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 153s 290ms/step - loss: 0.2699 - accuracy: 0.9292 - val_loss: 0.2112 - val_accuracy: 0.9295\n", - "Epoch 194/198\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.2731 - accuracy: 0.9248 - val_loss: 0.3021 - val_accuracy: 0.9455\n", - "Epoch 195/198\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2551 - accuracy: 0.9392 - val_loss: 0.3150 - val_accuracy: 0.9247\n", - "Epoch 196/198\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2326 - accuracy: 0.9399 - val_loss: 0.3005 - val_accuracy: 0.9343\n", - "Epoch 197/198\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1818 - accuracy: 0.9585 - val_loss: 0.3222 - val_accuracy: 0.9407\n", - "Epoch 198/198\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.1226 - accuracy: 0.9771 - val_loss: 0.3274 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2112}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3274\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m696.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m584.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 199/204\n", - "512/512 [==============================] - 93s 173ms/step - loss: 0.2488 - accuracy: 0.9299 - val_loss: 0.2098 - val_accuracy: 0.9487\n", - "Epoch 200/204\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2554 - accuracy: 0.9363 - val_loss: 0.2605 - val_accuracy: 0.9503\n", - "Epoch 201/204\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.2308 - accuracy: 0.9448 - val_loss: 0.3346 - val_accuracy: 0.9263\n", - "Epoch 202/204\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2659 - accuracy: 0.9324 - val_loss: 0.4138 - val_accuracy: 0.8926\n", - "Epoch 203/204\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1899 - accuracy: 0.9612 - val_loss: 0.2993 - val_accuracy: 0.9439\n", - "Epoch 204/204\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1459 - accuracy: 0.9666 - val_loss: 0.3281 - val_accuracy: 0.9375\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2098}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3281\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m639.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 205/210\n", - "512/512 [==============================] - 92s 172ms/step - loss: 0.2516 - accuracy: 0.9299 - val_loss: 0.2961 - val_accuracy: 0.9391\n", - "Epoch 206/210\n", - "512/512 [==============================] - 87s 171ms/step - loss: 0.2614 - accuracy: 0.9370 - val_loss: 0.2434 - val_accuracy: 0.9487\n", - "Epoch 207/210\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2402 - accuracy: 0.9453 - val_loss: 0.4394 - val_accuracy: 0.8894\n", - "Epoch 208/210\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.1976 - accuracy: 0.9561 - val_loss: 0.2486 - val_accuracy: 0.9439\n", - "Epoch 209/210\n", - "512/512 [==============================] - 85s 167ms/step - loss: 0.1348 - accuracy: 0.9688 - val_loss: 0.3123 - val_accuracy: 0.9455\n", - "Epoch 210/210\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1049 - accuracy: 0.9753 - val_loss: 0.2694 - val_accuracy: 0.9487\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2434}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2694\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m638.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m523.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 211/216\n", - "512/512 [==============================] - 92s 171ms/step - loss: 0.2418 - accuracy: 0.9307 - val_loss: 0.2496 - val_accuracy: 0.9455\n", - "Epoch 212/216\n", - "512/512 [==============================] - 86s 167ms/step - loss: 0.2612 - accuracy: 0.9307 - val_loss: 0.3104 - val_accuracy: 0.9071\n", - "Epoch 213/216\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.2478 - accuracy: 0.9431 - val_loss: 0.3774 - val_accuracy: 0.9359\n", - "Epoch 214/216\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.2076 - accuracy: 0.9546 - val_loss: 0.3438 - val_accuracy: 0.9391\n", - "Epoch 215/216\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1539 - accuracy: 0.9675 - val_loss: 0.4097 - val_accuracy: 0.9359\n", - "Epoch 216/216\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.1075 - accuracy: 0.9802 - val_loss: 0.4911 - val_accuracy: 0.9247\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2496}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4912\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m624.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m512.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 217/222\n", - "512/512 [==============================] - 89s 166ms/step - loss: 0.2197 - accuracy: 0.9365 - val_loss: 0.2334 - val_accuracy: 0.9471\n", - "Epoch 218/222\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2295 - accuracy: 0.9402 - val_loss: 0.3119 - val_accuracy: 0.8958\n", - "Epoch 219/222\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2134 - accuracy: 0.9561 - val_loss: 0.2296 - val_accuracy: 0.9311\n", - "Epoch 220/222\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1859 - accuracy: 0.9602 - val_loss: 0.2600 - val_accuracy: 0.9327\n", - "Epoch 221/222\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1649 - accuracy: 0.9680 - val_loss: 0.2953 - val_accuracy: 0.9375\n", - "Epoch 222/222\n", - "512/512 [==============================] - 82s 160ms/step - loss: 0.1140 - accuracy: 0.9792 - val_loss: 0.2859 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.2296}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2859\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m612.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 223/228\n", - "512/512 [==============================] - 89s 167ms/step - loss: 0.2312 - accuracy: 0.9331 - val_loss: 0.2373 - val_accuracy: 0.9407\n", - "Epoch 224/228\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2420 - accuracy: 0.9348 - val_loss: 0.2742 - val_accuracy: 0.9407\n", - "Epoch 225/228\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.2070 - accuracy: 0.9480 - val_loss: 0.2802 - val_accuracy: 0.9375\n", - "Epoch 226/228\n", - "512/512 [==============================] - 82s 161ms/step - loss: 0.1753 - accuracy: 0.9653 - val_loss: 0.3583 - val_accuracy: 0.9343\n", - "Epoch 227/228\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.1362 - accuracy: 0.9697 - val_loss: 0.3364 - val_accuracy: 0.9407\n", - "Epoch 228/228\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.0999 - accuracy: 0.9800 - val_loss: 0.2650 - val_accuracy: 0.9423\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2373}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2650\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m614.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 229/234\n", - "512/512 [==============================] - 89s 166ms/step - loss: 0.2398 - accuracy: 0.9309 - val_loss: 0.2423 - val_accuracy: 0.9439\n", - "Epoch 230/234\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.2820 - accuracy: 0.9268 - val_loss: 0.3899 - val_accuracy: 0.9327\n", - "Epoch 231/234\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2472 - accuracy: 0.9421 - val_loss: 0.2796 - val_accuracy: 0.9327\n", - "Epoch 232/234\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.2008 - accuracy: 0.9536 - val_loss: 0.2522 - val_accuracy: 0.9455\n", - "Epoch 233/234\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1747 - accuracy: 0.9631 - val_loss: 0.2504 - val_accuracy: 0.9407\n", - "Epoch 234/234\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1318 - accuracy: 0.9717 - val_loss: 0.3184 - val_accuracy: 0.9343\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2423}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3184\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m614.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 235/240\n", - "512/512 [==============================] - 89s 166ms/step - loss: 0.2393 - accuracy: 0.9336 - val_loss: 0.5106 - val_accuracy: 0.8878\n", - "Epoch 236/240\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2378 - accuracy: 0.9358 - val_loss: 0.2732 - val_accuracy: 0.9423\n", - "Epoch 237/240\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2494 - accuracy: 0.9429 - val_loss: 0.2851 - val_accuracy: 0.9407\n", - "Epoch 238/240\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1862 - accuracy: 0.9624 - val_loss: 0.3868 - val_accuracy: 0.9167\n", - "Epoch 239/240\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1377 - accuracy: 0.9707 - val_loss: 0.1950 - val_accuracy: 0.9375\n", - "Epoch 240/240\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.0853 - accuracy: 0.9844 - val_loss: 0.2666 - val_accuracy: 0.9359\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1950}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2666\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m620.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m506.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 241/246\n", - "512/512 [==============================] - 90s 167ms/step - loss: 0.1995 - accuracy: 0.9424 - val_loss: 0.2085 - val_accuracy: 0.9359\n", - "Epoch 242/246\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2204 - accuracy: 0.9478 - val_loss: 0.2644 - val_accuracy: 0.9487\n", - "Epoch 243/246\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.1884 - accuracy: 0.9580 - val_loss: 0.2390 - val_accuracy: 0.9391\n", - "Epoch 244/246\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2060 - accuracy: 0.9634 - val_loss: 0.2529 - val_accuracy: 0.9471\n", - "Epoch 245/246\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1437 - accuracy: 0.9773 - val_loss: 0.3027 - val_accuracy: 0.9487\n", - "Epoch 246/246\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1003 - accuracy: 0.9834 - val_loss: 0.2626 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2085}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2625\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m621.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m507.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d02-h05_m28_s20\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 247/252\n", - "512/512 [==============================] - 89s 166ms/step - loss: 0.2291 - accuracy: 0.9373 - val_loss: 0.2281 - val_accuracy: 0.9503\n", - "Epoch 248/252\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2351 - accuracy: 0.9370 - val_loss: 0.2358 - val_accuracy: 0.9359\n", - "Epoch 249/252\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.2046 - accuracy: 0.9553 - val_loss: 0.3557 - val_accuracy: 0.9439\n", - "Epoch 250/252\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.2004 - accuracy: 0.9583 - val_loss: 0.3836 - val_accuracy: 0.9247\n", - "Epoch 251/252\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1540 - accuracy: 0.9683 - val_loss: 0.3067 - val_accuracy: 0.9407\n", - "Epoch 252/252\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.1198 - accuracy: 0.9780 - val_loss: 0.2948 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2281}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2948\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m642.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m516.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 253/258\n", - "512/512 [==============================] - 92s 172ms/step - loss: 0.2299 - accuracy: 0.9380 - val_loss: 0.4194 - val_accuracy: 0.9359\n", - "Epoch 254/258\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2248 - accuracy: 0.9448 - val_loss: 0.2327 - val_accuracy: 0.9423\n", - "Epoch 255/258\n", - "512/512 [==============================] - 87s 171ms/step - loss: 0.2172 - accuracy: 0.9526 - val_loss: 0.2595 - val_accuracy: 0.9439\n", - "Epoch 256/258\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2070 - accuracy: 0.9568 - val_loss: 0.3004 - val_accuracy: 0.9471\n", - "Epoch 257/258\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.1542 - accuracy: 0.9663 - val_loss: 0.2271 - val_accuracy: 0.9487\n", - "Epoch 258/258\n", - "512/512 [==============================] - 88s 172ms/step - loss: 0.1053 - accuracy: 0.9805 - val_loss: 0.2649 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2271}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2649\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m654.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m530.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 259/264\n", - "119/512 [=====>........................] - ETA: 56s - loss: 0.2614 - accuracy: 0.9296\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 152s 289ms/step - loss: 0.2454 - accuracy: 0.9285 - val_loss: 0.2276 - val_accuracy: 0.9375\n", - "Epoch 260/264\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2876 - accuracy: 0.9326 - val_loss: 0.3372 - val_accuracy: 0.9455\n", - "Epoch 261/264\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2292 - accuracy: 0.9490 - val_loss: 0.3965 - val_accuracy: 0.9327\n", - "Epoch 262/264\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1709 - accuracy: 0.9634 - val_loss: 0.2338 - val_accuracy: 0.9519\n", - "Epoch 263/264\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1755 - accuracy: 0.9705 - val_loss: 0.2991 - val_accuracy: 0.9487\n", - "Epoch 264/264\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1390 - accuracy: 0.9792 - val_loss: 0.3091 - val_accuracy: 0.9439\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2276}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3090\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m713.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m586.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m126.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 265/270\n", - "512/512 [==============================] - 93s 174ms/step - loss: 0.2435 - accuracy: 0.9421 - val_loss: 0.2473 - val_accuracy: 0.9423\n", - "Epoch 266/270\n", - "512/512 [==============================] - 87s 171ms/step - loss: 0.2490 - accuracy: 0.9397 - val_loss: 0.2585 - val_accuracy: 0.9455\n", - "Epoch 267/270\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.2312 - accuracy: 0.9441 - val_loss: 0.3069 - val_accuracy: 0.9311\n", - "Epoch 268/270\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1901 - accuracy: 0.9558 - val_loss: 0.4075 - val_accuracy: 0.8830\n", - "Epoch 269/270\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1472 - accuracy: 0.9688 - val_loss: 0.3201 - val_accuracy: 0.9343\n", - "Epoch 270/270\n", - "512/512 [==============================] - 86s 168ms/step - loss: 0.1057 - accuracy: 0.9775 - val_loss: 0.3155 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2473}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3155\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m656.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m528.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 271/276\n", - "149/512 [=======>......................] - ETA: 53s - loss: 0.1901 - accuracy: 0.9455\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 154s 292ms/step - loss: 0.2038 - accuracy: 0.9377 - val_loss: 0.5999 - val_accuracy: 0.8958\n", - "Epoch 272/276\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.2129 - accuracy: 0.9421 - val_loss: 0.3347 - val_accuracy: 0.9183\n", - "Epoch 273/276\n", - "512/512 [==============================] - 88s 172ms/step - loss: 0.2193 - accuracy: 0.9543 - val_loss: 0.2374 - val_accuracy: 0.9535\n", - "Epoch 274/276\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1566 - accuracy: 0.9619 - val_loss: 0.1854 - val_accuracy: 0.9503\n", - "Epoch 275/276\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1191 - accuracy: 0.9719 - val_loss: 0.2512 - val_accuracy: 0.9503\n", - "Epoch 276/276\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.0937 - accuracy: 0.9807 - val_loss: 0.3150 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1854}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3150\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m719.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m589.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 277/282\n", - "512/512 [==============================] - 93s 174ms/step - loss: 0.2085 - accuracy: 0.9377 - val_loss: 0.2248 - val_accuracy: 0.9423\n", - "Epoch 278/282\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2104 - accuracy: 0.9468 - val_loss: 0.3161 - val_accuracy: 0.9535\n", - "Epoch 279/282\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1777 - accuracy: 0.9631 - val_loss: 0.3394 - val_accuracy: 0.9359\n", - "Epoch 280/282\n", - "512/512 [==============================] - 88s 172ms/step - loss: 0.1579 - accuracy: 0.9670 - val_loss: 0.3100 - val_accuracy: 0.9567\n", - "Epoch 281/282\n", - "512/512 [==============================] - 85s 165ms/step - loss: 0.1234 - accuracy: 0.9778 - val_loss: 0.4045 - val_accuracy: 0.9247\n", - "Epoch 282/282\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.0810 - accuracy: 0.9878 - val_loss: 0.3237 - val_accuracy: 0.9391\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.2248}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3237\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m649.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 283/288\n", - "512/512 [==============================] - 90s 167ms/step - loss: 0.2240 - accuracy: 0.9316 - val_loss: 0.2660 - val_accuracy: 0.9503\n", - "Epoch 284/288\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1951 - accuracy: 0.9448 - val_loss: 0.2729 - val_accuracy: 0.9343\n", - "Epoch 285/288\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2376 - accuracy: 0.9524 - val_loss: 0.3847 - val_accuracy: 0.9423\n", - "Epoch 286/288\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1763 - accuracy: 0.9658 - val_loss: 0.3685 - val_accuracy: 0.9423\n", - "Epoch 287/288\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.1385 - accuracy: 0.9685 - val_loss: 0.2170 - val_accuracy: 0.9519\n", - "Epoch 288/288\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.1054 - accuracy: 0.9773 - val_loss: 0.2134 - val_accuracy: 0.9503\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2134}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2134\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m508.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 289/294\n", - "512/512 [==============================] - 89s 167ms/step - loss: 0.2156 - accuracy: 0.9392 - val_loss: 0.3535 - val_accuracy: 0.9455\n", - "Epoch 290/294\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2335 - accuracy: 0.9390 - val_loss: 0.2257 - val_accuracy: 0.9487\n", - "Epoch 291/294\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.1945 - accuracy: 0.9519 - val_loss: 0.2495 - val_accuracy: 0.9503\n", - "Epoch 292/294\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1647 - accuracy: 0.9592 - val_loss: 0.1974 - val_accuracy: 0.9487\n", - "Epoch 293/294\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.1159 - accuracy: 0.9719 - val_loss: 0.1649 - val_accuracy: 0.9535\n", - "Epoch 294/294\n", - "512/512 [==============================] - 85s 166ms/step - loss: 0.0944 - accuracy: 0.9792 - val_loss: 0.1747 - val_accuracy: 0.9551\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1649}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1747\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m511.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 295/300\n", - "512/512 [==============================] - 89s 167ms/step - loss: 0.2244 - accuracy: 0.9324 - val_loss: 0.2936 - val_accuracy: 0.9439\n", - "Epoch 296/300\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2622 - accuracy: 0.9272 - val_loss: 0.2860 - val_accuracy: 0.9407\n", - "Epoch 297/300\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2746 - accuracy: 0.9451 - val_loss: 0.4849 - val_accuracy: 0.9071\n", - "Epoch 298/300\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.2036 - accuracy: 0.9556 - val_loss: 0.2450 - val_accuracy: 0.9375\n", - "Epoch 299/300\n", - "512/512 [==============================] - 83s 161ms/step - loss: 0.1328 - accuracy: 0.9712 - val_loss: 0.2686 - val_accuracy: 0.9327\n", - "Epoch 300/300\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.0898 - accuracy: 0.9807 - val_loss: 0.3176 - val_accuracy: 0.9279\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.2450}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3176\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m625.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m505.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 301/306\n", - "512/512 [==============================] - 90s 168ms/step - loss: 0.2080 - accuracy: 0.9434 - val_loss: 0.2541 - val_accuracy: 0.9439\n", - "Epoch 302/306\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.2532 - accuracy: 0.9343 - val_loss: 0.2347 - val_accuracy: 0.9375\n", - "Epoch 303/306\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.2141 - accuracy: 0.9495 - val_loss: 0.2215 - val_accuracy: 0.9519\n", - "Epoch 304/306\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.1817 - accuracy: 0.9597 - val_loss: 0.2861 - val_accuracy: 0.9407\n", - "Epoch 305/306\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.1299 - accuracy: 0.9766 - val_loss: 0.1812 - val_accuracy: 0.9455\n", - "Epoch 306/306\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.0898 - accuracy: 0.9844 - val_loss: 0.2148 - val_accuracy: 0.9407\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1812}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2148\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m627.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m509.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 307/312\n", - "512/512 [==============================] - 89s 167ms/step - loss: 0.1939 - accuracy: 0.9478 - val_loss: 0.2203 - val_accuracy: 0.9439\n", - "Epoch 308/312\n", - "512/512 [==============================] - 84s 164ms/step - loss: 0.2045 - accuracy: 0.9504 - val_loss: 0.2867 - val_accuracy: 0.9487\n", - "Epoch 309/312\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1870 - accuracy: 0.9624 - val_loss: 0.2737 - val_accuracy: 0.9487\n", - "Epoch 310/312\n", - "512/512 [==============================] - 83s 162ms/step - loss: 0.1676 - accuracy: 0.9653 - val_loss: 0.2592 - val_accuracy: 0.9423\n", - "Epoch 311/312\n", - "512/512 [==============================] - 83s 163ms/step - loss: 0.1200 - accuracy: 0.9749 - val_loss: 0.2440 - val_accuracy: 0.9487\n", - "Epoch 312/312\n", - "512/512 [==============================] - 84s 163ms/step - loss: 0.0816 - accuracy: 0.9839 - val_loss: 0.2292 - val_accuracy: 0.9471\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2203}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2292\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m506.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 313/318\n", - "512/512 [==============================] - 90s 167ms/step - loss: 0.1826 - accuracy: 0.9436 - val_loss: 0.2867 - val_accuracy: 0.9279\n", - "Epoch 314/318\n", - "512/512 [==============================] - 84s 165ms/step - loss: 0.2192 - accuracy: 0.9497 - val_loss: 0.3617 - val_accuracy: 0.9311\n", - "Epoch 315/318\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1704 - accuracy: 0.9583 - val_loss: 0.3324 - val_accuracy: 0.9423\n", - "Epoch 316/318\n", - "512/512 [==============================] - 88s 172ms/step - loss: 0.1448 - accuracy: 0.9727 - val_loss: 0.4824 - val_accuracy: 0.9022\n", - "Epoch 317/318\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1045 - accuracy: 0.9792 - val_loss: 0.3307 - val_accuracy: 0.9359\n", - "Epoch 318/318\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.0721 - accuracy: 0.9851 - val_loss: 0.3812 - val_accuracy: 0.9311\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2867}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3813\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m646.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 319/324\n", - "167/512 [========>.....................] - ETA: 50s - loss: 0.2437 - accuracy: 0.9341\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\u001b[0;33mResuming training...\u001b[0m\n", - "512/512 [==============================] - 153s 291ms/step - loss: 0.2265 - accuracy: 0.9348 - val_loss: 0.4225 - val_accuracy: 0.9135\n", - "Epoch 320/324\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.2323 - accuracy: 0.9395 - val_loss: 0.3071 - val_accuracy: 0.9359\n", - "Epoch 321/324\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.2141 - accuracy: 0.9475 - val_loss: 0.4155 - val_accuracy: 0.9391\n", - "Epoch 322/324\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1850 - accuracy: 0.9578 - val_loss: 0.6766 - val_accuracy: 0.9199\n", - "Epoch 323/324\n", - "512/512 [==============================] - 86s 169ms/step - loss: 0.1652 - accuracy: 0.9595 - val_loss: 0.3307 - val_accuracy: 0.9247\n", - "Epoch 324/324\n", - "512/512 [==============================] - 88s 171ms/step - loss: 0.1119 - accuracy: 0.9768 - val_loss: 0.4258 - val_accuracy: 0.9215\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.3071}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4258\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m721.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m590.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 325/330\n", - "512/512 [==============================] - 94s 175ms/step - loss: 0.2087 - accuracy: 0.9353 - val_loss: 0.3861 - val_accuracy: 0.9279\n", - "Epoch 326/330\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2161 - accuracy: 0.9392 - val_loss: 0.2966 - val_accuracy: 0.9199\n", - "Epoch 327/330\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.2025 - accuracy: 0.9521 - val_loss: 0.5782 - val_accuracy: 0.8029\n", - "Epoch 328/330\n", - "512/512 [==============================] - 87s 170ms/step - loss: 0.1962 - accuracy: 0.9507 - val_loss: 0.4708 - val_accuracy: 0.9263\n", - "Epoch 329/330\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.1362 - accuracy: 0.9670 - val_loss: 0.2955 - val_accuracy: 0.9279\n", - "Epoch 330/330\n", - "512/512 [==============================] - 87s 169ms/step - loss: 0.0947 - accuracy: 0.9785 - val_loss: 0.4636 - val_accuracy: 0.9215\n", - "\u001b[0;32mSubset training done.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9279}, \u001b[0m\u001b[0;33mloss{0.2955}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4636\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m665.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m528.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m136.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", - "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", - "\u001b[0m\n", - "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;33mPreparing train data...\u001b[0m\n", - "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", - "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", - "\u001b[0;32mTraining on subset...\u001b[0m\n", - "Epoch 331/336\n", - "119/512 [=====>........................] - ETA: 57s - loss: 0.2186 - accuracy: 0.9401\u001b[0;31m\n", - "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", - "\n", - "KeyboardInterrupt.\n", - "Training done.\n", - "\n" - ] - } - ], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "# CONF <-------------------------------------------------------------------------->\n", - "# Hyperparameters for training the model:\n", - "max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n", - "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", - "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", - "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n", - "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", - "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", - "RES_Train = False # RES_Train: Resume training if True.\n", - "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", - "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n", - "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n", - "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n", - "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n", - "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n", - "Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n", - "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n", - "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n", - "USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True.\n", - "AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n", - "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n", - "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n", - "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n", - "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n", - "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n", - "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n", - "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n", - "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n", - "Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️).\n", - "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n", - "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n", - "Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n", - "BEST_RSN = 'PAI_model_T' # Best model save name prefix.\n", - "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n", - "IMAGE_GEN_PATH = 'Data\\\\image_SUB_generator.pkl'\n", - "# CONF END <---------------------------------------------------------------------->\n", - "#Prep\n", - "if RES_Train:\n", - " MAX_LR = RES_LR\n", - " PL_epoch = 1\n", - "#VAR\n", - "Total_SUB_epoch_C = 0 # TO FIX TensorBoard\n", - "CU_LR = MAX_LR\n", - "all_histories = []\n", - "chosen_indices = []\n", - "subset_sizes = []\n", - "best_acc = 0\n", - "best_loss = float('inf')\n", - "#Funcs\n", - "def normalize_TO_RANGE(arr, min_val, max_val):\n", - " arr = arr.astype('float32')\n", - " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", - " arr = arr * (max_val - min_val) + min_val\n", - " return arr\n", - "\n", - "def Z_SCORE_normalize(arr):\n", - " arr = arr.astype('float32')\n", - " mean = np.mean(arr)\n", - " std_dev = np.std(arr)\n", - " arr = (arr - mean) / std_dev\n", - " return arr\n", - "\n", - "def add_image_grain_TRLRev2(image, intensity = 0.01):\n", - " # Generate random noise array\n", - " noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \\\n", - " + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2\n", - "\n", - " # Scale the noise array\n", - " scaled_noise = (noise * intensity).astype(np.float32)\n", - " # Add the noise to the image\n", - " noisy_image = cv2.add(image, scaled_noise)\n", - "\n", - " return noisy_image\n", - "# noise_func_TRLRev2 ([REV1 OLD])\n", - "if not USE_REV2_DP:\n", - " def noise_func_TRLRev2(image): \n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.08, 0.08)\n", - " intensityL1 = random.uniform(-0.05, 0.05)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image.shape[0], block_size_L2):\n", - " for j in range(0, image.shape[1], block_size_L2):\n", - " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image = new_image \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image.shape[0], block_size_L1):\n", - " for j in range(0, image.shape[1], block_size_L1):\n", - " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.07) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", - "# noise_func_TRLRev2 ([REV2 NEW])\n", - "else:\n", - " def noise_func_TRLRev2(image):\n", - " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", - " new_image = np.copy(image)\n", - " \n", - " if noise_type == 'L3':\n", - " intensityL2 = random.uniform(-0.07, 0.07)\n", - " intensityL1 = random.uniform(-0.06, 0.06)\n", - " else:\n", - " intensityL2 = random.uniform(-0.09, 0.09)\n", - " intensityL1 = random.uniform(-0.07, 0.07)\n", - " \n", - " block_size_L1 = random.randint(16, 32)\n", - " block_size_L2 = random.randint(32, 112)\n", - " \n", - " for channel in range(3): # Iterate over each RGB channel\n", - " image_channel = image[:, :, channel]\n", - " new_image_channel = new_image[:, :, channel]\n", - " \n", - " if noise_type == 'L2' or noise_type == 'L3':\n", - " for i in range(0, image_channel.shape[0], block_size_L2):\n", - " for j in range(0, image_channel.shape[1], block_size_L2):\n", - " block = image_channel[i:i+block_size_L2, j:j+block_size_L2]\n", - " block = (np.random.rand() * intensityL2 + 1) * block\n", - " new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block\n", - " image_channel = new_image_channel \n", - " \n", - " if noise_type == 'L1' or noise_type == 'L3': \n", - " for i in range(0, image_channel.shape[0], block_size_L1):\n", - " for j in range(0, image_channel.shape[1], block_size_L1):\n", - " block = image_channel[i:i+block_size_L1, j:j+block_size_L1]\n", - " block = (np.random.rand() * intensityL1 + 1) * block\n", - " new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block\n", - " \n", - " new_image[:, :, channel] = new_image_channel\n", - " \n", - " if add_img_grain:\n", - " intensity = random.uniform(0, 0.05) # Random intensity \n", - " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", - " return new_image\n", - "#CONST\n", - "train_SUB_datagen = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True,\n", - " rotation_range=179,\n", - " zoom_range=0.18, \n", - " shear_range=0.18,\n", - " width_shift_range=0.18,\n", - " brightness_range=(0.82, 1.18),\n", - " height_shift_range=0.18,\n", - " channel_shift_range=100,\n", - " featurewise_center=True,\n", - " featurewise_std_normalization=True,\n", - " zca_whitening=False,\n", - " interpolation_order=2,\n", - " fill_mode='nearest',\n", - " preprocessing_function=noise_func_TRLRev2\n", - " )\n", - "class TerminateOnHighTemp(tf.keras.callbacks.Callback):\n", - " def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60):\n", - " super().__init__()\n", - " self.active = active\n", - " self.check_every_n_batches = check_every_n_batches\n", - " self.high_temp = high_temp\n", - " self.low_temp = low_temp\n", - " self.pause_time = pause_time\n", - " self.batch_counter = 0\n", - "\n", - " def on_batch_end(self, batch, logs=None):\n", - " if not self.active:\n", - " return\n", - " self.batch_counter += 1\n", - " if self.batch_counter % self.check_every_n_batches == 0:\n", - " temperature = gpu_control.get_temperature()\n", - " if temperature > self.high_temp:\n", - " print_Color(f'\\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False)\n", - " time.sleep(self.pause_time) \n", - " while gpu_control.get_temperature() > self.low_temp:\n", - " time.sleep(4)\n", - " print_Color('Resuming training...', ['yellow'])\n", - "class ExtendedTensorBoard(TensorBoard):\n", - " def on_epoch_end(self, epoch, logs=None):\n", - " logs = logs or {}\n", - " logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr)\n", - " logs['momentum'] = self.model.optimizer.momentum \n", - " super().on_epoch_end(epoch, logs)\n", - "class DummyCallback(Callback):\n", - " pass\n", - "steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2\n", - "#callbacks>>>\n", - "# EarlyStopping\n", - "early_stopping = EarlyStopping(monitor='val_accuracy',\n", - " patience=EarlyStopping_P,\n", - " verbose=1, restore_best_weights=True,\n", - " mode='max'\n", - " ) if Use_ES_ONSUBT else DummyCallback()\n", - "# ModelCheckpoint \n", - "checkpoint_SUB = ModelCheckpoint(f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', \n", - " monitor=load_SUB_BRW_MODE,\n", - " save_best_only=True, mode=ModelCheckpoint_mode,\n", - " save_weights_only = True\n", - " ) if load_SUB_BRW else DummyCallback()\n", - "checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", - "# TerminateOnHighTemp\n", - "TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M,\n", - " check_every_n_batches=6,\n", - " high_temp=72,\n", - " low_temp=58,\n", - " pause_time=60)\n", - "# TensorBoard\n", - "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - "if Use_extended_tensorboard:\n", - " tensorboard_callback = ExtendedTensorBoard(\n", - " log_dir=log_dir,\n", - " write_images=False, # Uses a lot of memory\n", - " histogram_freq=1,\n", - " update_freq='epoch',\n", - " write_grads=True,\n", - " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", - " )\n", - "else:\n", - " tensorboard_callback = TensorBoard(\n", - " log_dir=log_dir,\n", - " write_images=False, # Uses a lot of memory\n", - " histogram_freq=1,\n", - " update_freq='epoch',\n", - " write_grads=True,\n", - " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", - " )\n", - "# OneCycleLr\n", - "if OneCycleLr_UFTS: \n", - " learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR,\n", - " steps_per_epoch=steps_per_epoch_train_SUB,\n", - " epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) \n", - "#PRES\n", - "# ...\n", - "#MAIN\n", - "print('Training the model...')\n", - "# INFOp\n", - "print_Color('\\nSetup Verbose:', ['yellow'])\n", - "print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", - "#warnings\n", - "P_warning('RES_Train is True.') if RES_Train else None\n", - "print_Color('Setup Verbose END.', ['yellow'])\n", - "# MAIN LOOP\n", - "try:\n", - " for epoch in range(1, max_epoch):\n", - " # Start Epoch\n", - " STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning'\n", - " C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT\n", - " if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1\n", - " start_FULL_time = time.time()\n", - " if Auto_clear_cache:\n", - " subprocess.run([\"Cache_clear.cmd\"], shell=True)\n", - " # TSEC: Total-Subset-Epoch-Count\n", - " print_Color(f'\\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True)\n", - " # DP\n", - " if not AdvSubsetC:\n", - " print_Color('Shuffling data...', ['yellow'])\n", - " x_train, y_train = shuffle_data(x_train, y_train)\n", - " print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", - " if AdvSubsetC:\n", - " if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0:\n", - " print_Color('└───Shuffling data...', ['yellow'])\n", - " x_train, y_train = shuffle_data(x_train, y_train)\n", - " chosen_indices = [] # Reset chosen_indices\n", - "\n", - " available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices))\n", - " \n", - " if len(available_indices) < subset_size:\n", - " #DEBUG\n", - " # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.')\n", - " # If there are not enough available indices, choose from the indices that were chosen the longest time ago\n", - " old_indices = chosen_indices[:subset_size - len(available_indices)]\n", - " subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False))\n", - " \n", - " # Update the list of chosen indices and their sizes\n", - " chosen_indices = chosen_indices[len(old_indices):] + subset_indices\n", - " subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices)\n", - " else:\n", - " subset_indices = list(np.random.choice(available_indices, subset_size, replace=False))\n", - " \n", - " # Add the chosen indices to the list of already chosen indices\n", - " chosen_indices += subset_indices\n", - " subset_sizes += [subset_size] * len(subset_indices)\n", - " else:\n", - " subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False)\n", - " # Taking the subset\n", - " x_SUB_train = x_train[subset_indices]\n", - " y_SUB_train = y_train[subset_indices]\n", - " x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train)\n", - " assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}'\n", - " print_Color('Preparing train data...', ['yellow']) \n", - " # if epoch == 1: # OLD\n", - " # print_Color('- ImageDataGenerator fit...', ['yellow']) \n", - " # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", - " # print_Color('- ImageDataGenerator fit done.', ['yellow'])\n", - " if epoch == 1 or ALWAYS_REFIT_IDG == 2:\n", - " if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG:\n", - " print_Color('- Loading fitted ImageDataGenerator...', ['yellow'])\n", - " train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) \n", - " else:\n", - " print_Color('- Fitting ImageDataGenerator...', ['yellow'])\n", - " IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n", - " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", - " pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb'))\n", - " print_Color('- ImageDataGenerator fit done.', ['yellow']) \n", - "\n", - " print_Color('- Augmenting Image Data...', ['yellow']) \n", - " train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255,\n", - " y_SUB_train,\n", - " shuffle=False,\n", - " batch_size=len(x_SUB_train)\n", - " ).next()\n", - " print_Color('- Normalizing Image Data...', ['yellow'])\n", - " x_SUB_train = normalize_TO_RANGE(train_SUB_augmented_images[0], 0, 255)\n", - " x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 0.5) / 255\n", - " # x_SUB_train = x_SUB_train / 255\n", - " x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1)\n", - " y_SUB_train = train_SUB_augmented_images[1]\n", - " # DEBUG\n", - " if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1):\n", - " SITD = np.random.choice(subset_size, size=400, replace=False)\n", - " S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - " print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", - " save_images_to_dir(np.clip(x_SUB_train[SITD], 0, 1), y_SUB_train[SITD], S_dir)\n", - " # learning_rate_schedule_SUB\n", - " if PL_epoch == 0:\n", - " CU_LR = MIN_LR\n", - " elif epoch >= PL_epoch and CU_LR > MIN_LR:\n", - " if (CU_LR - DEC_LR) < MIN_LR:\n", - " CU_LR = MIN_LR\n", - " else:\n", - " CU_LR -= DEC_LR\n", - " if not OneCycleLr_UFTS: \n", - " learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR,\n", - " steps_per_epoch=steps_per_epoch_train_SUB,\n", - " epochs=C_subset_epoch)\n", - " #FV\n", - " print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + \"~*~*\") if not OneCycleLr_UFTS else \"~*OneCycleLr_UFTS Is ON~*\"}]~*...',\n", - " ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True)\n", - " print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", - " # Train\n", - " print_Color('Training on subset...', ['green'])\n", - " start_SUBO_time = time.time()\n", - " SUB_history = model.fit(x_SUB_train,\n", - " y_SUB_train,\n", - " epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C)\n", - " batch_size=Conf_batch_size_REV2,\n", - " validation_data=(x_test, y_test),\n", - " verbose='auto',\n", - " initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard\n", - " callbacks=[\n", - " learning_rate_schedule_SUB,\n", - " TerminateOnHighTemp_CB,\n", - " checkpoint_SUB,\n", - " early_stopping,\n", - " tensorboard_callback\n", - " ]\n", - " )\n", - " end_SUBO_time = time.time()\n", - " print_Color('Subset training done.', ['green'])\n", - " if load_SUB_BRW_LMODE == 1:\n", - " if max(SUB_history.history['val_accuracy']) > best_acc: \n", - " load_weights = True \n", - " elif min(SUB_history.history['val_loss']) < best_loss:\n", - " load_weights = True \n", - " else:\n", - " load_weights = False \n", - " else: \n", - " load_weights = True \n", - " \n", - " if load_SUB_BRW and load_weights:\n", - " print_Color('Loading the best weights...', ['yellow'])\n", - " # Get the filename of the best weights file\n", - " list_of_files = glob.glob('cache\\\\*.h5') \n", - " try:\n", - " best_weights_filename = max(list_of_files, key=os.path.getctime)\n", - " print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow'])\n", - " model.load_weights(best_weights_filename)\n", - " except Exception as Err:\n", - " print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red'])\n", - " elif load_SUB_BRW and (not load_weights):\n", - " # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_acc:.4f}}}]',\n", - " # ['yellow']) # OLD\n", - " print_Color_V2(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_acc:.4f}}}]')\n", - " all_histories.append(SUB_history.history)\n", - " checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " tf.keras.backend.clear_session() \n", - " # Evaluate the model on the test data\n", - " evaluation = model.evaluate(x_test, y_test, verbose=0)\n", - " \n", - " # Extract the loss and accuracy from the evaluation results\n", - " loss = evaluation[0]\n", - " acc = evaluation[1]\n", - " print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True)\n", - " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", - " # If the accuracy is higher than the best_acc\n", - " if acc > best_acc:\n", - " print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.')\n", - " # Update the best_acc\n", - " best_acc = acc\n", - " if SAVE_FULLM:\n", - " # Save the model\n", - " if SAVE_TYPE == 'TF':\n", - " print_Color_V2(f'Saving full model tf format...')\n", - " model.save(BEST_RSN, save_format='tf')\n", - " else:\n", - " print_Color_V2(f'Saving full model H5 format...')\n", - " model.save(f'{BEST_RSN}.h5')\n", - " model.save_weights('PAI_model_weights.h5')\n", - " else:\n", - " print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.')\n", - " \n", - " # If the loss is higher than the best_loss\n", - " if loss < best_loss:\n", - " print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.')\n", - " \n", - " # Update the best_acc\n", - " best_loss = loss\n", - " \n", - " if SAVE_FULLM:\n", - " # Save the model\n", - " if SAVE_TYPE == 'TF':\n", - " print_Color_V2(f'Saving full model tf format...')\n", - " model.save(BEST_RSN + '_BL', save_format='tf')\n", - " else:\n", - " print_Color_V2(f'Saving full model H5 format...')\n", - " model.save(f'{BEST_RSN}_BL.h5')\n", - " model.save_weights('PAI_model_weights_BL.h5')\n", - " else:\n", - " print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') \n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " tf.keras.backend.clear_session() \n", - " # Epoch end\n", - " end_time = time.time()\n", - " epoch_time = end_time - start_FULL_time\n", - " print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec')\n", - " epoch_SUB_time = end_SUBO_time - start_SUBO_time\n", - " print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec')\n", - " epoch_OTHERO_time = epoch_time - epoch_SUB_time\n", - " print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec')\n", - " print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan'])\n", - " Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard\n", - "except KeyboardInterrupt:\n", - " print('\\nKeyboardInterrupt.')\n", - "# End\n", - "try:\n", - " history = {}\n", - " for key in all_histories[0].keys():\n", - " # For each metric, concatenate the values from all histories\n", - " history[key] = np.concatenate([h[key] for h in all_histories])\n", - "except Exception as Err:\n", - " print(f'Failed to make model `history` var.\\nERROR: {Err}')\n", - " \n", - "print('Training done.\\n')\n", - "# del vars\n", - "try:\n", - " del train_SUB_datagen\n", - " del train_SUB_augmented_images\n", - "except NameError:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rev1 (⚠️deprecated⚠️)\n", - "```\n", - "Working: βœ…\n", - "Other:\n", - " + Tensorboard works.\n", - " - Can cause overfitting.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()\n", - "#CONF\n", - "Conf_batch_size = 8 \n", - "OneCycleLr_epoch = 20\n", - "Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training)\n", - "#TensorBoard conf\n", - "TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining)\n", - "# Learning rate configuration\n", - "Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr\n", - "MAX_LR = 0.0174\n", - "# First time\n", - "if Learning_rate_conf == 1:\n", - " learning_rate_start = 8e-04\n", - " learning_rate_max = 5e-03\n", - " learning_rate_min = 5e-05\n", - " learning_rate_rampup_epochs = 5\n", - " learning_rate_sustain_epochs = 1\n", - " learning_rate_exp_decay = .3\n", - " #TEMP\n", - " # learning_rate_start = 8e-04\n", - " # learning_rate_max = 1e-02\n", - " # learning_rate_min = 8e-04\n", - " # learning_rate_rampup_epochs = 5\n", - " # learning_rate_sustain_epochs = 3\n", - " # learning_rate_exp_decay = .45\n", - "# 2th time\n", - "if Learning_rate_conf == 2:\n", - " if Learning_rate_conf_SET2C == 1:\n", - " learning_rate_start = 4.10e-06\n", - " learning_rate_max = 4.10e-06\n", - " learning_rate_min = 4.10e-06\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - " \n", - " elif Learning_rate_conf_SET2C == 2:\n", - " learning_rate_start = 4e-07\n", - " learning_rate_max = 4e-07\n", - " learning_rate_min = 4e-07\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - " \n", - " elif Learning_rate_conf_SET2C == 3:\n", - " learning_rate_start = 5e-04\n", - " learning_rate_max = 5e-04\n", - " learning_rate_min = 5e-04\n", - " learning_rate_rampup_epochs = 0\n", - " learning_rate_sustain_epochs = 0\n", - " learning_rate_exp_decay = .1\n", - "# Function to build learning rate schedule\n", - "if Learning_rate_conf in [1,2]:\n", - " def build_learning_rate_fn(lr_start=learning_rate_start,\n", - " lr_max=learning_rate_max,\n", - " lr_min=learning_rate_min,\n", - " lr_rampup_epochs=learning_rate_rampup_epochs,\n", - " lr_sustain_epochs=learning_rate_sustain_epochs,\n", - " lr_exp_decay=learning_rate_exp_decay): \n", - " lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync\n", - " def learning_rate_fn(epoch):\n", - " if epoch < lr_rampup_epochs:\n", - " lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n", - " elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n", - " lr = lr_max\n", - " else:\n", - " lr = (lr_max - lr_min) *\\\n", - " lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min\n", - " return lr\n", - " return learning_rate_fn\n", - " \n", - "# Calculate steps per epoch\n", - "steps_per_epoch_train = len(x_train) // Conf_batch_size\n", - "\n", - "# Set up callbacks\n", - "class EpochEndMON(tf.keras.callbacks.Callback):\n", - " def on_epoch_end(self, epoch, logs=None):\n", - " optimizer = self.model.optimizer\n", - " if hasattr(optimizer, 'lr'):\n", - " lr = tf.keras.backend.get_value(optimizer.lr)\n", - " print(f'\\nLearning rate for epoch {epoch+1} is {lr}')\n", - " if hasattr(optimizer, 'momentum'):\n", - " momentum = tf.keras.backend.get_value(optimizer.momentum)\n", - " print(f'Momentum for epoch {epoch+1} is {momentum}')\n", - " if logs:\n", - " val_loss = logs.get('val_loss')\n", - " val_acc = logs.get('val_accuracy')\n", - " print(f'Validation loss for epoch {epoch+1} is {val_loss}')\n", - " print(f'Validation accuracy for epoch {epoch+1} is {val_acc}')\n", - "\n", - " print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`')\n", - "\n", - "# Instantiate the callback\n", - "EpochEndMON_callback = EpochEndMON()\n", - "if Learning_rate_conf in [1,2]:\n", - " learning_rate_fn = build_learning_rate_fn()\n", - " learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1)\n", - "else:\n", - " learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch)\n", - "if SAVE_TYPE == 'TF':\n", - " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", - " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", - "else:\n", - " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", - " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", - "early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True)\n", - "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", - "TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch'\n", - "tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True)\n", - "\n", - "# Train the model\n", - "print('Log dir:', log_dir)\n", - "#MInfo\n", - "print('Input Shape:', model.input_shape)\n", - "print('Output Shape:', model.output_shape)\n", - "print('Loss Function:', model.loss)\n", - "print('Training the model...\\n')\n", - "history = model.fit(x_train,\n", - " y_train,\n", - " epochs=256,\n", - " batch_size=Conf_batch_size,\n", - " validation_data=(x_test, y_test),\n", - " verbose='auto',\n", - " callbacks=[early_stopping,\n", - " tensorboard_callback,\n", - " learning_rate_schedule,\n", - " checkpoint_BVAC,\n", - " checkpoint_BVL,\n", - " EpochEndMON_callback])\n", - "print('Training done.\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving model weights\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [], - "source": [ - "Extra_EXT = '_T'\n", - "# Save the weights\n", - "print('Saving weights...')\n", - "model.save_weights('PAI_model_weights.h5')\n", - "print('Saving full model...')\n", - "if SAVE_TYPE == 'TF':\n", - " print('Saving full model tf format...')\n", - " model.save(f'PAI_model{Extra_EXT}', save_format='tf')\n", - "else:\n", - " try:\n", - " model.save(f'PAI_model{Extra_EXT}.h5')\n", - " except ValueError:\n", - " print('failed to save in .h5 format!')\n", - " print('Saving full model in tf format...')\n", - " model.save(f'PAI_model{Extra_EXT}', save_format='tf')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Garbage Collection (memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "tf.keras.backend.clear_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse model Training performance" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Save history\n", - "save_list(history, 'history\\\\model_history.pkl.gz', compress=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load history\n", - "history = load_list('history\\\\model_history.pkl.gz', compressed=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2023-12-28T07:04:52.565658900Z", - "start_time": "2023-12-28T07:04:51.032425100Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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pU9SpU6cUh0QAaQPBCcATN23aNOXKlUtjxoxJsmzOnDmaO3euxo0bJw8PD9WoUUNeXl6Kjo5WtWrVtHLlSpuZyiSpUKFC+v3331W3bt2HvjRq9uzZcnd317Jly+Tm5mZtnzRpkk2//PnzKzExUYcOHbI5M3Pvd1B5enoqU6ZMSkhIUFBQ0EPVJEkNGzZUpkyZFBUVpXTp0unChQs2l+mtXr1a//zzj+bMmWPzfUiHDh2ye1t3ZoPbt29fkmV79+61ef7TTz8pLi5OCxYssPlr+72XXUkpv1wtf/781m3duYzr7u3fWf6orl27pvnz56t58+Zq2rRpkuXdu3fXtGnTVLt2bRUqVEiS9Mcff9z3fSxYsKC1z4Nky5ZNFy9eTNJuzyViKT1OCxUqpMTERP35558qU6bMA9fZpk0bhYeH6+TJk4qKilJISIj1jImZEydO6Nq1azZnnf766y9Jss4gmBq/n8mpVq2asmXLpunTp+uDDz54pCB9r+Teq/j4eJ08efKR1pszZ07NmjVL1apVU926dbVu3Tp5e3s/0joBPDlcqgfgibpx44bmzJmjBg0aqGnTpkke3bp105UrV7RgwQJJt+/DaNq0qX766SdNmTJFt27dsrlMT7p91uD48eMaP358stu7du2aaV3Ozs6yWCw2f1E+fPiw5s2bZ9Pvzr0+X3/9tU37l19+mWR9TZo00ezZs5P9QJ2SqYql2399b9SokRYvXqyxY8cqQ4YMevnll222I9n+ZTs+Pj5JfSnh7Oys4OBgzZs3T0eOHLG27969W8uWLUvS997tXrp0KckHeEnKkCFDsoHhXuXKlVOuXLk0btw4m2mqlyxZot27dyskJMTel5SsuXPn6tq1a+ratWuyx2CDBg00e/ZsxcXFqWzZsipQoIBGjRqV5DXcee2enp6qUaOGJk6caLPf7u4j3Q4Qly5d0o4dO6xtJ0+etF4mlxIpPU7DwsLk5OSkjz76KMnZv3vPgrRo0UIWi0U9evTQwYMH7fpS5Vu3btlMVx8fH69vvvlGnp6eCgwMlJQ6v5/JSZ8+vfr06aPdu3erT58+yZ4xmjp1qjZt2mT3ugsVKqS1a9fatH377bd2TXV/P/ny5dPPP/+sGzdu6IUXXtA///zzyOsE8GRwxgnAE3XnRvSGDRsmu7xSpUrWL8O9E5CaN2+uL7/8UhERESpZsqSKFStmM6Z169b68ccf9dZbb2nVqlWqWrWqEhIStGfPHv34449atmyZypUr98C6QkJCNGLECL344ot67bXXdObMGY0ZM0aFCxe2+aAbGBioJk2aaNSoUfrnn39UqVIlrVmzxvpX9rv/oj506FCtWrVKFStW1BtvvKHixYvr/Pnz2rp1q37++WedP38+RfusVatW+uGHH7Rs2TK1bNnS5q/7VapUUbZs2dS2bVt1795dFotFU6ZMSdFlR8kZNGiQli5dqurVq6tLly66deuWvvzyS5UoUcJmP9SrV0+urq4KDQ1Vp06ddPXqVY0fP165cuVK8lf5wMBAjR07Vh9//LEKFy6sXLlyJTmjJEnp0qXTsGHD1L59e9WsWVMtWrTQ6dOnNXr0aPn6+qpXr14P9ZruNW3aNOXIkUNVqlRJdnnDhg01fvx4LVq0SI0bN9bYsWMVGhqqMmXKqH379vLy8tKePXu0a9cua6D84osvVK1aNZUtW1ZvvvmmChQooMOHD2vRokXavn27pNuXnfbp00eNGjVS9+7ddf36dY0dO1ZFihQxnSjkjpQep4ULF1a/fv00ePBgVa9eXY0bN5abm5s2b94sb29vRUZGWvt6enrqxRdf1MyZM5U1a1a7Aqq3t7eGDRumw4cPq0iRIoqOjtb27dv17bffKl26dJJS5/fzft59913t2rVLw4cP16pVq9S0aVPlyZNHp06d0rx587Rp0yatX7/e7vW+/vrreuutt9SkSRO98MIL+v3337Vs2TLlzJnzoeq8V+HChbV8+XLVqlVLwcHBWrlypfWeLQBpmINm8wPwjAoNDTXc3d2Na9eu3bdPu3btjHTp0lmn8U5MTDR8fHwMScbHH3+c7Jj4+Hhj2LBhRokSJQw3NzcjW7ZsRmBgoDFo0CDj0qVL1n6SjK5duya7jgkTJhh+fn6Gm5ub4e/vb0yaNCnZqaKvXbtmdO3a1ciePbuRMWNGIywszNi7d68hyRg6dKhN39OnTxtdu3Y1fHx8jHTp0hl58uQx6tata3z77bcp2l+GcXtKbC8vL0OSsXjx4iTLf/31V6NSpUqGh4eH4e3tbbz33nvWqdzv/o6klExHbhiGsWbNGiMwMNBwdXU1ChYsaIwbNy7Z/bBgwQKjVKlShru7u+Hr62sMGzbMmDhxoiHJOHTokLXfqVOnjJCQECNTpkyGJOvU5Ml9j5NhGEZ0dLTx/PPPG25ubkb27NmNli1bGseOHbPp07ZtWyNDhgxJ9kVydd7t9OnThouLi9G6dev79rl+/bqRPn16o1GjRta2devWGS+88IKRKVMmI0OGDEapUqWML7/80mbcH3/8YTRq1MjImjWr4e7ubhQtWtTo37+/TZ/ly5cbAQEBhqurq1G0aFFj6tSp952O/FGPU8MwjIkTJ1r3ZbZs2YyaNWtavwLgbj/++KMhyXjzzTfvu1/uVbNmTaNEiRLGli1bjMqVKxvu7u5G/vz5bb7H647U+P18kFmzZhn16tUzsmfPbri4uBheXl5G8+bNjdWrV1v73Jk6/M73v92R3HGYkJBg9OnTx8iZM6eRPn16Izg42Ni/f/99pyNPyTrv/R4nwzCM3377zciUKZNRo0YN068nAOB4FsN4yD9LAgCstm/frueff15Tp061uQcJ+C+YP3++wsLCtHbtWlWvXj1FY2rVqqVz586Z3tsFAE8L7nECADvduHEjSduoUaPk5ORkM0ED8F8xfvx4FSxYUNWqVXN0KQCQZnGPEwDY6dNPP1VsbKxq164tFxcXLVmyREuWLNGbb76ZZGplIC2bMWOGduzYoUWLFmn06NGpOusdADxtuFQPAOy0YsUKDRo0SH/++aeuXr2q5557Tq1bt1a/fv3k4sLfo/DfYbFYlDFjRjVv3lzjxo2z6/jlUj0AzxqCEwAAAACY4B4nAAAAADBBcAIAAAAAE8/cxfiJiYk6ceKEMmXKxE2wAAAAwDPMMAxduXJF3t7ecnJ68DmlZy44nThxglmvAAAAAFgdPXpU+fLle2CfZy44ZcqUSdLtnZM5c2YHVwMAAADAUS5fviwfHx9rRniQZy443bk8L3PmzAQnAAAAACm6hYfJIQAAAADABMEJAAAAAEwQnAAAAADAxDN3jxMAAMDjYhiGbt26pYSEBEeXAuD/pUuXTs7Ozo+8HoITAABAKoiPj9fJkyd1/fp1R5cC4C4Wi0X58uVTxowZH2k9BCcAAIBHlJiYqEOHDsnZ2Vne3t5ydXVN0SxdAB4vwzB09uxZHTt2TH5+fo905ongBAAA8Iji4+OVmJgoHx8fpU+f3tHlALiLp6enDh8+rJs3bz5ScGJyCAAAgFTi5MRHKyCtSa2zv/x2AwAAAIAJghMAAAAAmOAeJwAAgMfE9/1FT3R7h4eGPNHtAc8SzjgBAAA84zZs2CBnZ2eFhDxbwatTp05ydnbWzJkzHV0K/gMITgAAAM+4CRMm6O2339batWt14sSJx7qtO18S7GjXr1/XjBkz9N5772nixImOLkfx8fGOLgEmCE4AAADPsKtXryo6OlqdO3dWSEiIJk+ebF322muvqXnz5jb9b968qZw5c+qHH36QdPs7rCIjI1WgQAF5eHiodOnSmjVrlrX/6tWrZbFYtGTJEgUGBsrNzU3r1q3TgQMH9PLLLyt37tzKmDGjypcvr59//tlmWydPnlRISIg8PDxUoEABRUVFydfXV6NGjbL2uXjxol5//XV5enoqc+bMqlOnjn7//XfT1z1z5kwVL15c77//vtauXaujR4/aLI+Li1OfPn3k4+MjNzc3FS5cWBMmTLAu37Vrlxo0aKDMmTMrU6ZMql69ug4cOCBJqlWrlnr27GmzvrCwMLVr18763NfXV4MHD1abNm2UOXNmvfnmm5KkPn36qEiRIkqfPr0KFiyo/v376+bNmzbr+umnn1S+fHm5u7srZ86catSokSTpo48+UkBAQJLXWqZMGfXv3990n+DBCE4AAADPsB9//FH+/v4qWrSoWrVqpYkTJ8owDElSy5Yt9dNPP+nq1avW/suWLdP169etH9YjIyP1ww8/aNy4cdq1a5d69eqlVq1aac2aNTbbef/99zV06FDt3r1bpUqV0tWrV1W/fn3FxMRo27ZtevHFFxUaGqojR45Yx7Rp00YnTpzQ6tWrNXv2bH377bc6c+aMzXpfeeUVnTlzRkuWLFFsbKzKli2runXr6vz58w983RMmTFCrVq2UJUsWvfTSSzaB8c62p0+fri+++EK7d+/WN998o4wZM0qSjh8/rho1asjNzU0rV65UbGysOnToYPeZtM8//1ylS5fWtm3brMEmU6ZMmjx5sv7880+NHj1a48eP18iRI61jFi1apEaNGql+/fratm2bYmJiVKFCBUlShw4dtHv3bm3evNnaf9u2bdqxY4fat29vV21IymLc+c14Rly+fFlZsmTRpUuXlDlzZkeXAwAAngL//vuvDh06pAIFCsjd3d3a/l+YHKJq1apq1qyZevTooVu3bsnLy0szZ85UrVq1rM9HjBih1q1bS7p9FioxMVEzZsxQXFycsmfPrp9//lmVK1e2rvP111/X9evXFRUVpdWrV6t27dqaN2+eXn755QfWEhAQoLfeekvdunXTnj17VKxYMW3evFnlypWTJO3fv19+fn4aOXKkevbsqXXr1ikkJERnzpyRm5ubdT2FCxfWe++9Zz2Lc699+/apRIkSOnHihHLmzKl58+YpPDxcBw4ckMVi0V9//aWiRYtqxYoVCgoKSjL+gw8+0IwZM7R3716lS5cuyfJatWqpTJkyNmfGwsLClDVrVmtA8/X11fPPP6+5c+c+cJ98/vnnmjFjhrZs2SJJqlKligoWLKipU6cm279+/fry9fXV119/LUnq3r27du7cqVWrVj1wO0+z+/1+SvZlA844AQAAPKP27t2rTZs2qUWLFpIkFxcXNW/e3HpJmouLi5o1a6Zp06ZJkq5du6b58+erZcuWkm4HmevXr+uFF15QxowZrY8ffvjBetnaHXfCzx1Xr15V7969VaxYMWXNmlUZM2bU7t27rWec9u7dKxcXF5UtW9Y6pnDhwsqWLZv1+e+//66rV68qR44cNts/dOhQku3fbeLEiQoODlbOnDkl3Q4bly5d0sqVKyVJ27dvl7Ozs2rWrJns+O3bt6t69erJhiZ73LtPJCk6OlpVq1ZVnjx5lDFjRn344Yc2Z+G2b9+uunXr3nedb7zxhqZPn65///1X8fHxioqKUocOHR6pTtzGdOQAAADPqAkTJujWrVvy9va2thmGITc3N3311VfKkiWLWrZsqZo1a+rMmTNasWKFPDw89OKLL0qS9RK+RYsWKW/evDbrvvsMkCRlyJDB5nnv3r21YsUKff755ypcuLA8PDzUtGlTuyZJuHr1qry8vLR69eoky7JmzZrsmISEBH3//fc6deqUXFxcbNonTpyounXrysPD44HbNVvu5OSkey/quvc+JSnpPtmwYYNatmypQYMGKTg4WFmyZNGMGTM0fPjwFG87NDRUbm5umjt3rlxdXXXz5k01bdr0gWOQMgQnAACAZ9CtW7f0ww8/aPjw4apXr57NsrCwME2fPl1vvfWWqlSpIh8fH0VHR2vJkiV65ZVXrGdaihcvLjc3Nx05cuS+Z2fu59dff1W7du2s90pdvXpVhw8fti4vWrSobt26pW3btikwMFDS7TNcFy5csPYpW7asNQD5+vqmaLuLFy/WlStXtG3bNjk7O1vb//jjD7Vv314XL15UyZIllZiYqDVr1iR7qV6pUqX0/fff6+bNm8medfL09NTJkyetzxMSEvTHH3+odu3aD6xt/fr1yp8/v/r162dt+/vvv5NsOyYm5r73LLm4uKht27aaNGmSXF1d9eqrr5qGLaQMwQkAHM1iSVm/Z+uWVACP2cKFC3XhwgV17NhRWbJksVnWpEkTTZgwQW+99Zak2/c1jRs3Tn/99ZfNvTKZMmVS79691atXLyUmJqpatWq6dOmSfv31V2XOnFlt27a97/b9/Pw0Z84chYaGymKxqH///kpMTLQu9/f3V1BQkN58802NHTtW6dKl0zvvvCMPDw9Z/v/fzaCgIFWuXFlhYWH69NNPVaRIEZ04ccI6gUJyl8JNmDBBISEhKl26tE178eLF1atXL02bNk1du3ZV27Zt1bptO/UZNExFigfo5PGjOn/urIJDG6l249YaNfoLvfRyE3Xs2kvliuTTxo0bVaFCBRUtWlR16tRReHi4Fi1apEKFCmnEiBG6ePGi6Xvi5+enI0eOaMaMGSpfvrwWLVqU5B6oiIgI1a1bV4UKFdKrr76qW7duafHixerTp4+1z+uvv65ixYpJuh1QkToITgDwH2PPzeYPc6M4gNSTln8HJ0yYoKCgoCShSbodnD799FPt2LFDpUqVUsuWLfXJJ58of/78qlq1qk3fwYMHy9PTU5GRkTp48KCyZs2qsmXL6oMPPnjg9keMGKEOHTqoSpUqypkzp/r06aPLly/b9Pnhhx/UsWNH1ahRQ3ny5FFkZKR27dplvcHfYrFo8eLF6tevn9q3b6+zZ88qT548qlGjhnLnzp1km6dPn9aiRYsUFRWVZJmTk5MaNWqkCRMmqGvXrho7dqzeePsdDenXWxcvnpeXdz517BYuScqaLbvGR8/XiI8j1OGVBkrn4qwyZcpY902HDh30+++/q02bNnJxcVGvXr1MzzZJUsOGDdWrVy9169ZNcXFxCgkJUf/+/TVw4EBrn1q1amnmzJkaPHiwhg4dqsyZM6tGjRo26/Hz81OVKlV0/vx5VaxY0XS7SBlm1QOeESn9sP0o/8nzgf4h97OdZ5weZj/z3jxdnsTvM+zzoFm7kHqOHTsmHx8f/fzzzw+cICE5O45dTFG/UvmyPtKYJ8GsLsMwFFo9UD27d1N4ePiTKSoNS61Z9TjjhGcGHzSAtOlhfjf5fcbT5El8OE/pNu7ezsOMSW0rV67U1atXVbJkSZ08eVLvvfeefH19k5xh+S9L7f18/p9zWrpgjs6dPcN3N6UyghMA3AcfzvEkcJwB93fz5k198MEHOnjwoDJlyqQqVapo2rRp2n36WorGP+kzQWlB7TJ+ypY9hwYMHWkzdTseHcEpDeA/zWcb7z/wZPC79mx72t7/tHoJWWoLDg5WcHBwknZ7ztI8a34/esG8Ex4KwQkAgFTCvWRPl6ctbAF4NAQnOBz/MdmPfYYngePs6fKk3k+OG9jrWTl7hv8+ghNSFf9h4kngOMOTwNkjftcA4G4EJyAV8UELAOzDv5t4mqSFmQjx+BCc8J/EX0GBtInfTTwJhK20i8vu0i7em0dHcALug/+Yn4wntZ/5QA/gSdh76rIsLv+a9uPDKf4LCFu2CE4AAACPSSkf8+/RKfUw671P+47HNBX1/B+j9Nmgvlq36+8Uj+nfq4uuXL6kUROmPZaagCeN4ATgvjhLAwBPt/uFm9WrV6t27dr65Y/Dypwli4JDG6lanRccVGXyNm9Yp9ebhVprTImXa1XQ8aN/a+mGHcqZK/djrjBt2LBhg6pVq6aqterqq+9/dHQ5/2lOji4AAAAAaZu7h4dy5PR0dBmPZOumDYr794ZeqN9QC2ZNf+zbuxkf/9i3kRITJkzQ22+/rdjfNujMqZMOrSU+jeyTh0VwAgAAwAPN/zFK1Urkt2n7dvTnqlXGT5X9ffT666/r/fffV7Pg6knGfj/uS9UN9FeNkgU1pF9v3bx507osLi5Owwf3V1C54qpYJK9ahgZp84Z11uUnjh3R2+1fVbUAX2XIkEElSpTQ4sWLdfzoEb3eLFSSVD3AV6V9sql/ry4PfA1zZ0zVS2FN1aBxc82L/t8ZtvVrVqp84Ty6fOmSTf8ePXro9eYNrc+3btqgdo1fUoXCXqpXoYSGDuij69evWZf7+vrqm1GfqV/Pt1Sl2HP6qE9PSdLIIREKrVFOFf28Vb9qGX312Sc2++DefTnw3e4aFTkwyb6cM/0HFStWTO7u7nq5VgVFf//dA1+vJF29elXR0dHq3Lmzqtd5QQtmRiXps3rFEr0WUkflC+dRzVKF1KhRI+uy+Lg4jRwSoXoVSqhcodxqUK2s5syYIin5Y2LevHmyWCzW5wMHDlSZMmX03XffqUCBAnJ3d5ckLV26VNWqVVPWrFmVI0cONWjQQAcOHLBZ17Fjx9SiRQtlz55dGTJkULly5fTbb7/p8OHDcnJy0pYtW2z6jxo1Svnz51diYqLpfnlYBCcAAADYZdHcH/Xdl8PVs+9ATV+8Ss8995zGjh2bpN/mDb/o6N+H9F30Ag0e+bXmz5xu8+G9W7du2rF1kz4d851mLV+neiEvq0vrpvr70O0P0UM+fFfxcfGaNHORdu7cqWHDhiljxozK451Xw7/9QZI0f81mxcTu0XuDIu9b75UrV7Ri0XyFNGqmSjVq6+qVy9r623pJUsVqNZUpcxb9vGSBtX9CQoKio6NVv9ErkqSjhw+pS+tXFFS/oWauWKdPv56obZs3KvLD92y288O3X6pIsQBFL1mjN3u8K0nKkCGTBo8YozkrN+q9gZGaM/0HTf3u6/vuyzx582nmlIlJ9vfXn0fqk08+0e7du/V2n/4a8/kQLZj54DNnP/74o/z9/VW0aFGFNG6medHTZBiGdfnamGUKf6O1qtV5QdFL1ujbGfNUoUIF6/J+PTtr6fzZ6jNomOat/E39h45U+vQZHrjNe+3fv1+zZ8/WnDlztH37dknStWvXFB4eri1btigmJkZOTk5q1KiRNfRcvXpVNWvW1PHjx7VgwQL9/vvveu+995SYmChfX18FBQVp0qRJNtuZNGmS2rVrJyenxxdvuMcJAADgGbY2ZpkqFc1nfe5kuR0cHmT6pPEKe7WVwpq3lCQ1rDFAy5cv17kLtmdtMmfJqr4ffyZnZ2cVKFxENerW02/r1kjv9dCRI0c0adIkLd24U7nyeEmS2r71tn5dE6P50dPU/f0BOnX8mILqN5RfsRIqmC+rChYsKOn2bG9Zst6eeCN7Dk/Te5xmzJih5woUVOGixSRJLzZsrLkzpqpsxSpydnbWiw0ba8m8WWr8amtJUkxMjC5evKigl26fcZowZqTqN2qqVq93liTlL1BIfQYNVcdXGujDIcOt2ylfpYbadupms+03e/S2/pzX5zn9fWC/li6YI30Skey+fKvne9qwdqVuXPvf2ayxw4fqnf6D1bhxY0lS0EvZdPCvvZo1bZIavtLivq97woQJatWqlSSpaq0gRVzppi0bf1X5ytUkSd99OVzBDRuryzt9rWNeqXf7TNdff/2l5Qvn6puouapUvZYkKV9+3wfu5+TEx8frhx9+kKfn/y71bNKkiU2fiRMnytPTU3/++acCAgIUFRWls2fPavPmzcqePbskqXDhwtb+r7/+ut566y2NGDFCbm5u2rp1q3bu3Kn58+fbXZ89CE7/QUyTDQAAUkv5KtXV75P/ffj398qs3377zfqBOzmHD+5T8zYdbdoqVKigxctW2LQVKuIvZ2dn6/OcuXJr354/JUk7d+5UQkKCGtYsbzPmZnycsmS9/WH5tQ6d9MkH72jD2pVqWP9FNWnSRKVK2T8P4cSJExXSqJn1eUijZurwSgO9P3iYMmTMpPphr6j1yy/cvgcoX1ZNmzZNISEh1kD2159/6K89u7R47izrOgzDUGJioo4f/VvlC+eRJJUoVSbJtpcumKPpk77R0b8P6/q1a0pIuKUMGTNZlye3LwNKB2rz+rWSpOvXr+no34c08N3uGvx+T0lSoiElJNxSxkyZ7/uaDx/Yp02bNmnu3LmSJBcXF9ULbaS5M6ZYg9PeXX+ocYu2yY7fvn27nJ2dFVip6n23kRL58+e3CU2StG/fPg0YMEC//fabzp07Zz3TdOTIEQUEBGj79u16/vnnraHpXmFhYeratavmzp2rV199VZMnT1bt2rXl6+v7SLWaITg9IwhbAAAgOR4e6fVcgYLW54XzZdWxY8dSZd0uLulsnlssFhl3XY7l7OysGYtXycnJ2aZf+gy3Lwdr3KKNqtSso7Uxy7Vz8zpFRkZq+PDhqtmodYpr+PPPP7Vx40Zt2rRJoyMHWtsTEhK0dMEcNXmtrQLKlFW+/AW0dMEcVS0Rrrlz52ry5MnWvtevX1PTlu30WvtOSdbvlfd/Z+s87rmM7ffYTfqg+5vqHP6+qtSsq4yZM2vp/DmaMv6rFNd/58zTgE9H6ZWX6kiS9py8LElycna+77i5M6bo1q1b8vb2trYZhiFXVzddGfypMmXOIrf/v+coOR4eHg+sy8nJyeayP0lJ7t2SpAwZkl7aFxoaqvz582v8+PHy9vZWYmKiAgICrJNHmG3b1dVVbdq00aRJk9S4cWNFRUVp9OjRDxyTGghOAAAAsItvQT/98ftWhTZ91dq2efNmu9bx/PPPKyEhQefPnVXZilXu2y+Pdz41a91BpfqGq2/fvho/frxqNmqtdOluh7LExAdfVjhhwgTVqFFD3fvb3gM1/8cozZ0xVU1eu33GJaTRK1o8b6YqBPjJyclJISEh2nv2hiSpWEApHdy31yZgpsT2LZvklddHb3T/3+V6J48ftemT3L7c9ftW6885PHPJM7eXjv39t/VytevuFx+43Vu3bumn2dEaPny46tWrJ+n2lzNLUq/XW2nJ/Nlq1rqD/IqV0G+/rrFeJni3kiVLKjExUbEbf7Veqne3bDly6NrVq/8/QUbW26/3/+9hepB//vlHe/fu1fjx41W9+u3LAtetW2fTp1SpUvruu+90/vz5+551ev311xUQEKCvv/5at27dsl7G+DgRnHBffIcPAABITov2b+ij93qqRKnnVbpcBS2YvEQ7duyQl09+88H/r0iRImrZsqX69eqsd/p/LP8SpXThn3Pa9Osa+RUroRp1g/XpwL6qWitI+QsW1q0zB7Vq1SoVK3b7PiWvvD6yWCxa+/MyVavzgtzd3ZU+Q0abbdy8eVNTpkzRRx99JD//4jbLGrdorSnjx2j/3t0qXLSY6oe9orEjhuqTTz5R06ZN5ebmJul2cGrfpYdaN6ynIR++q8Yt2sgjfXod/GuvNvyySh98/Nl9X2P+AgV16sQxLZk/WwGly2rtyuVauXThA/flsgVztW/PLuV9ztfap8s772vYgPdV9LncevHFF7Xv77PatWO7Ll+6qDZvdk2y3bU/L9PlSxfVsWNHZfn/yw0Ts16UJNWtH6p5M6aqWesOeqtXH7356svyyV9ALzZsrIRbt7Rk2jr16dNHvr6+Cm3aQhG9u6nPoGEqUjxAJ48f1flzZxUc2kgly5STu0d6fTlssDw/eFe//fab9SzdjmO3t3X68r/692aC9bkkJSZalDVbdn377bfy8vLSkSNH9P7779vukxYtNGTIEIWFhSkyMlJeXl7atm2bvL29VblyZUlSsWLFVKlSJfXp00cdOnQwPUuVGghOAAAAj8mOoxeSbS+VL+v/+tz1ofJBHmbM4xLSqJmO/f23RnzcX3FxcXq1eTO1a9dOq9dtsGs9kyZN0tvvfajhgz/UmVMnlS1bDpUsW0416gZLun05XeSH7+r0qRPKkjmzXnzxRY0cOVLHb0i5vbzVObyvRg8dpAHvdFVok1c1eOTXNutfsGCB/vnnHzVq1Ein77mKrKBfURX0K6q5M6bq3YhP9FyBggooE6gd22M1atQom75FigVowsyF+vLTj9W+SX0ZhiGf/L4KDm2kB6lVr75avd5ZQ/u/p/j4eFWv84Le7PGuxo0cet99Wa9BmBq+8pr+2P6/s06NW7SRu7uHJk38Wu+++67cPdLLz7+4WnbsnOx250ZPUaVqNa2h6W5BLzXU5LFf6K/df6h85Wr6bNxkfTv6M038epQyZsyk2rVqWvt+OGS4vhg2WEP69dbFi+fl5Z1PHbuFS5KyZMumIaO/0chPBqjk9B9Ut25dDRw4UG+++eYD94mTk5OGjZmgUYM/UEBAgIoWLaovvvhCtWrVsvZxdXXV8uXL9c4776h+/fq6deuWihcvrjFjxtisq2PHjlq/fr06dOjwwG2mFoITAADAM+reoHFHrVq19Ptdoe/lZq/p5Wav2fTp1PNddep5e8rtUvmy6oUXXpCPb4EHrvu9gbaXy6VLl05d3ulrM6vb3foO/tT6893B8fj/B8e7a0hOkyZNrDMEnk4mbM5dudHm+bSffrbZzt0CypTVN1Fz7rutw4cPJxtoe/X7SL36fWTTdmd2vjvufR2dXmtksy8lqX6jV/T+229IMg/OX06acd9lJZ8PtHlvg14KVdBLodbnd79+N3d3vRvxid6N+CTZddV5MUR1XgyxGVPxpVesP3cOf1+dw99PMq5S9Vr6888/bdruvV8qf/78mjVrlh7k+PHjKlmypMqXL//AfqmF4AQAAAC73LhxXTOnTFKVmnXk7Oys2eMX6eeff9Y3UXMdXdp/zr37csn82dr4y2r25QNcvXpVhw8f1ldffaWPP/74iW2X4AQAAAC7WGTRulUr9N2XwxUXF6di/kU1e/ZsFa5Qy9Gl/efcuy99CxXW8G9/SHZCBtzWrVs3TZ8+XWFhYU/sMj2J4AQAAAA7uXt46Nvp86zP71yq5eh7r/6L7t2XMDd58mSb6eKfFKcnvkUAAAAA+I8hOAEAAKSWe25wB+B490488bAITgAAAI/ozpexGrfiHVwJgHvFx9/+vXR2dn6k9XCPEwAAwCNydnZW1qxZtefIKWXLLllcXCWL5b79//33X+vPKQ1bjzLGnkD3JMY86df/pMawnx9uzOOUmJios2fPKn369HJxebToQ3ACAABIBXny5NHny/eqbsEEpXO2SLp/cHK94WH9+cyFGyla/6OMSWn/JzXmSb/+JzWG/fxwYx43JycnPffcc7I84I8ZKUFwAgAASAUWi0Wzd1/Ton3Xlc3dSU4P+IwW804t68+vz1mdovU/ypiU9n9SY570639SY9jPDzfmcXN1dZWT06PfoURwAgAASEX/3jJ08mrCA/u4u7tbfz5+5cF9U2NMSvs/qTFP+vU/qTHs54cb81/B5BAAAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmHB6cxowZI19fX7m7u6tixYratGnTA/uPGjVKRYsWlYeHh3x8fNSrVy/9+++/T6haAAAAAM8ihwan6OhohYeHKyIiQlu3blXp0qUVHBysM2fOJNs/KipK77//viIiIrR7925NmDBB0dHR+uCDD55w5QAAAACeJQ4NTiNGjNAbb7yh9u3bq3jx4ho3bpzSp0+viRMnJtt//fr1qlq1ql577TX5+vqqXr16atGihelZKgAAAAB4FA4LTvHx8YqNjVVQUND/inFyUlBQkDZs2JDsmCpVqig2NtYalA4ePKjFixerfv36991OXFycLl++bPMAAAAAAHu4OGrD586dU0JCgnLnzm3Tnjt3bu3ZsyfZMa+99prOnTunatWqyTAM3bp1S2+99dYDL9WLjIzUoEGDUrV2AAAAAM8Wh08OYY/Vq1dryJAh+vrrr7V161bNmTNHixYt0uDBg+87pm/fvrp06ZL1cfTo0SdYMQAAAICngcPOOOXMmVPOzs46ffq0Tfvp06eVJ0+eZMf0799frVu31uuvvy5JKlmypK5du6Y333xT/fr1k5NT0hzo5uYmNze31H8BAAAAAJ4ZDjvj5OrqqsDAQMXExFjbEhMTFRMTo8qVKyc75vr160nCkbOzsyTJMIzHVywAAACAZ5rDzjhJUnh4uNq2baty5cqpQoUKGjVqlK5du6b27dtLktq0aaO8efMqMjJSkhQaGqoRI0bo+eefV8WKFbV//371799foaGh1gAFAAAAAKnNocGpefPmOnv2rAYMGKBTp06pTJkyWrp0qXXCiCNHjticYfrwww9lsVj04Ycf6vjx4/L09FRoaKg++eQTR70EAAAAAM8AhwYnSerWrZu6deuW7LLVq1fbPHdxcVFERIQiIiKeQGUAAAAAcNt/alY9AAAAAHAEghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJu4NTRESE/v7778dRCwAAAACkSXYHp/nz56tQoUKqW7euoqKiFBcX9zjqAgAAAIA0w+7gtH37dm3evFklSpRQjx49lCdPHnXu3FmbN29+HPUBAAAAgMM91D1Ozz//vL744gudOHFCEyZM0LFjx1S1alWVKlVKo0eP1qVLl1K7TgAAAABwmEeaHMIwDN28eVPx8fEyDEPZsmXTV199JR8fH0VHR6dWjQAAAADgUA8VnGJjY9WtWzd5eXmpV69eev7557V7926tWbNG+/bt0yeffKLu3bundq0AAAAA4BB2B6eSJUuqUqVKOnTokCZMmKCjR49q6NChKly4sLVPixYtdPbs2VQtFAAAAAAcxcXeAc2aNVOHDh2UN2/e+/bJmTOnEhMTH6kwAAAAAEgr7A5O/fv3fxx1AAAAAECaZfelek2aNNGwYcOStH/66ad65ZVXUqUoAAAAAEhL7A5Oa9euVf369ZO0v/TSS1q7dq3dBYwZM0a+vr5yd3dXxYoVtWnTpgf2v3jxorp27SovLy+5ubmpSJEiWrx4sd3bBQAAAICUsvtSvatXr8rV1TVJe7p06XT58mW71hUdHa3w8HCNGzdOFStW1KhRoxQcHKy9e/cqV65cSfrHx8frhRdeUK5cuTRr1izlzZtXf//9t7JmzWrvywAAAACAFHuoWfWS+46mGTNmqHjx4nata8SIEXrjjTfUvn17FS9eXOPGjVP69Ok1ceLEZPtPnDhR58+f17x581S1alX5+vqqZs2aKl26tL0vAwAAAABS7KEmh2jcuLEOHDigOnXqSJJiYmI0ffp0zZw5M8XriY+PV2xsrPr27Wttc3JyUlBQkDZs2JDsmAULFqhy5crq2rWr5s+fL09PT7322mvq06ePnJ2dkx0TFxenuLg463N7z4oBAAAAgN1nnEJDQzVv3jzt379fXbp00TvvvKNjx47p559/VlhYWIrXc+7cOSUkJCh37tw27blz59apU6eSHXPw4EHNmjVLCQkJWrx4sfr376/hw4fr448/vu92IiMjlSVLFuvDx8cnxTUCAAAAgPQQZ5wkKSQkRCEhIaldi6nExETlypVL3377rZydnRUYGKjjx4/rs88+U0RERLJj+vbtq/DwcOvzy5cvE54AAAAA2OWhglNqyJkzp5ydnXX69Gmb9tOnTytPnjzJjvHy8lK6dOlsLssrVqyYTp06pfj4+GQnrXBzc5Obm1vqFg8AAADgmWL3pXoJCQn6/PPPVaFCBeXJk0fZs2e3eaSUq6urAgMDFRMTY21LTExUTEyMKleunOyYqlWrav/+/UpMTLS2/fXXX/Ly8ko2NAEAAABAarA7OA0aNEgjRoxQ8+bNdenSJYWHh6tx48ZycnLSwIED7VpXeHi4xo8fr++//167d+9W586dde3aNbVv316S1KZNG5vJIzp37qzz58+rR48e+uuvv7Ro0SINGTJEXbt2tfdlAAAAAECK2X2p3rRp0zR+/HiFhIRo4MCBatGihQoVKqRSpUpp48aN6t69e4rX1bx5c509e1YDBgzQqVOnVKZMGS1dutQ6YcSRI0fk5PS/bOfj46Nly5apV69eKlWqlPLmzasePXqoT58+9r4MAAAAAEgxu4PTqVOnVLJkSUlSxowZdenSJUlSgwYN1L9/f7sL6Natm7p165bsstWrVydpq1y5sjZu3Gj3dgAAAADgYdl9qV6+fPl08uRJSVKhQoW0fPlySdLmzZuZhAEAAADAU8nu4NSoUSPrhA5vv/22+vfvLz8/P7Vp00YdOnRI9QIBAAAAwNHsvlRv6NCh1p+bN2+u/Pnza/369fLz81NoaGiqFgcAAAAAaYFdwenmzZvq1KmT+vfvrwIFCkiSKlWqpEqVKj2W4gAAAAAgLbDrUr106dJp9uzZj6sWAAAAAEiT7L7HKSwsTPPmzXsMpQAAAABA2mT3PU5+fn766KOP9OuvvyowMFAZMmSwWW7P9zgBAAAAwH+B3cFpwoQJypo1q2JjYxUbG2uzzGKxEJwAAAAAPHXsDk6HDh16HHUAAAAAQJpl9z1OAAAAAPCssfuMk9mX3E6cOPGhiwEAAACAtMju4HThwgWb5zdv3tQff/yhixcvqk6dOqlWGAAAAACkFXYHp7lz5yZpS0xMVOfOnVWoUKFUKQoAAAAA0pJUucfJyclJ4eHhGjlyZGqsDgAAAADSlFSbHOLAgQO6detWaq0OAAAAANIMuy/VCw8Pt3luGIZOnjypRYsWqW3btqlWGAAAAACkFXYHp23bttk8d3Jykqenp4YPH2464x4AAAAA/BfZHZxWrVr1OOoAAAAAgDTL7nucDh06pH379iVp37dvnw4fPpwaNQEAAABAmmJ3cGrXrp3Wr1+fpP23335Tu3btUqMmAAAAAEhT7A5O27ZtU9WqVZO0V6pUSdu3b0+NmgAAAAAgTbE7OFksFl25ciVJ+6VLl5SQkJAqRQEAAABAWmJ3cKpRo4YiIyNtQlJCQoIiIyNVrVq1VC0OAAAAANICu2fVGzZsmGrUqKGiRYuqevXqkqRffvlFly9f1sqVK1O9QAAAAABwNLvPOBUvXlw7duxQs2bNdObMGV25ckVt2rTRnj17FBAQ8DhqBAAAAACHsvuMkyR5e3tryJAhqV0LAAAAAKRJdp9xmjRpkmbOnJmkfebMmfr+++9TpSgAAAAASEvsDk6RkZHKmTNnkvZcuXJxFgoAAADAU8nu4HTkyBEVKFAgSXv+/Pl15MiRVCkKAAAAANISu4NTrly5tGPHjiTtv//+u3LkyJEqRQEAAABAWmJ3cGrRooW6d++uVatWKSEhQQkJCVq5cqV69OihV1999XHUCAAAAAAOZfeseoMHD9bhw4dVt25dubjcHp6YmKg2bdrok08+SfUCAQAAAMDR7A5Orq6uio6O1scff6zt27fLw8NDJUuWVP78+R9HfQAAAADgcA/1PU6S5OfnJz8/P0nS5cuXNXbsWE2YMEFbtmxJteIAAAAAIC146OAkSatWrdLEiRM1Z84cZcmSRY0aNUqtugAAAAAgzbA7OB0/flyTJ0/WpEmTdPHiRV24cEFRUVFq1qyZLBbL46gRAAAAABwqxbPqzZ49W/Xr11fRokW1fft2DR8+XCdOnJCTk5NKlixJaAIAAADw1ErxGafmzZurT58+io6OVqZMmR5nTQAAAACQpqT4jFPHjh01ZswYvfjiixo3bpwuXLjwOOsCAAAAgDQjxcHpm2++0cmTJ/Xmm29q+vTp8vLy0ssvvyzDMJSYmPg4awQAAAAAh0pxcJIkDw8PtW3bVmvWrNHOnTtVokQJ5c6dW1WrVtVrr72mOXPmPK46AQAAAMBh7ApOd/Pz89OQIUN09OhRTZ06VdevX1eLFi1SszYAAAAASBMe6XucJMnJyUmhoaEKDQ3VmTNnUqMmAAAAAEhTHvqMU3Jy5cqVmqsDAAAAgDQhVYMTAAAAADyNCE4AAAAAYILgBAAAAAAmHio4Xbx4Ud9995369u2r8+fPS5K2bt2q48ePp2pxAAAAAJAW2D2r3o4dOxQUFKQsWbLo8OHDeuONN5Q9e3bNmTNHR44c0Q8//PA46gQAAAAAh7H7jFN4eLjatWunffv2yd3d3dpev359rV27NlWLAwAAAIC0wO7gtHnzZnXq1ClJe968eXXq1KlUKQoAAAAA0hK7g5Obm5suX76cpP2vv/6Sp6dnqhQFAAAAAGmJ3cGpYcOG+uijj3Tz5k1JksVi0ZEjR9SnTx81adIk1QsEAAAAAEezOzgNHz5cV69eVa5cuXTjxg3VrFlThQsXVqZMmfTJJ588jhoBAAAAwKHsnlUvS5YsWrFihdatW6cdO3bo6tWrKlu2rIKCgh5HfQAAAADgcHYHpzuqVaumatWqpWYtAAAAAJAm2R2cvvjii2TbLRaL3N3dVbhwYdWoUUPOzs6PXBwAAAAApAV2B6eRI0fq7Nmzun79urJlyyZJunDhgtKnT6+MGTPqzJkzKliwoFatWiUfH59ULxgAAAAAnjS7J4cYMmSIypcvr3379umff/7RP//8o7/++ksVK1bU6NGjdeTIEeXJk0e9evV6HPUCAAAAwBNn9xmnDz/8ULNnz1ahQoWsbYULF9bnn3+uJk2a6ODBg/r000+ZmhwAAADAU8PuM04nT57UrVu3krTfunVLp06dkiR5e3vrypUrj14dAAAAAKQBdgen2rVrq1OnTtq2bZu1bdu2bercubPq1KkjSdq5c6cKFCiQelUCAAAAgAPZHZwmTJig7NmzKzAwUG5ubnJzc1O5cuWUPXt2TZgwQZKUMWNGDR8+PNWLBQAAAABHsPsepzx58mjFihXas2eP/vrrL0lS0aJFVbRoUWuf2rVrp16FAAAAAOBgD/0FuP7+/vL390/NWgAAAAAgTXqo4HTs2DEtWLBAR44cUXx8vM2yESNGpEphAAAAAJBW2B2cYmJi1LBhQxUsWFB79uxRQECADh8+LMMwVLZs2cdRIwAAAAA4lN2TQ/Tt21e9e/fWzp075e7urtmzZ+vo0aOqWbOmXnnllcdRIwAAAAA4lN3Baffu3WrTpo0kycXFRTdu3FDGjBn10UcfadiwYaleIAAAAAA4mt3BKUOGDNb7mry8vHTgwAHrsnPnzqVeZQAAAACQRth9j1OlSpW0bt06FStWTPXr19c777yjnTt3as6cOapUqdLjqBEAAAAAHMru4DRixAhdvXpVkjRo0CBdvXpV0dHR8vPzY0Y9AAAAAE8lu4JTQkKCjh07plKlSkm6fdneuHHjHkthAAAAAJBW2HWPk7Ozs+rVq6cLFy4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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "import seaborn as sns\n", - "\n", - "# Chunk size for 3D plot\n", - "chunk_size = 6 # Change this to your desired chunk size\n", - " \n", - "def convert_history(history):\n", - " if isinstance(history, tf.keras.callbacks.History):\n", - " return history.history\n", - " else:\n", - " return history\n", - " \n", - "def chunked_data(data, chunk_size):\n", - " return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n", - "\n", - "\n", - "try:\n", - " EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' \n", - " history = convert_history(history)\n", - "\n", - " # Calculate deltas\n", - " delta_loss = np.diff(history['loss'])\n", - " delta_accuracy = np.diff(history['accuracy'])\n", - "\n", - " try:\n", - " delta_val_loss = np.diff(history['val_loss'])\n", - " delta_val_accuracy = np.diff(history['val_accuracy'])\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss or val_accuracy for delta calculation.')\n", - "\n", - " plt.figure(figsize=(16, 10))\n", - " # Loss\n", - " plt.subplot(2, 2, 1)\n", - " plt.plot(history['loss'], label='loss')\n", - " try:\n", - " plt.plot(history['val_loss'], label='val_loss', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss.')\n", - " plt.title('Model Loss')\n", - " plt.ylabel('Loss')\n", - " plt.xlabel(EPM)\n", - " plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", - " plt.grid(True)\n", - " \n", - " # Density plot for loss\n", - " plt.subplot(2, 2, 2)\n", - " plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100)\n", - " try:\n", - " plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100)\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_loss (density plot).')\n", - " plt.title('Density Plot for Loss')\n", - " plt.xlabel('Loss')\n", - " plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", - " plt.grid(True)\n", - " \n", - " \n", - " # Accuracy\n", - " plt.subplot(2, 2, 3)\n", - " plt.plot(history['accuracy'], label='accuracy')\n", - " try:\n", - " plt.plot(history['val_accuracy'], label='val_accuracy', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_accuracy.')\n", - " plt.title('Model Accuracy')\n", - " plt.ylabel('Accuracy')\n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - " \n", - " # Density plot for accuracy\n", - " plt.subplot(2, 2, 4)\n", - " plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40)\n", - " try:\n", - " plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40)\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load val_accuracy (density plot).')\n", - " plt.title('Density Plot for Accuracy')\n", - " plt.xlabel('Accuracy')\n", - " plt.grid(True)\n", - "\n", - " # Delta Loss\n", - " plt.figure(figsize=(14, 8))\n", - " plt.subplot(2, 2, 1)\n", - " plt.plot(delta_loss, label='delta_loss')\n", - " try:\n", - " plt.plot(delta_val_loss, label='delta_val_loss', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load delta_val_loss.')\n", - " plt.title('Delta Model Loss')\n", - " plt.ylabel('Delta Loss')\n", - " plt.ylim(top=1.5, bottom=-1.5) \n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - " # Delta Accuracy\n", - " plt.subplot(2, 2, 2)\n", - " plt.plot(delta_accuracy, label='delta_accuracy')\n", - " try:\n", - " plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange')\n", - " except (ValueError, NameError):\n", - " print('\\033[91mfailed to load delta_val_accuracy.')\n", - " plt.title('Delta Model Accuracy')\n", - " plt.ylabel('Delta Accuracy')\n", - " plt.xlabel(EPM)\n", - " plt.grid(True)\n", - "\n", - " # Calculate chunked data\n", - " chunked_loss = chunked_data(history['val_loss'], chunk_size)\n", - " chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size)\n", - "\n", - " # Clip the loss values to a maximum of max(history['val_loss'][10:])\n", - " max_loss = max(history['val_loss'][10:])\n", - " chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss)\n", - "\n", - " # Create 3D surface plots for each chunk\n", - " fig = plt.figure(figsize=(14, 8))\n", - " ax = fig.add_subplot(121, projection='3d')\n", - " X = np.arange(len(chunked_loss))\n", - " Y = np.arange(chunk_size)\n", - " X, Y = np.meshgrid(X, Y)\n", - " Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y\n", - " ax.plot_surface(X, Y, Z, cmap='viridis')\n", - " ax.set_title('3D Surface Plot of Chunked Loss')\n", - " ax.set_xlabel('Chunk Index')\n", - " ax.set_ylabel('Epoch')\n", - " ax.set_zlabel('Loss')\n", - "\n", - " ax = fig.add_subplot(122, projection='3d')\n", - " X = np.arange(len(chunked_accuracy))\n", - " Y = np.arange(chunk_size)\n", - " X, Y = np.meshgrid(X, Y)\n", - " Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y\n", - " ax.plot_surface(X, Y, Z, cmap='viridis')\n", - " ax.set_title('3D Surface Plot of Chunked Accuracy')\n", - " ax.set_xlabel('Chunk Index')\n", - " ax.set_ylabel('Epoch')\n", - " ax.set_zlabel('Accuracy')\n", - "\n", - " # Function to calculate the average of chunks\n", - " def chunked_average(values, chunk_size):\n", - " return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)]\n", - "\n", - " avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size)\n", - " avg_loss_chunks = chunked_average(history['val_loss'], chunk_size)\n", - "\n", - " # Find the chunk with the highest average accuracy\n", - " max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n", - " max_acc_value = avg_accuracy_chunks[max_acc_chunk_index]\n", - "\n", - " # Create a pile plot for accuracy\n", - " plt.figure(figsize=(10, 6))\n", - " plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy')\n", - " plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy')\n", - " plt.xlabel('Chunk')\n", - " plt.ylabel('Average Accuracy')\n", - " plt.title('Average Validation Accuracy per Chunk')\n", - " plt.legend()\n", - "\n", - " # Create a pile plot for loss\n", - " plt.figure(figsize=(10, 6))\n", - " plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss')\n", - " plt.xlabel('Chunk')\n", - " plt.ylabel('Average Loss')\n", - " plt.title('Average Validation Loss per Chunk')\n", - " plt.legend()\n", - "\n", - " # Function to calculate the average of each epoch across chunks, ignoring the first chunk\n", - " def average_across_chunks(values, chunk_size):\n", - " num_chunks = len(values) // chunk_size\n", - " avg_values = []\n", - " for epoch in range(chunk_size):\n", - " epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)]\n", - " avg_values.append(np.mean(epoch_values))\n", - " return avg_values\n", - "\n", - " # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk\n", - " avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size)\n", - " avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size)\n", - "\n", - " # Create a bar plot for average accuracy and loss of each epoch across chunks\n", - " plt.figure(figsize=(12, 6))\n", - "\n", - " # Create an index for each epoch\n", - " epoch_indices = np.arange(len(avg_accuracy_epochs))\n", - "\n", - " # Plot accuracy and loss as bars\n", - " plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6)\n", - " plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6)\n", - "\n", - " # Add labels and title\n", - " plt.xlabel('Epoch (within chunk)')\n", - " plt.ylabel('Average Value')\n", - " plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)')\n", - " plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers\n", - " plt.legend()\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "except (ValueError, NameError) as E:\n", - " print(f'\\033[91mFailed to load model history.\\nError: {E}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse model Predicting performance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradcam heatmap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### V2" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_heatmap(model, img_array, conv_layer_name, pred_index):\n", - " \"\"\"\n", - " Helper function to compute the heatmap for a given convolutional layer.\n", - " \"\"\"\n", - " grad_model = tf.keras.models.Model(\n", - " [model.inputs], \n", - " [model.get_layer(conv_layer_name).output, model.output]\n", - " )\n", - "\n", - " with tf.GradientTape() as tape:\n", - " conv_layer_output, preds = grad_model(img_array)\n", - " class_channel = preds[:, pred_index]\n", - "\n", - " grads = tape.gradient(class_channel, conv_layer_output)\n", - " pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n", - "\n", - " conv_layer_output = conv_layer_output[0]\n", - " heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis]\n", - " heatmap = tf.squeeze(heatmap)\n", - " heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n", - " return heatmap\n", - "\n", - "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0):\n", - " \"\"\"\n", - " Function to compute the Grad-CAM heatmap for a specific class, given an input image.\n", - " \"\"\"\n", - " if pred_index is None:\n", - " preds = model.predict(img_array)\n", - " pred_index = tf.argmax(preds[0])\n", - "\n", - " # Compute heatmap for the last convolutional layer\n", - " heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index)\n", - " \n", - " # Apply threshold and adjust sensitivity\n", - " heatmap = np.where(heatmap > threshold, heatmap, 0)\n", - " heatmap = heatmap ** sensitivity_map\n", - "\n", - " if second_last_conv_layer_name is not None:\n", - " # Compute heatmap for the second last convolutional layer\n", - " heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)\n", - " \n", - " # Apply threshold and adjust sensitivity\n", - " heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)\n", - " heatmap_second = heatmap_second ** sensitivity_map\n", - " \n", - " # Average the two heatmaps\n", - " heatmap = (heatmap + heatmap_second) / 2.0\n", - " \n", - " return heatmap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Main test" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "notebookRunGroups": { - "groupValue": "" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 2s 2s/step\n", - "20/20 [==============================] - 2s 94ms/step\n", - "The accuracy of the model on validation data is 93.75%(93.75000%)\n", - "The accuracy of the model on test data is 97.12%(97.11538%)\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Predicting: 1%| | 1/156 [00:00<00:49, 3.12dpb/s]Exception ignored in: \n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\framework\\c_api_util.py\", line 74, in __del__\n", - " self.deleter(obj)\n", - "KeyboardInterrupt: \n", - "Predicting: 17%|β–ˆβ–‹ | 27/156 [00:33<02:38, 1.23s/dpb]\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[16], line 136\u001b[0m\n\u001b[0;32m 133\u001b[0m y_test_subset \u001b[38;5;241m=\u001b[39m y_test[indices]\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# Make predictions on the subset of test data\u001b[39;00m\n\u001b[1;32m--> 136\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test_subset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 137\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(test_predictions, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 138\u001b[0m y_test_original_subset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_test_subset, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\training.py:2253\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 2251\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[0;32m 2252\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m-> 2253\u001b[0m tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m 2255\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:954\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m 952\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m 953\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 954\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[0;32m 956\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 957\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m 2494\u001b[0m (graph_function,\n\u001b[0;32m 2495\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2497\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1860\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1861\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1863\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1865\u001b[0m args,\n\u001b[0;32m 1866\u001b[0m possible_gradient_type,\n\u001b[0;32m 1867\u001b[0m executing_eagerly)\n\u001b[0;32m 1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n", - "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "import seaborn as sns\n", - "from sklearn.metrics import confusion_matrix, accuracy_score\n", - "from scipy.stats import binom\n", - "from tqdm import tqdm\n", - "import efficientnet.tfkeras\n", - "import cv2\n", - "import gc\n", - "# Garbage Collection (memory)\n", - "gc.collect()\n", - "\n", - "Extra_EXT = '_T' # _T or _T_BL\n", - "prob_L = 0.9995\n", - "tick_spacing = 5\n", - "Train_data_test = False\n", - "if SAVE_TYPE == 'TF':\n", - " # Load the pre-trained model\n", - " model = load_model(f'PAI_model{Extra_EXT}')\n", - "else:\n", - " # Load the pre-trained model\n", - " model = load_model(f'PAI_model{Extra_EXT}.h5')\n", - "\n", - "# Ensure the model's input_shape matches your data\n", - "assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.'\n", - "\n", - "# Make predictions on validation data\n", - "val_predictions = model.predict(x_val)\n", - "val_predictions = np.argmax(val_predictions, axis=1)\n", - "\n", - "# Make predictions on Train data\n", - "if Train_data_test:\n", - " Train_predictions = model.predict(x_train)\n", - " Train_predictions = np.argmax(Train_predictions, axis=1)\n", - "\n", - "# Make predictions on test data\n", - "test_predictions = model.predict(x_test)\n", - "test_predictions = np.argmax(test_predictions, axis=1)\n", - "\n", - "# Convert y_val and y_test from one-hot encoder to their original form\n", - "y_val_original = np.argmax(y_val, axis=1)\n", - "y_test_original = np.argmax(y_test, axis=1)\n", - "if Train_data_test:\n", - " y_train_original = np.argmax(y_train, axis=1)\n", - "\n", - "# Calculate accuracy on validation data\n", - "val_accuracy = accuracy_score(y_val_original, val_predictions)\n", - "\n", - "# Calculate accuracy on Train data\n", - "if Train_data_test:\n", - " Train_accuracy = accuracy_score(y_val_original, Train_predictions)\n", - "\n", - "# Calculate accuracy on test data\n", - "test_accuracy = accuracy_score(y_test_original, test_predictions)\n", - "\n", - "# Print acc\n", - "if Train_data_test:\n", - " print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}({Train_accuracy:.5%})')\n", - "print(f'The accuracy of the model on validation data is {val_accuracy:.2%}({val_accuracy:.5%})')\n", - "print(f'The accuracy of the model on test data is {test_accuracy:.2%}({test_accuracy:.5%})')\n", - "\n", - "# Visualize the predictions on validation data as a grid of squares\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 5, i+1)\n", - " plt.imshow(x_val[i])\n", - " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", - " plt.axis('off')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "#Heatmap\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 5, i+1)\n", - " img = x_val[i]\n", - " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) \n", - " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", - " heatmap = np.uint8(255 * heatmap)\n", - " # Apply Adaptive Histogram Equalization\n", - " clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n", - " heatmap = clahe.apply(heatmap)\n", - " heatmap = cv2.applyColorMap(np.max(heatmap) - heatmap, cv2.COLORMAP_JET)\n", - " if RANGE_NOM:\n", - " superimposed_img = (heatmap / 255) * 0.4 + img \n", - " else:\n", - " superimposed_img = (heatmap / 255) * 0.4 + (img / 255)\n", - " #clip\n", - " superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1]\n", - " plt.imshow(superimposed_img)\n", - " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", - " plt.axis('off')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Define the list of labels\n", - "labels = ['NORMAL', 'PNEUMONIA']\n", - "\n", - "# Create a confusion matrix for validation data\n", - "val_cm = confusion_matrix(y_val_original, val_predictions)\n", - "\n", - "# Create a confusion matrix for test data\n", - "test_cm = confusion_matrix(y_test_original, test_predictions)\n", - "\n", - "# Plot the confusion matrix as a heatmap for validation data\n", - "plt.figure(figsize=(8, 6))\n", - "sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", - "plt.title('Confusion Matrix - Validation Data')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('True')\n", - "plt.show()\n", - "\n", - "# Plot the confusion matrix as a heatmap for test data\n", - "plt.figure(figsize=(8, 6))\n", - "sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", - "plt.title('Confusion Matrix - Test Data')\n", - "plt.xlabel('Predicted')\n", - "plt.ylabel('True')\n", - "plt.show()\n", - "\n", - "# Define the range of test data sizes to use\n", - "data_sizes = range(1, len(x_test), 4) \n", - "# Calculate the probability of a wrong prediction based on test accuracy\n", - "prob_wrong = 1 - test_accuracy\n", - "\n", - "# Create a list to store the number of incorrect predictions for each test data size\n", - "incorrect_predictions = []\n", - "\n", - "# Generate predictions and track incorrect predictions for each data size\n", - "for size in tqdm(data_sizes, desc='Predicting', unit='dpb'):\n", - " # Garbage Collection (memory)\n", - " gc.collect()\n", - " # Randomly select a subset of test data\n", - " indices = np.random.choice(len(x_test), size, replace=False)\n", - " x_test_subset = x_test[indices]\n", - " y_test_subset = y_test[indices]\n", - "\n", - " # Make predictions on the subset of test data\n", - " test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False)\n", - " test_predictions = np.argmax(test_predictions, axis=1)\n", - " y_test_original_subset = np.argmax(y_test_subset, axis=1)\n", - "\n", - " # Calculate the number of incorrect predictions\n", - " incorrect_preds = np.sum(test_predictions != y_test_original_subset)\n", - " incorrect_predictions.append(incorrect_preds)\n", - " \n", - "# Plot the number of incorrect predictions vs. the number of data points\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(data_sizes, incorrect_predictions)\n", - "plt.xlabel('Number of Data Points')\n", - "plt.ylabel('Number of Incorrect Predictions')\n", - "# Add gridlines for the x and y axes\n", - "plt.grid(True)\n", - "\n", - "# Change the tick spacing for the x and y axes\n", - "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50))\n", - "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", - "\n", - "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", - "plt.show()\n", - "\n", - "# Define the range of test data sizes to use\n", - "data_sizes = range(1, len(x_test), 1) \n", - "\n", - "# Calculate the probability of a wrong prediction based on test accuracy\n", - "prob_wrong = 1 - test_accuracy\n", - "\n", - "# Create a list to store the probability of getting at least one wrong answer for each test data size\n", - "probabilities = []\n", - "\n", - "# Calculate the probability of getting at least one wrong answer for each data size\n", - "for size in data_sizes:\n", - " # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", - " cdf = binom.cdf(0, size, prob_wrong)\n", - " # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", - " prob = 1 - cdf\n", - " probabilities.append(prob)\n", - "\n", - "# Find the index of the first data point that has a probability greater than prob_L%\n", - "index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", - "\n", - "# Limit the x-axis to the first data point that has a probability greater than prob_L%\n", - "data_sizes = data_sizes[:index+1]\n", - "probabilities = probabilities[:index+1]\n", - "\n", - "# Plot the probability vs. the number of data points\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(data_sizes, probabilities)\n", - "plt.xlabel('Number of Data Points')\n", - "plt.ylabel('Probability')\n", - "\n", - "# Add gridlines for the x and y axes\n", - "plt.grid(True)\n", - "\n", - "# Change the tick spacing for the x and y axes\n", - "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 2))\n", - "plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100))\n", - "\n", - "plt.ylim(top=1.01)\n", - "\n", - "plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# keras/TF model\n", + "
\n",
+    " Copyright (c) 2023 Aydin Hamedi\n",
+    " \n",
+    " This software is released under the MIT License.\n",
+    " https://opensource.org/licenses/MIT\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre Conf" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:27:44.939427800Z", + "start_time": "2023-12-28T02:27:44.923095500Z" + }, + "notebookRunGroups": { + "groupValue": "21" + } + }, + "outputs": [], + "source": [ + "CPU_only = False # True to Force TF to use the cpu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pylibs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:27:47.128539500Z", + "start_time": "2023-12-28T02:27:44.940432900Z" + }, + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import time\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "if CPU_only:\n", + " os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n", + "import cv2\n", + "import glob \n", + "import keras\n", + "import pprint\n", + "import random\n", + "import shutil\n", + "import gzip\n", + "import glob\n", + "import pickle\n", + "import datetime\n", + "import subprocess\n", + "import gpu_control\n", + "import numpy as np\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", + "import seaborn as sns\n", + "from hyperas import optim\n", + "# import tensorflow_addons as tfa\n", + "from keras_adabound import AdaBound\n", + "from importlib import reload\n", + "from keras.losses import categorical_crossentropy\n", + "import tensorflow as tf\n", + "from keras.models import Model\n", + "from scipy.ndimage import zoom\n", + "import matplotlib.pyplot as plt\n", + "from model_profiler import model_profiler\n", + "from keras_gradient_noise import add_gradient_noise\n", + "from keras.optimizers import SGD, Adam, Adagrad, Adadelta, Nadam, RMSprop, Adamax\n", + "# from tensorflow_addons.optimizers import Yogi\n", + "from adabelief_tf import AdaBeliefOptimizer\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from imblearn.over_sampling import SMOTE\n", + "from keras.regularizers import l2\n", + "from keras.models import load_model\n", + "from matplotlib import pyplot as plt\n", + "from PIL import Image, ImageDraw, ImageFont\n", + "from keras import Sequential\n", + "from random import randint, choice, shuffle\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.callbacks import TensorBoard\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import ModelCheckpoint, Callback, LearningRateScheduler\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.layers import Conv2D,\\\n", + " MaxPooling2D,\\\n", + " Flatten,\\\n", + " Dense,\\\n", + " Dropout,\\\n", + " BatchNormalization,\\\n", + " SeparableConv2D,\\\n", + " Input, Concatenate,\\\n", + " GlobalAveragePooling2D,\\\n", + " CuDNNLSTM, concatenate,\\\n", + " Reshape, Multiply, \\\n", + " Conv1D, MaxPooling1D\n", + "# Utils\n", + "from Utils.one_cycle import OneCycleLr\n", + "from Utils.lr_find import LrFinder\n", + "from Utils.print_color_V2_NEW import print_Color_V2\n", + "from Utils.print_color_V1_OLD import print_Color\n", + "from Utils.Other import *\n", + "# Other\n", + "tf.get_logger().setLevel('ERROR')\n", + "physical_devices = tf.config.list_physical_devices('GPU')\n", + "for gpu_instance in physical_devices:\n", + " tf.config.experimental.set_memory_growth(gpu_instance, True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conf\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data processing conf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:27:47.139048Z", + "start_time": "2023-12-28T02:27:47.116546100Z" + }, + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [], + "source": [ + "# Directory paths# Directory paths for training, test and validation image data\n", + "train_dir = 'Database\\\\Train\\\\Data\\\\train'\n", + "test_dir = 'Database\\\\Train\\\\Data\\\\test'\n", + "validation_dir = 'Database\\\\Train\\\\Data\\\\val'\n", + "img_res = [224, 224, 3]\n", + "# img_res = [324, 324, 3]\n", + "# img_res = [224, 224, 3]\n", + "# img_res = [384, 384, 3] # Very slow needs >=24Gb Vram for batch size of 1 (NR!)\n", + "interpolation_order_IFG = 2\n", + "categorical_IMP = True\n", + "Make_EV_DATA = False\n", + "R_fill_mode = True\n", + "add_img_grain = True\n", + "Save_TS = True\n", + "Use_SMOTE = False # (⚠️Beta⚠️)\n", + "ADBD = 0\n", + "OP_HDC = False\n", + "SL_EX = '_V1' # _NONOM_V1 | _V1 | _SDNP_V1\n", + "LNTS = 0\n", + "Debug_OUT = False\n", + "adjust_brightness_Mode = True\n", + "RANGE_NOM = True # False for 0 to 255 True for 0 to 1 >> use False for models like ConvNeXtXLarge (⚠️deprecated⚠️)\n", + "scale_data_NP_M = False # (⚠️deprecated⚠️)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:27:48.287855100Z", + "start_time": "2023-12-28T02:27:48.252944800Z" + }, + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "SAVE_TYPE = 'H5'\n", + "Use_mixed_float16 = False\n", + "#Other\n", + "if Use_mixed_float16:\n", + " tf.keras.mixed_precision.set_global_policy('mixed_float16')\n", + "else:\n", + " tf.keras.mixed_precision.set_global_policy('float32')\n", + " \n", + "print(tf.keras.mixed_precision.global_policy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## data processing \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:31:27.059139500Z", + "start_time": "2023-12-28T02:27:50.219209700Z" + }, + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;33mUsing Def IDG...\u001b[0m\n", + "Found 23681 images belonging to 2 classes.\n", + "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n", + "\u001b[0;33mMaking categorical data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mNormalizing image data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat32\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n", + "\u001b[0;33mSetting LNTS...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n", + "\u001b[0;33mshuffling data...\u001b[0m\n", + "\u001b[0;33mSaving TS...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m01_d01-h22_m20_s41\u001b[0m\n", + "\u001b[0;32mDone.\u001b[0m\n" + ] + } + ], + "source": [ + "#Z_SCORE_normalize\n", + "def Z_SCORE_normalize(arr):\n", + " arr = arr.astype('float32')\n", + " mean = np.mean(arr)\n", + " std_dev = np.std(arr)\n", + " arr = (arr - mean) / std_dev\n", + " return arr\n", + "#normalize_TO_RANGE\n", + "def normalize_TO_RANGE(arr, min_val, max_val):\n", + " arr = arr.astype('float32')\n", + " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", + " arr = arr * (max_val - min_val) + min_val\n", + " return arr\n", + "#scale_data\n", + "def scale_data_NP(data):\n", + " if scale_data_NP_M:\n", + " data = data.astype('float32')\n", + " data = (data - 127.5) / 127.5\n", + " return data\n", + " else:\n", + " return data / 255\n", + "#add_image_grain\n", + "def add_image_grain(image, intensity = 0.01):\n", + " # Generate random noise array\n", + " noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8)\n", + "\n", + " # Scale the noise array\n", + " scaled_noise = (noise * intensity).astype(np.float32)\n", + " # Add the noise to the image\n", + " noisy_image = cv2.add(image, scaled_noise)\n", + "\n", + " return noisy_image\n", + "#apply_clahe_rgb_array\n", + "def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)):\n", + " # Create a CLAHE object\n", + " clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n", + " \n", + " # Iterate over each image in the array\n", + " for i in range(len(images)):\n", + " # Split the image into color channels\n", + " b, g, r = cv2.split(images[i])\n", + " \n", + " # Convert the channels to the appropriate format\n", + " b = cv2.convertScaleAbs(b)\n", + " g = cv2.convertScaleAbs(g)\n", + " r = cv2.convertScaleAbs(r)\n", + " \n", + " # Apply adaptive histogram equalization to each channel\n", + " equalized_b = clahe.apply(b)\n", + " equalized_g = clahe.apply(g)\n", + " equalized_r = clahe.apply(r)\n", + "\n", + " # Merge the equalized channels back into an image\n", + " equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r))\n", + "\n", + " # Replace the original image with the equalized image in the array\n", + " images[i] = equalized_image\n", + "\n", + " return images\n", + "#noise_func\n", + "def noise_func(image):\n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.05, 0.05)\n", + " intensityL1 = random.uniform(-0.04, 0.04)\n", + " else:\n", + " intensityL2 = random.uniform(-0.06, 0.06)\n", + " intensityL1 = random.uniform(-0.04, 0.04)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 64)\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image.shape[0], block_size_L2):\n", + " for j in range(0, image.shape[1], block_size_L2):\n", + " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image = new_image \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image.shape[0], block_size_L1):\n", + " for j in range(0, image.shape[1], block_size_L1):\n", + " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.045) # Random intensity between 0 and 0.026\n", + " new_image = add_image_grain(new_image, intensity=intensity)\n", + " return new_image\n", + "#shuffle_data\n", + "def shuffle_data(x, y):\n", + " indices = np.arange(x.shape[0])\n", + " np.random.shuffle(indices)\n", + " x = x[indices]\n", + " y = y[indices]\n", + " return x, y\n", + "#save_images_to_dir\n", + "def save_images_to_dir(images, labels, dir_path):\n", + " # create the directory if it doesn't exist\n", + " if not os.path.exists(dir_path):\n", + " os.makedirs(dir_path)\n", + " # iterate over the images and labels\n", + " for i, (image, label) in enumerate(zip(images, labels)):\n", + " # get the class label\n", + " class_label = np.argmax(label)\n", + " # create the file path\n", + " file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png')\n", + " # save the image to the file path\n", + " plt.imsave(file_path, image.squeeze())\n", + " # compress the directory\n", + " shutil.make_archive(dir_path, 'gztar', dir_path)\n", + " # remove the original directory\n", + " shutil.rmtree(dir_path)\n", + "#Debug_img_Save\n", + "def Debug_img_Save(img, id = 'DEF'): \n", + " SITD = np.random.choice(img.shape[0], size=400, replace=False)\n", + " S_dir = f'Samples\\\\Debug\\\\{id}\\\\TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*[Debug] (DPO) Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", + " save_images_to_dir(normalize_TO_RANGE(img[SITD], 0, 1), img[SITD], S_dir)\n", + "# Create an ImageDataGenerator for the training set\n", + "if OP_HDC:\n", + " print_Color('Using OP_HDC IDG...', ['yellow'])\n", + " train_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.24, \n", + " shear_range=0.22,\n", + " width_shift_range=0.21,\n", + " brightness_range=(0.86, 1.1),\n", + " height_shift_range=0.21,\n", + " channel_shift_range=100,\n", + " featurewise_center=False,\n", + " featurewise_std_normalization=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " fill_mode='nearest', # constant\n", + " preprocessing_function=noise_func\n", + " )\n", + "else:\n", + " print_Color('Using Def IDG...', ['yellow'])\n", + " train_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.26, \n", + " shear_range=0.25,\n", + " width_shift_range=0.25,\n", + " brightness_range=(0.78, 1.1),\n", + " height_shift_range=0.25,\n", + " channel_shift_range=100,\n", + " featurewise_center=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " featurewise_std_normalization=False,\n", + " fill_mode='nearest', # constant\n", + " preprocessing_function=noise_func\n", + " )\n", + "train_datagen_SM = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " vertical_flip=False,\n", + " rotation_range=20,\n", + " zoom_range=0.07, \n", + " shear_range=0.07,\n", + " width_shift_range=0.07,\n", + " brightness_range=(0.99, 1.01),\n", + " height_shift_range=0.07,\n", + " channel_shift_range=0,\n", + " featurewise_center=False,\n", + " interpolation_order=interpolation_order_IFG,\n", + " featurewise_std_normalization=False\n", + ")\n", + "# Create an iterator for the training set\n", + "train_generator_SM = train_datagen_SM.flow_from_directory(\n", + " train_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]),\n", + " class_mode='binary')\n", + "# Create an ImageDataGenerator for the validation set (OP)\n", + "if Make_EV_DATA:\n", + " val_datagen = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " zoom_range = 0.01, \n", + " width_shift_range=0.01, \n", + " interpolation_order=interpolation_order_IFG,\n", + " height_shift_range=0.01)\n", + "\n", + " # Create an iterator for the validation set\n", + " val_generator = val_datagen.flow_from_directory(\n", + " validation_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]),\n", + " class_mode='binary',\n", + " color_mode='rgb')\n", + "\n", + " # Create an ImageDataGenerator for the test set\n", + " test_datagen = ImageDataGenerator(\n", + " horizontal_flip=False,\n", + " zoom_range = 0.01, \n", + " width_shift_range=0.01, \n", + " interpolation_order=interpolation_order_IFG,\n", + " height_shift_range=0.01)\n", + "\n", + " # Create an iterator for the test set\n", + " test_generator = test_datagen.flow_from_directory(\n", + " test_dir,\n", + " target_size=(img_res[0], img_res[1]),\n", + " batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]),\n", + " class_mode='binary',\n", + " color_mode='rgb')\n", + "# Load all images and labels into memory\n", + "print_Color('Loading all images and labels into memory...', ['yellow'])\n", + "x_train, y_train = next(iter(train_generator_SM))\n", + "if Make_EV_DATA:\n", + " x_val, y_val = next(iter(val_generator))\n", + " x_test, y_test = next(iter(test_generator))\n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST1') # DEBUG\n", + "# fit parameters from data\n", + "# train_datagen.fit(x_train)\n", + "#to_categorical (TEMP)\n", + "if categorical_IMP:\n", + " print_Color('Making categorical data...', ['yellow'])\n", + " y_train = to_categorical(y_train, num_classes=2)\n", + " if Make_EV_DATA:\n", + " y_val = to_categorical(y_val, num_classes=2)\n", + " y_test = to_categorical(y_test, num_classes=2)\n", + "# Use_SMOTE\n", + "if Use_SMOTE:\n", + " print_Color('SMOTE...', ['yellow'])\n", + " # Convert y_train from one-hot encoding to label encoding\n", + " y_train_label_encoded = np.argmax(y_train, axis=1)\n", + "\n", + " # Print the original label distribution\n", + " unique, counts = np.unique(y_train_label_encoded, return_counts=True)\n", + " print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True)\n", + "\n", + " # Use SMOTE to oversample the minority class\n", + " smote = SMOTE(random_state=42)\n", + " x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded)\n", + "\n", + " # Print the resampled label distribution\n", + " unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True)\n", + " print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True)\n", + "\n", + " # Reshape x_train_res back to the original x_train shape\n", + " x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3])\n", + "\n", + " # Convert y_train_res from label encoding back to one-hot encoding\n", + " y_train_res = to_categorical(y_train_res_label_encoded)\n", + "\n", + " # Calculate the ratio of two labels after resampling\n", + " pneumonia_count = np.sum(y_train_res[:, 1])\n", + " total_count = y_train_res.shape[0]\n", + " label_ratio_res = pneumonia_count / total_count\n", + " label_ratio_percentage_res = label_ratio_res * 100\n", + "\n", + " # Replace the original data with the resampled data\n", + " x_train = x_train_res\n", + " y_train = y_train_res\n", + "\n", + " # Delete the resampled data to free up memory\n", + " del x_train_res, y_train_res_label_encoded, y_train_res\n", + "# Generating augmented data\n", + "print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...',\n", + " ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'],\n", + " advanced_mode=True)\n", + "if ADBD > 0:\n", + " for i in range(ADBD):\n", + " # ADB_clip_limit Scheduler>>>\n", + " if i == 0:\n", + " ADB_clip_limit = 0.8\n", + " else:\n", + " #V1>>>\n", + " CL_SLM = 2.4\n", + " ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05)\n", + " # Try it in win graphing calculator copy and paste:\n", + " # β”Œ-------------┬--┬---------------┐\n", + " # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚\n", + " # β””-------------β”΄--β”΄---------------β”˜\n", + " #V2>>>\n", + " # CL_SLM_2 = 1.4\n", + " # CL_SLM_Start_2 = 2\n", + " # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) \n", + " # Try it in win graphing calculator copy and paste:\n", + " # β”Œ-----------------┬--┬-------------------┐\n", + " # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚\n", + " # β””-----------------β”΄--β”΄-------------------β”˜\n", + " print(f'> Generating ADB[{i+1}/{ADBD}]...')\n", + " # prepare an iterators to scale images\n", + " train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train))\n", + "\n", + " # get augmented data\n", + " x_train_augmented, y_train_augmented = train_iterator.next()\n", + " print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...')\n", + " print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}')\n", + " x_train_augmented = np.clip(x_train_augmented, 0, 255) \n", + " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST2') # DEBUG\n", + " #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", + " x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss\n", + " print(f'> └───Adding the Generated ADB...')\n", + " if Debug_OUT: Debug_img_Save(x_train_augmented, 'ST3') # DEBUG\n", + " # append augmented data to original data\n", + " x_train = np.concatenate([x_train, x_train_augmented])\n", + " y_train = np.concatenate([y_train, y_train_augmented])\n", + " #free up memory\n", + " del y_train_augmented\n", + " del x_train_augmented\n", + "# normalizing \n", + "print_Color('Normalizing image data...', ['yellow'])\n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST4') # DEBUG\n", + "x_train = np.clip(x_train, 0, 255)\n", + "if RANGE_NOM:\n", + " x_train = scale_data_NP(x_train)\n", + "y_train = np.array(y_train) \n", + "if Make_EV_DATA:\n", + " x_test = np.clip(x_test, 0, 255) \n", + " x_val = np.clip(x_val, 0, 255) \n", + " if RANGE_NOM:\n", + " x_val = scale_data_NP(x_val)\n", + " y_val = np.array(y_val) \n", + " if RANGE_NOM: \n", + " x_test = scale_data_NP(x_test)\n", + " y_test = np.array(y_test) \n", + "if Debug_OUT: Debug_img_Save(x_train, 'ST5') # DEBUG\n", + "# Check the data type of image data\n", + "print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True)\n", + "# Check the range of image data\n", + "print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True)\n", + "# Calculate the ratio of two labels\n", + "if categorical_IMP:\n", + " label_sums = np.sum(y_train, axis=0)\n", + " label_ratio = label_sums / (np.sum(y_train) + 1e-10)\n", + " label_ratio_percentage = label_ratio * 100\n", + " print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL',\n", + " ['normal', 'red', 'magenta', 'green'], advanced_mode=True) \n", + "print_Color('Setting LNTS...', ['yellow'])\n", + "# Get the total number of samples in the arrays\n", + "num_samples = x_train.shape[0]\n", + "print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", + "if LNTS != 0:\n", + " print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True)\n", + " print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True)\n", + " # Generate random indices to select LNTS samples\n", + " indices = np.random.choice(num_samples, size=LNTS, replace=False)\n", + " # Select the samples using the generated indices\n", + " x_selected = x_train[indices]\n", + " y_selected = y_train[indices]\n", + " x_train = x_selected\n", + " y_train = y_selected\n", + " #free up memory\n", + " del x_selected\n", + " del y_selected\n", + " del indices\n", + " #Debug\n", + " num_samples = x_train.shape[0]\n", + " print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True)\n", + "# Shuffle the training data\n", + "print_Color('shuffling data...', ['yellow'])\n", + "x_train, y_train = shuffle_data(x_train, y_train)\n", + "#save_images_to_dir \n", + "if Save_TS:\n", + " print_Color('Saving TS...', ['yellow'])\n", + " SITD = np.random.choice(num_samples, size=400, replace=False)\n", + " S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True)\n", + " if RANGE_NOM:\n", + " if scale_data_NP_M:\n", + " save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir)\n", + " else:\n", + " save_images_to_dir(x_train[SITD], y_train[SITD], S_dir)\n", + " else:\n", + " save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir)\n", + "print_Color('Done.', ['green'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save EV Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy', x_val)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy', y_val)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy', x_test)\n", + "np.save(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy', y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load EV Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:31:27.380088800Z", + "start_time": "2023-12-28T02:31:27.270860200Z" + }, + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [], + "source": [ + "x_val = np.load(f'Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy')\n", + "y_val = np.load(f'Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy')\n", + "x_test = np.load(f'Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy')\n", + "y_test = np.load(f'Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Analyzation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import seaborn as sns\n", + "from scipy.stats import zscore\n", + "\n", + "# Select a subset of your data\n", + "subset_size_pixels = 10 # Change this to the size of the subset you want for individual pixels\n", + "subset_size_mean = 200 # Change this to the size of the subset you want for mean RGB values\n", + "indices_pixels = np.random.choice(x_train.shape[0], subset_size_pixels, replace=False)\n", + "indices_mean = np.random.choice(x_train.shape[0], subset_size_mean, replace=False)\n", + "subset_pixels = x_train[indices_pixels]\n", + "subset_mean = x_train[indices_mean]\n", + "\n", + "# Reshape the data for calculating Z-scores\n", + "reshaped_data_pixels = subset_pixels.reshape(-1, subset_pixels.shape[-1])\n", + "reshaped_data_mean = subset_mean.reshape(-1, subset_mean.shape[-1])\n", + "\n", + "# Calculate the mean intensity\n", + "mean_intensity_pixels = reshaped_data_pixels.mean(axis=-1)\n", + "mean_intensity_mean = reshaped_data_mean.mean(axis=-1)\n", + "\n", + "# Stack the mean intensity with the reshaped data\n", + "data_with_mean_pixels = np.hstack([reshaped_data_pixels, mean_intensity_pixels.reshape(-1, 1)])\n", + "data_with_mean_mean = np.hstack([reshaped_data_mean, mean_intensity_mean.reshape(-1, 1)])\n", + "\n", + "# Calculate Z-scores\n", + "z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))\n", + "z_scores_mean = np.abs(zscore(data_with_mean_mean, axis=0))\n", + "\n", + "# Identify outliers\n", + "outliers_pixels = np.where(z_scores_pixels > 3)\n", + "outliers_mean = np.where(z_scores_mean > 3)\n", + "\n", + "# Create a 3D scatter plot for RGB channels\n", + "fig = plt.figure(figsize=(10, 20))\n", + "\n", + "# Plot for individual pixels\n", + "ax = fig.add_subplot(211, projection='3d')\n", + "ax.scatter(z_scores_pixels[:, 0], z_scores_pixels[:, 1], z_scores_pixels[:, 2], alpha=0.1)\n", + "ax.scatter(z_scores_pixels[outliers_pixels[0], 0], z_scores_pixels[outliers_pixels[0], 1], z_scores_pixels[outliers_pixels[0], 2], color='red')\n", + "ax.set_title('Z-Score Scatter Plot for Individual Pixels')\n", + "ax.set_xlabel('Red')\n", + "ax.set_ylabel('Green')\n", + "ax.set_zlabel('Blue')\n", + "\n", + "# Plot for mean RGB values\n", + "ax = fig.add_subplot(212, projection='3d')\n", + "ax.scatter(z_scores_mean[:, 0], z_scores_mean[:, 1], z_scores_mean[:, 2], alpha=0.1)\n", + "ax.scatter(z_scores_mean[outliers_mean[0], 0], z_scores_mean[outliers_mean[0], 1], z_scores_mean[outliers_mean[0], 2], color='red')\n", + "ax.set_title('Z-Score Scatter Plot for Mean RGB Values')\n", + "ax.set_xlabel('Red')\n", + "ax.set_ylabel('Green')\n", + "ax.set_zlabel('Blue')\n", + "\n", + "# Density plot of the mean intensity\n", + "plt.figure(figsize=(10, 5))\n", + "sns.kdeplot(data=z_scores_pixels[:, -1], fill=True)\n", + "plt.title('Density Plot of Z-Scores for Mean Intensity for Individual Pixels')\n", + "plt.xlabel('Z-Score')\n", + "\n", + "sns.kdeplot(data=z_scores_mean[:, -1], fill=True)\n", + "plt.title('Density Plot of Z-Scores for Mean Intensity for Mean RGB Values')\n", + "plt.xlabel('Z-Score')\n", + "\n", + "# Display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1\n", + "```\n", + "recommended: ⚠️\n", + "statuses: Ready\n", + "Working: βœ…\n", + "Max fine tuned acc: β‰…95.1\n", + "Max fine tuned acc TLRev2: N/A\n", + "type: transfer learning>>>(EfficientNetB7)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import EfficientNetB7\n", + "\n", + "EfficientNet_M = EfficientNetB7(include_top=True, input_shape=(img_res[0], img_res[1], img_res[2]), weights=None, classes=2, classifier_activation='softmax')\n", + "# define new model\n", + "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.summary()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.1\n", + "```\n", + "recommended: ❌\n", + "statuses: S.Ready (can improve)\n", + "Working: ❌\n", + "Max fine tuned acc: β‰…93.2\n", + "Max fine tuned acc TLRev2: N/A\n", + "type: transfer learning>>>(ConvNeXtLarge)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.applications import ConvNeXtLarge\n", + "\n", + "ConvNeXtLarge_M = ConvNeXtLarge(include_top=False, input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=False)\n", + "# define new model\n", + "model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.summary()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "source": [ + "### Rev1.2\n", + "```\n", + "recommended: βœ…\n", + "statuses: Ready\n", + "Working: βœ…\n", + "Max fine tuned acc: 95.3\n", + "Max fine tuned acc TLRev2: 96.96\n", + "type: transfer learning>>>(EfficientNetB7::CCL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-27T17:34:12.077394600Z", + "start_time": "2023-12-27T17:34:05.068171500Z" + }, + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total layers in the base model: 806\n", + "Freezing 0 layers in the base model...\n", + "Percentage of the base model that is frozen: 0.00%\n", + "Total model layers: 814\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 65,428,818\n", + "Non-trainable params: 312,768\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB7 as KENB7\n", + "# FUNC\n", + "def Eff_B7_NS(freeze_layers):\n", + " base_model = KENB7(input_shape=(\n", + " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) /\n", + " len(base_model.layers)) * 100\n", + " print(\n", + " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", + " Dense_L1 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(\n", + " inputs=base_model.input, outputs=predictions)\n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " # OPT/compile\n", + " opt = SGD(momentum=0.9, nesterov=False)\n", + " # opt = Nadam()\n", + " # opt = Adamax()\n", + " # opt = RMSprop(momentum=0.9)\n", + " # opt = Adagrad()\n", + " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B7_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.3\n", + "```\n", + "recommended: ❌\n", + "statuses: Test\n", + "Working: βœ…\n", + "Max fine tuned acc: ⚠️\n", + "Max fine tuned acc TLRev2: ⚠️\n", + "type: transfer learning>>>(EfficientNetB7|Xception::CCL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total base_model1 layers: 806\n", + "Total base_model2 layers: 132\n", + "Total model layers: 15\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " efficientnet-b7 (Functional) (None, 7, 7, 2560) 64097680 ['input_1[0][0]'] Y \n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", + "| )] |\n", + "| |\n", + "| stem_conv (Conv2D) (None, 112, 112, 64 1728 [] Y |\n", + "| ) |\n", + "| |\n", + "| stem_bn (BatchNormalization) (None, 112, 112, 64 256 [] Y |\n", + "| ) |\n", + "| |\n", + "| stem_activation (Activation) (None, 112, 112, 64 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block1a_bn (BatchNormalization (None, 112, 112, 64 256 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1a_activation (Activation (None, 112, 112, 64 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1a_se_squeeze (GlobalAver (None, 64) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 [] Y |\n", + "| |\n", + "| block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 [] Y |\n", + "| |\n", + "| block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 [] Y |\n", + "| |\n", + "| block1a_se_excite (Multiply) (None, 112, 112, 64 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1a_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", + "| lization) ) |\n", + "| |\n", + "| block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block1b_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1b_activation (Activation (None, 112, 112, 32 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1b_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", + "| |\n", + "| block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", + "| |\n", + "| block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", + "| |\n", + "| block1b_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1b_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", + "| lization) ) |\n", + "| |\n", + "| block1b_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1b_add (Add) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block1c_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1c_activation (Activation (None, 112, 112, 32 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1c_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", + "| |\n", + "| block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", + "| |\n", + "| block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", + "| |\n", + "| block1c_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1c_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", + "| lization) ) |\n", + "| |\n", + "| block1c_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1c_add (Add) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block1d_bn (BatchNormalization (None, 112, 112, 32 128 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1d_activation (Activation (None, 112, 112, 32 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block1d_se_squeeze (GlobalAver (None, 32) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 [] Y |\n", + "| |\n", + "| block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 [] Y |\n", + "| |\n", + "| block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 [] Y |\n", + "| |\n", + "| block1d_se_excite (Multiply) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1d_project_bn (BatchNorma (None, 112, 112, 32 128 [] Y |\n", + "| lization) ) |\n", + "| |\n", + "| block1d_drop (FixedDropout) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1d_add (Add) (None, 112, 112, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 [] Y |\n", + "| 2) |\n", + "| |\n", + "| block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 [] Y |\n", + "| ization) 2) |\n", + "| |\n", + "| block2a_expand_activation (Act (None, 112, 112, 19 0 [] Y |\n", + "| ivation) 2) |\n", + "| |\n", + "| block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2a_bn (BatchNormalization (None, 56, 56, 192) 768 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2a_activation (Activation (None, 56, 56, 192) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2a_se_squeeze (GlobalAver (None, 192) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 [] Y |\n", + "| |\n", + "| block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 [] Y |\n", + "| |\n", + "| block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 [] Y |\n", + "| |\n", + "| block2a_se_excite (Multiply) (None, 56, 56, 192) 0 [] Y |\n", + "| |\n", + "| block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 [] Y |\n", + "| |\n", + "| block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2b_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2b_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2b_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2b_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2b_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2b_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2c_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2c_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2c_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2c_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2c_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2c_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2d_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2d_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2d_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2d_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2d_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2d_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2e_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2e_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2e_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2e_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2e_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2e_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2f_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2f_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2f_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2f_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2f_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2f_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block2g_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 [] Y |\n", + "| D) |\n", + "| |\n", + "| block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2g_activation (Activation (None, 56, 56, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2g_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block2g_se_excite (Multiply) (None, 56, 56, 288) 0 [] Y |\n", + "| |\n", + "| block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 [] Y |\n", + "| |\n", + "| block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block2g_drop (FixedDropout) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block2g_add (Add) (None, 56, 56, 48) 0 [] Y |\n", + "| |\n", + "| block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 [] Y |\n", + "| |\n", + "| block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3a_expand_activation (Act (None, 56, 56, 288) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3a_activation (Activation (None, 28, 28, 288) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3a_se_squeeze (GlobalAver (None, 288) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 [] Y |\n", + "| |\n", + "| block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 [] Y |\n", + "| |\n", + "| block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 [] Y |\n", + "| |\n", + "| block3a_se_excite (Multiply) (None, 28, 28, 288) 0 [] Y |\n", + "| |\n", + "| block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 [] Y |\n", + "| |\n", + "| block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3b_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3b_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3b_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3b_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3b_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3b_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3c_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3c_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3c_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3c_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3c_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3c_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3d_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3d_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3d_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3d_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3d_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3d_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3e_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3e_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3e_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3e_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3e_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3e_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3f_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3f_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3f_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3f_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3f_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3f_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block3g_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3g_activation (Activation (None, 28, 28, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block3g_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block3g_se_excite (Multiply) (None, 28, 28, 480) 0 [] Y |\n", + "| |\n", + "| block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 [] Y |\n", + "| |\n", + "| block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3g_drop (FixedDropout) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block3g_add (Add) (None, 28, 28, 80) 0 [] Y |\n", + "| |\n", + "| block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 [] Y |\n", + "| |\n", + "| block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4a_expand_activation (Act (None, 28, 28, 480) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4a_activation (Activation (None, 14, 14, 480) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4a_se_squeeze (GlobalAver (None, 480) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 [] Y |\n", + "| |\n", + "| block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 [] Y |\n", + "| |\n", + "| block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 [] Y |\n", + "| |\n", + "| block4a_se_excite (Multiply) (None, 14, 14, 480) 0 [] Y |\n", + "| |\n", + "| block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 [] Y |\n", + "| |\n", + "| block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4b_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4b_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4b_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4b_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4b_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4b_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4c_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4c_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4c_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4c_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4c_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4c_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4d_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4d_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4d_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4d_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4d_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4d_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4e_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4e_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4e_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4e_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4e_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4e_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4f_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4f_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4f_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4f_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4f_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4f_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4g_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4g_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4g_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4g_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4g_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4g_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4h_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4h_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4h_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4h_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4h_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4h_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4i_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4i_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4i_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4i_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4i_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4i_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block4j_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 [] Y |\n", + "| D) |\n", + "| |\n", + "| block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4j_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block4j_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block4j_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 [] Y |\n", + "| |\n", + "| block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4j_drop (FixedDropout) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block4j_add (Add) (None, 14, 14, 160) 0 [] Y |\n", + "| |\n", + "| block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 [] Y |\n", + "| |\n", + "| block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block5a_expand_activation (Act (None, 14, 14, 960) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 [] Y |\n", + "| D) |\n", + "| |\n", + "| block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5a_activation (Activation (None, 14, 14, 960) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5a_se_squeeze (GlobalAver (None, 960) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 [] Y |\n", + "| |\n", + "| block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 [] Y |\n", + "| |\n", + "| block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 [] Y |\n", + "| |\n", + "| block5a_se_excite (Multiply) (None, 14, 14, 960) 0 [] Y |\n", + "| |\n", + "| block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 [] Y |\n", + "| |\n", + "| block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5b_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5b_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5b_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5b_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5b_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5b_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5c_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5c_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5c_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5c_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5c_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5c_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5d_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5d_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5d_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5d_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5d_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5d_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5e_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5e_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5e_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5e_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5e_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5e_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5f_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5f_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5f_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5f_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5f_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5f_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5g_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5g_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5g_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5g_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5g_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5g_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5h_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5h_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5h_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5h_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5h_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5h_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5i_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5i_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5i_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5i_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5i_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5i_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block5j_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 [] Y |\n", + "| D) ) |\n", + "| |\n", + "| block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5j_activation (Activation (None, 14, 14, 1344 0 [] Y |\n", + "| ) ) |\n", + "| |\n", + "| block5j_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block5j_se_excite (Multiply) (None, 14, 14, 1344 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 [] Y |\n", + "| |\n", + "| block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5j_drop (FixedDropout) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block5j_add (Add) (None, 14, 14, 224) 0 [] Y |\n", + "| |\n", + "| block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 [] Y |\n", + "| ization) ) |\n", + "| |\n", + "| block6a_expand_activation (Act (None, 14, 14, 1344 0 [] Y |\n", + "| ivation) ) |\n", + "| |\n", + "| block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6a_activation (Activation (None, 7, 7, 1344) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6a_se_squeeze (GlobalAver (None, 1344) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 [] Y |\n", + "| |\n", + "| block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 [] Y |\n", + "| |\n", + "| block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 [] Y |\n", + "| |\n", + "| block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 [] Y |\n", + "| |\n", + "| block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 [] Y |\n", + "| |\n", + "| block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6b_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6b_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6b_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6b_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6b_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6c_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6c_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6c_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6c_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6c_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6d_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6d_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6d_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6d_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6d_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6e_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6e_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6e_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6e_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6e_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6f_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6f_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6f_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6f_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6f_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6g_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6g_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6g_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6g_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6g_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6h_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6h_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6h_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6h_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6h_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6i_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6i_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6i_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6i_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6i_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6j_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6j_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6j_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6j_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6j_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6k_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6k_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6k_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6k_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6k_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6l_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6l_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6l_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6l_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6l_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block6m_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 [] Y |\n", + "| D) |\n", + "| |\n", + "| block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6m_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block6m_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 [] Y |\n", + "| |\n", + "| block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6m_drop (FixedDropout) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block6m_add (Add) (None, 7, 7, 384) 0 [] Y |\n", + "| |\n", + "| block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 [] Y |\n", + "| |\n", + "| block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block7a_expand_activation (Act (None, 7, 7, 2304) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 [] Y |\n", + "| D) |\n", + "| |\n", + "| block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7a_activation (Activation (None, 7, 7, 2304) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7a_se_squeeze (GlobalAver (None, 2304) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 [] Y |\n", + "| |\n", + "| block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 [] Y |\n", + "| |\n", + "| block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 [] Y |\n", + "| |\n", + "| block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 [] Y |\n", + "| |\n", + "| block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 [] Y |\n", + "| |\n", + "| block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", + "| |\n", + "| block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block7b_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", + "| D) |\n", + "| |\n", + "| block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7b_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7b_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", + "| |\n", + "| block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", + "| |\n", + "| block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", + "| |\n", + "| block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", + "| |\n", + "| block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", + "| |\n", + "| block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7b_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| block7b_add (Add) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", + "| |\n", + "| block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block7c_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", + "| D) |\n", + "| |\n", + "| block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7c_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7c_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", + "| |\n", + "| block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", + "| |\n", + "| block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", + "| |\n", + "| block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", + "| |\n", + "| block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", + "| |\n", + "| block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7c_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| block7c_add (Add) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 [] Y |\n", + "| |\n", + "| block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 [] Y |\n", + "| ization) |\n", + "| |\n", + "| block7d_expand_activation (Act (None, 7, 7, 3840) 0 [] Y |\n", + "| ivation) |\n", + "| |\n", + "| block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 [] Y |\n", + "| D) |\n", + "| |\n", + "| block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7d_activation (Activation (None, 7, 7, 3840) 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block7d_se_squeeze (GlobalAver (None, 3840) 0 [] Y |\n", + "| agePooling2D) |\n", + "| |\n", + "| block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 [] Y |\n", + "| |\n", + "| block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 [] Y |\n", + "| |\n", + "| block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 [] Y |\n", + "| |\n", + "| block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 [] Y |\n", + "| |\n", + "| block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 [] Y |\n", + "| |\n", + "| block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7d_drop (FixedDropout) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| block7d_add (Add) (None, 7, 7, 640) 0 [] Y |\n", + "| |\n", + "| top_conv (Conv2D) (None, 7, 7, 2560) 1638400 [] Y |\n", + "| |\n", + "| top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 [] Y |\n", + "| |\n", + "| top_activation (Activation) (None, 7, 7, 2560) 0 [] Y |\n", + "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", + " xception (Functional) (None, 7, 7, 2048) 20861480 ['input_1[0][0]'] Y \n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| input_3 (InputLayer) [(None, 224, 224, 3 0 [] Y |\n", + "| )] |\n", + "| |\n", + "| block1_conv1 (Conv2D) (None, 111, 111, 32 864 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1_conv1_bn (BatchNormaliz (None, 111, 111, 32 128 [] Y |\n", + "| ation) ) |\n", + "| |\n", + "| block1_conv1_act (Activation) (None, 111, 111, 32 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1_conv2 (Conv2D) (None, 109, 109, 64 18432 [] Y |\n", + "| ) |\n", + "| |\n", + "| block1_conv2_bn (BatchNormaliz (None, 109, 109, 64 256 [] Y |\n", + "| ation) ) |\n", + "| |\n", + "| block1_conv2_act (Activation) (None, 109, 109, 64 0 [] Y |\n", + "| ) |\n", + "| |\n", + "| block2_sepconv1 (SeparableConv (None, 109, 109, 12 8768 [] Y |\n", + "| 2D) 8) |\n", + "| |\n", + "| block2_sepconv1_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", + "| lization) 8) |\n", + "| |\n", + "| block2_sepconv2_act (Activatio (None, 109, 109, 12 0 [] Y |\n", + "| n) 8) |\n", + "| |\n", + "| block2_sepconv2 (SeparableConv (None, 109, 109, 12 17536 [] Y |\n", + "| 2D) 8) |\n", + "| |\n", + "| block2_sepconv2_bn (BatchNorma (None, 109, 109, 12 512 [] Y |\n", + "| lization) 8) |\n", + "| |\n", + "| conv2d (Conv2D) (None, 55, 55, 128) 8192 [] Y |\n", + "| |\n", + "| block2_pool (MaxPooling2D) (None, 55, 55, 128) 0 [] Y |\n", + "| |\n", + "| batch_normalization (BatchNorm (None, 55, 55, 128) 512 [] Y |\n", + "| alization) |\n", + "| |\n", + "| add (Add) (None, 55, 55, 128) 0 [] Y |\n", + "| |\n", + "| block3_sepconv1_act (Activatio (None, 55, 55, 128) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block3_sepconv1 (SeparableConv (None, 55, 55, 256) 33920 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block3_sepconv1_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block3_sepconv2_act (Activatio (None, 55, 55, 256) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block3_sepconv2 (SeparableConv (None, 55, 55, 256) 67840 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block3_sepconv2_bn (BatchNorma (None, 55, 55, 256) 1024 [] Y |\n", + "| lization) |\n", + "| |\n", + "| conv2d_1 (Conv2D) (None, 28, 28, 256) 32768 [] Y |\n", + "| |\n", + "| block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 [] Y |\n", + "| |\n", + "| batch_normalization_1 (BatchNo (None, 28, 28, 256) 1024 [] Y |\n", + "| rmalization) |\n", + "| |\n", + "| add_1 (Add) (None, 28, 28, 256) 0 [] Y |\n", + "| |\n", + "| block4_sepconv1_act (Activatio (None, 28, 28, 256) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block4_sepconv1 (SeparableConv (None, 28, 28, 728) 188672 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block4_sepconv1_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block4_sepconv2_act (Activatio (None, 28, 28, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block4_sepconv2 (SeparableConv (None, 28, 28, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block4_sepconv2_bn (BatchNorma (None, 28, 28, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| conv2d_2 (Conv2D) (None, 14, 14, 728) 186368 [] Y |\n", + "| |\n", + "| block4_pool (MaxPooling2D) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| batch_normalization_2 (BatchNo (None, 14, 14, 728) 2912 [] Y |\n", + "| rmalization) |\n", + "| |\n", + "| add_2 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block5_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block5_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block5_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block5_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block5_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block5_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block5_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block5_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| add_3 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block6_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block6_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block6_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block6_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block6_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block6_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block6_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block6_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| add_4 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block7_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block7_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block7_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block7_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block7_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block7_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block7_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block7_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| add_5 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block8_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block8_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block8_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block8_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block8_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block8_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block8_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block8_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block8_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| add_6 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block9_sepconv1_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block9_sepconv1 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block9_sepconv1_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block9_sepconv2_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block9_sepconv2 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block9_sepconv2_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| block9_sepconv3_act (Activatio (None, 14, 14, 728) 0 [] Y |\n", + "| n) |\n", + "| |\n", + "| block9_sepconv3 (SeparableConv (None, 14, 14, 728) 536536 [] Y |\n", + "| 2D) |\n", + "| |\n", + "| block9_sepconv3_bn (BatchNorma (None, 14, 14, 728) 2912 [] Y |\n", + "| lization) |\n", + "| |\n", + "| add_7 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block10_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block10_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block10_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block10_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block10_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block10_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block10_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block10_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block10_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| add_8 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block11_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block11_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block11_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block11_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block11_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block11_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block11_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block11_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block11_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| add_9 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block12_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block12_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block12_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block12_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block12_sepconv2 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block12_sepconv2_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block12_sepconv3_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block12_sepconv3 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block12_sepconv3_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| add_10 (Add) (None, 14, 14, 728) 0 [] Y |\n", + "| |\n", + "| block13_sepconv1_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block13_sepconv1 (SeparableCon (None, 14, 14, 728) 536536 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block13_sepconv1_bn (BatchNorm (None, 14, 14, 728) 2912 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block13_sepconv2_act (Activati (None, 14, 14, 728) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block13_sepconv2 (SeparableCon (None, 14, 14, 1024 752024 [] Y |\n", + "| v2D) ) |\n", + "| |\n", + "| block13_sepconv2_bn (BatchNorm (None, 14, 14, 1024 4096 [] Y |\n", + "| alization) ) |\n", + "| |\n", + "| conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 [] Y |\n", + "| |\n", + "| block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 [] Y |\n", + "| |\n", + "| batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 [] Y |\n", + "| rmalization) |\n", + "| |\n", + "| add_11 (Add) (None, 7, 7, 1024) 0 [] Y |\n", + "| |\n", + "| block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 [] Y |\n", + "| on) |\n", + "| |\n", + "| block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 [] Y |\n", + "| v2D) |\n", + "| |\n", + "| block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 [] Y |\n", + "| alization) |\n", + "| |\n", + "| block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 [] Y |\n", + "| on) |\n", + "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", + " global_average_pooling2d (Glob (None, 2560) 0 ['efficientnet-b7[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " global_average_pooling2d_1 (Gl (None, 2048) 0 ['xception[0][0]'] Y \n", + " obalAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dense_1 (Dense) (None, 512) 1049088 ['global_average_pooling2d_1[0] Y \n", + " [0]'] \n", + " \n", + " concatenate (Concatenate) (None, 1024) 0 ['dense[0][0]', Y \n", + " 'dense_1[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 1024) 1049600 ['concatenate[0][0]'] Y \n", + " \n", + " dropout (Dropout) (None, 1024) 0 ['dense_2[0][0]'] Y \n", + " \n", + " batch_normalization_4 (BatchNo (None, 1024) 4096 ['dropout[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_3 (Dense) (None, 512) 524800 ['batch_normalization_4[0][0]'] Y \n", + " \n", + " batch_normalization_5 (BatchNo (None, 512) 2048 ['dense_3[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_4 (Dense) (None, 128) 65664 ['batch_normalization_5[0][0]'] Y \n", + " \n", + " dense_5 (Dense) (None, 2) 258 ['dense_4[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 88,965,946\n", + "Trainable params: 88,597,626\n", + "Non-trainable params: 368,320\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB7 as KENB7\n", + "from keras.applications.xception import Xception\n", + "\n", + "#FUNC\n", + "def Combo_Model(freeze_layers1, freeze_layers2):\n", + " # Define a common input\n", + " common_input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", + "\n", + " # Base model 1\n", + " base_model1 = KENB7(input_shape=(img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " # base_model1.load_weights('models\\Ready\\Other\\EfficientNetB7_PRET.h5', by_name=True, skip_mismatch=True)\n", + " base_model1_out = base_model1(common_input)\n", + " \n", + " # Base model 2\n", + " base_model2 = Xception(input_shape=(img_res[0], img_res[1], img_res[2]), weights='imagenet', include_top=False)\n", + " # base_model1.load_weights('models\\Ready\\Other\\Xception_PRET.h5', by_name=True, skip_mismatch=True)\n", + " base_model2_out = base_model2(common_input)\n", + "\n", + " print('Total base_model1 layers: ', len(base_model1.layers))\n", + " print('Total base_model2 layers: ', len(base_model2.layers))\n", + " \n", + " # Freeze the specified number of layers in both models\n", + " for layer in base_model1.layers[:freeze_layers1]:\n", + " layer.trainable = False\n", + " for layer in base_model2.layers[:freeze_layers2]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest in both models\n", + " for layer in base_model1.layers[freeze_layers1:]:\n", + " layer.trainable = True\n", + " for layer in base_model2.layers[freeze_layers2:]:\n", + " layer.trainable = True\n", + "\n", + " # Combine the output of the two base models\n", + " combined = concatenate([Dense(512,\n", + " activation='relu',\n", + " kernel_regularizer=l2(0.02)\n", + " )(GlobalAveragePooling2D()(base_model1_out)),\n", + " Dense(512,\n", + " activation='relu',\n", + " kernel_regularizer=l2(0.02)\n", + " )(GlobalAveragePooling2D()(base_model2_out))])\n", + "\n", + " # adding CDL\n", + " Dense_L1 = Dense(1024, activation='relu', kernel_regularizer=l2(0.03))(combined)\n", + " Dropout_L1 = Dropout(0.4)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " combo_model = Model(inputs=common_input, outputs=predictions) \n", + " print('Total model layers: ', len(combo_model.layers))\n", + " \n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " combo_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return combo_model\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers_1 = 0\n", + "freeze_layers_2 = 0\n", + "model = Combo_Model(freeze_layers_1, freeze_layers_2)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rev1.4\n", + "```\n", + "recommended: ⚠️\n", + "statuses: Test\n", + "Working: βœ…\n", + "Max fine tuned acc: ⚠️\n", + "Max fine tuned acc TLRev2: β‰…95.64\n", + "type: transfer learning>>>(EfficientNetV2XL)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n", + " ) 'stack_0_block0_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n", + " ) 'stack_0_block1_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n", + " ) 'stack_0_block2_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n", + " ) ) \n", + " \n", + " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n", + " malization) ) ] \n", + " \n", + " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n", + " ation) ) \n", + " \n", + " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n", + " ) 'stack_0_block3_fu_swish[0][0] \n", + " '] \n", + " \n", + " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_1_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n", + " 'stack_1_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n", + " 'stack_1_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n", + " 'stack_1_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n", + " 'stack_1_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n", + " 'stack_1_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n", + " 'stack_1_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_2_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n", + " 'stack_2_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n", + " 'stack_2_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n", + " 'stack_2_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n", + " 'stack_2_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n", + " 'stack_2_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n", + " v2D) 0][0]'] \n", + " \n", + " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n", + " 'stack_2_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", + " a) [0]'] \n", + " \n", + " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_1[0][0]'] \n", + " \n", + " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_3[0][0]'] \n", + " \n", + " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_3_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_5[0][0]'] \n", + " \n", + " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n", + " 'stack_3_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_7[0][0]'] \n", + " \n", + " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n", + " 'stack_3_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_9[0][0]'] \n", + " \n", + " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n", + " 'stack_3_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_11[0][0]'] \n", + " \n", + " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n", + " 'stack_3_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_13[0][0]'] \n", + " \n", + " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n", + " 'stack_3_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_15[0][0]'] \n", + " \n", + " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n", + " 'stack_3_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_17[0][0]'] \n", + " \n", + " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n", + " 'stack_3_block8_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", + " bda) [0]'] \n", + " \n", + " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_19[0][0]'] \n", + " \n", + " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n", + " 'stack_3_block9_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_21[0][0]'] \n", + " \n", + " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n", + " 'stack_3_block10_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_23[0][0]'] \n", + " \n", + " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n", + " 'stack_3_block11_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_25[0][0]'] \n", + " \n", + " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n", + " 'stack_3_block12_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_27[0][0]'] \n", + " \n", + " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n", + " 'stack_3_block13_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_29[0][0]'] \n", + " \n", + " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n", + " 'stack_3_block14_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_31[0][0]'] \n", + " \n", + " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n", + " 'stack_3_block15_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_33[0][0]'] \n", + " \n", + " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n", + " onv2D) ) '] \n", + " \n", + " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_35[0][0]'] \n", + " \n", + " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_4_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_37[0][0]'] \n", + " \n", + " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n", + " 'stack_4_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_39[0][0]'] \n", + " \n", + " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n", + " 'stack_4_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_41[0][0]'] \n", + " \n", + " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n", + " 'stack_4_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_43[0][0]'] \n", + " \n", + " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n", + " 'stack_4_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_45[0][0]'] \n", + " \n", + " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n", + " 'stack_4_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_47[0][0]'] \n", + " \n", + " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n", + " 'stack_4_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_49[0][0]'] \n", + " \n", + " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n", + " 'stack_4_block8_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n", + " seConv2D) ) 0][0]'] \n", + " \n", + " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n", + " Normalization) ) \n", + " \n", + " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n", + " tivation) ) '] \n", + " \n", + " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n", + " ) [0]', \n", + " 'activation_51[0][0]'] \n", + " \n", + " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n", + " 'stack_4_block9_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_53[0][0]'] \n", + " \n", + " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n", + " 'stack_4_block10_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_55[0][0]'] \n", + " \n", + " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n", + " 'stack_4_block11_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_57[0][0]'] \n", + " \n", + " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n", + " 'stack_4_block12_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_59[0][0]'] \n", + " \n", + " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n", + " 'stack_4_block13_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_61[0][0]'] \n", + " \n", + " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n", + " 'stack_4_block14_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_63[0][0]'] \n", + " \n", + " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n", + " 'stack_4_block15_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_65[0][0]'] \n", + " \n", + " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n", + " 'stack_4_block16_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_67[0][0]'] \n", + " \n", + " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n", + " 'stack_4_block17_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_69[0][0]'] \n", + " \n", + " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n", + " 'stack_4_block18_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_71[0][0]'] \n", + " \n", + " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n", + " 'stack_4_block19_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_73[0][0]'] \n", + " \n", + " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n", + " 'stack_4_block20_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_75[0][0]'] \n", + " \n", + " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n", + " 'stack_4_block21_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_77[0][0]'] \n", + " \n", + " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n", + " 'stack_4_block22_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n", + " Conv2D) ) \n", + " \n", + " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n", + " tchNormalization) ) 0][0]'] \n", + " \n", + " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n", + " (Activation) ) [0]'] \n", + " \n", + " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n", + " iseConv2D) ) [0][0]'] \n", + " \n", + " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n", + " hNormalization) ) ] \n", + " \n", + " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n", + " ctivation) ) ]'] \n", + " \n", + " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n", + " ) ][0]', \n", + " 'activation_79[0][0]'] \n", + " \n", + " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n", + " 'stack_4_block23_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n", + " onv2D) ) \n", + " \n", + " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n", + " chNormalization) ) ][0]'] \n", + " \n", + " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n", + " Activation) ) 0]'] \n", + " \n", + " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_81[0][0]'] \n", + " \n", + " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_83[0][0]'] \n", + " \n", + " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_5_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_85[0][0]'] \n", + " \n", + " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n", + " 'stack_5_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_87[0][0]'] \n", + " \n", + " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n", + " 'stack_5_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_89[0][0]'] \n", + " \n", + " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n", + " 'stack_5_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_91[0][0]'] \n", + " \n", + " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n", + " 'stack_5_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_93[0][0]'] \n", + " \n", + " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n", + " 'stack_5_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_95[0][0]'] \n", + " \n", + " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n", + " 'stack_5_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_97[0][0]'] \n", + " \n", + " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n", + " 'stack_5_block8_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_99[0][0]'] \n", + " \n", + " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n", + " 'stack_5_block9_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_101[0][0]'] \n", + " \n", + " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n", + " 'stack_5_block10_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_103[0][0]'] \n", + " \n", + " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n", + " 'stack_5_block11_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_105[0][0]'] \n", + " \n", + " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n", + " 'stack_5_block12_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_107[0][0]'] \n", + " \n", + " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n", + " 'stack_5_block13_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_109[0][0]'] \n", + " \n", + " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n", + " 'stack_5_block14_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_111[0][0]'] \n", + " \n", + " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n", + " 'stack_5_block15_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_113[0][0]'] \n", + " \n", + " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n", + " 'stack_5_block16_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_115[0][0]'] \n", + " \n", + " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n", + " 'stack_5_block17_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_117[0][0]'] \n", + " \n", + " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n", + " 'stack_5_block18_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_119[0][0]'] \n", + " \n", + " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n", + " 'stack_5_block19_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_121[0][0]'] \n", + " \n", + " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n", + " 'stack_5_block20_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_123[0][0]'] \n", + " \n", + " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n", + " 'stack_5_block21_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_125[0][0]'] \n", + " \n", + " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n", + " 'stack_5_block22_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_127[0][0]'] \n", + " \n", + " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n", + " 'stack_5_block23_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_129[0][0]'] \n", + " \n", + " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n", + " 'stack_5_block24_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_131[0][0]'] \n", + " \n", + " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n", + " 'stack_5_block25_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_133[0][0]'] \n", + " \n", + " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n", + " 'stack_5_block26_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_135[0][0]'] \n", + " \n", + " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n", + " 'stack_5_block27_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_137[0][0]'] \n", + " \n", + " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n", + " 'stack_5_block28_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_139[0][0]'] \n", + " \n", + " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n", + " 'stack_5_block29_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_141[0][0]'] \n", + " \n", + " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n", + " 'stack_5_block30_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n", + " Conv2D) \n", + " \n", + " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n", + " tchNormalization) 0][0]'] \n", + " \n", + " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n", + " (Activation) [0]'] \n", + " \n", + " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n", + " iseConv2D) [0][0]'] \n", + " \n", + " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n", + " hNormalization) ] \n", + " \n", + " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n", + " ctivation) ]'] \n", + " \n", + " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", + " mbda) ][0]'] \n", + " \n", + " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n", + " v2D) ] \n", + " \n", + " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n", + " 0]'] \n", + " \n", + " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n", + " v2D) \n", + " \n", + " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n", + " 0]'] \n", + " \n", + " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n", + " ][0]', \n", + " 'activation_143[0][0]'] \n", + " \n", + " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n", + " nv2D) \n", + " \n", + " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n", + " hNormalization) [0]'] \n", + " \n", + " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n", + " 'stack_5_block31_MB_pw_bn[0][0 \n", + " ]'] \n", + " \n", + " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_145[0][0]'] \n", + " \n", + " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n", + " onv2D) '] \n", + " \n", + " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_147[0][0]'] \n", + " \n", + " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n", + " ', \n", + " 'stack_6_block1_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_149[0][0]'] \n", + " \n", + " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n", + " 'stack_6_block2_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_151[0][0]'] \n", + " \n", + " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n", + " 'stack_6_block3_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_153[0][0]'] \n", + " \n", + " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n", + " 'stack_6_block4_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_155[0][0]'] \n", + " \n", + " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n", + " 'stack_6_block5_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_157[0][0]'] \n", + " \n", + " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n", + " 'stack_6_block6_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n", + " onv2D) \n", + " \n", + " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n", + " chNormalization) ][0]'] \n", + " \n", + " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n", + " Activation) 0]'] \n", + " \n", + " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n", + " seConv2D) 0][0]'] \n", + " \n", + " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n", + " Normalization) \n", + " \n", + " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n", + " tivation) '] \n", + " \n", + " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", + " mbda) [0]'] \n", + " \n", + " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n", + " 2D) ] \n", + " \n", + " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n", + " ]'] \n", + " \n", + " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n", + " 2D) \n", + " \n", + " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n", + " ]'] \n", + " \n", + " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n", + " [0]', \n", + " 'activation_159[0][0]'] \n", + " \n", + " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n", + " v2D) \n", + " \n", + " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n", + " Normalization) 0]'] \n", + " \n", + " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n", + " 'stack_6_block7_MB_pw_bn[0][0] \n", + " '] \n", + " \n", + " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n", + " \n", + " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n", + " \n", + " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n", + " \n", + " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n", + " 2D) \n", + " \n", + " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n", + " \n", + " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 207,618,394\n", + "Trainable params: 206,841,370\n", + "Non-trainable params: 777,024\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from keras_efficientnet_v2 import EfficientNetV2XL\n", + "\n", + "EfficientNet_M = EfficientNetV2XL(input_shape=(img_res[0], img_res[1], img_res[2]), pretrained='imagenet21k-ft1k', num_classes=2, dropout=0.4)\n", + "# define new model\n", + "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n", + "\n", + "# compile model\n", + "opt = SGD(momentum=0.9)\n", + "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n", + "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "# opt = Adam()\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "freeze_layers = 0\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from efficientnet.keras import EfficientNetL2 as KENBL2\n", + "#FUNC\n", + "def Eff_B7_NS(freeze_layers):\n", + " base_model = KENBL2(input_shape=(img_res[0], img_res[1], img_res[2]),\n", + " weights='./download/Models/EFN_L2/efficientnet-l2_noisy-student_notop.h5',\n", + " include_top=False,\n", + " drop_connect_rate=0)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n", + " print(f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", + " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions) \n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer = opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return model_EfficientNetB7_NS\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B7_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T02:31:32.994176700Z", + "start_time": "2023-12-28T02:31:27.381088600Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total layers in the base model: 806\n", + "Freezing 0 layers in the base model...\n", + "Percentage of the base model that is frozen: 0.00%\n", + "Total model layers: 817\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " conv2d (Conv2D) (None, 7, 7, 64) 163904 ['top_activation[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 64) 0 ['conv2d[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 33280 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 256) 131328 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " batch_normalization_2 (BatchNo (None, 256) 1024 ['dense_2[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_3 (Dense) (None, 128) 32896 ['batch_normalization_2[0][0]'] Y \n", + " \n", + " dense_4 (Dense) (None, 2) 258 ['dense_3[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 64,727,122\n", + "Trainable params: 64,413,842\n", + "Non-trainable params: 313,280\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB7 as KENB7\n", + "# FUNC\n", + "def Eff_B7_NS(freeze_layers):\n", + " base_model = KENB7(input_shape=(\n", + " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) /\n", + " len(base_model.layers)) * 100\n", + " print(\n", + " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " # base\n", + " base_model_FT_Conv = Conv2D(64, (1, 1))(base_model.output)\n", + " base_model_FT = GlobalAveragePooling2D()(base_model_FT_Conv)\n", + " # L1\n", + " Dense_L1 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", + " # L2\n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " # L3\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(256, activation='relu')(BatchNorm_L3)\n", + " # L3\n", + " BatchNorm_L4 = BatchNormalization()(Dense_L3)\n", + " Dense_L4 = Dense(128, activation='relu')(BatchNorm_L4)\n", + " # L(end)\n", + " # predictions = Dense(2, activation='softmax')(Dense_L4) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L4)\n", + "\n", + " model_EfficientNetB7_NS = Model(\n", + " inputs=base_model.input, outputs=predictions)\n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " # OPT/compile\n", + " opt = SGD(momentum=0.9, nesterov=False)\n", + " # opt = Nadam()\n", + " # opt = Adamax()\n", + " # opt = RMSprop(momentum=0.9)\n", + " # opt = Adagrad()\n", + " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_B7_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total model layers: 11\n", + "Model: \"model\"\n", + "____________________________________________________________________________\n", + " Layer (type) Output Shape Param # Trainable \n", + "============================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3)] 0 Y \n", + " \n", + " lambda (Lambda) (None, 224, 224, 3) 0 Y \n", + " \n", + " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n", + " ) 8 \n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n", + "| |\n", + "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n", + "| malization (Normalization) |\n", + "| |\n", + "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n", + "| ntial) |\n", + "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", + "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n", + "|| Conv2D) ||\n", + "|| ||\n", + "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n", + "|| orm (LayerNormalization) ||\n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", + "| ck_0_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", + "| ck_0_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", + "| ck_0_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", + "| ck_0_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", + "| ck_0_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", + "| ck_0_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", + "| ck_0_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n", + "| ambda) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", + "| ck_1_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", + "| ck_1_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", + "| ck_1_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", + "| ck_1_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", + "| ck_1_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", + "| ck_1_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", + "| ck_1_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n", + "| ck_2_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n", + "| ck_2_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n", + "| ck_2_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n", + "| ck_2_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n", + "| ck_2_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n", + "| ck_2_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n", + "| ck_2_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n", + "| g_block_0 (Sequential) |\n", + "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n", + "|| g_layernorm_0 (LayerNormali ||\n", + "|| zation) ||\n", + "|| ||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n", + "|| g_conv_0 (Conv2D) ||\n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", + "| ck_0_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", + "| ck_0_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", + "| ck_0_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", + "| ck_0_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", + "| ck_0_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", + "| ck_0_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", + "| ck_0_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", + "| ck_1_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", + "| ck_1_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", + "| ck_1_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", + "| ck_1_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", + "| ck_1_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", + "| ck_1_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", + "| ck_1_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n", + "| ck_2_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n", + "| ck_2_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n", + "| ck_2_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n", + "| ck_2_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n", + "| ck_2_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n", + "| ck_2_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n", + "| ck_2_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n", + "| g_block_1 (Sequential) |\n", + "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n", + "|| g_layernorm_1 (LayerNormali ||\n", + "|| zation) ||\n", + "|| ||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n", + "|| g_conv_1 (Conv2D) ||\n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_0_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_0_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_0_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_0_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_0_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_0_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_0_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_1_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_1_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_1_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_1_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_1_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_1_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_1_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_2_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_2_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_2_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_2_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_2_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_2_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_2_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_3_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_3_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_3_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_3_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_3_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_3_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_3_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n", + "| pLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_4_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_4_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_4_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_4_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_4_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_4_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_4_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_5_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_5_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_5_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_5_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_5_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_5_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_5_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_6_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_6_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_6_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_6_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_6_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_6_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_6_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_7_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_7_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_7_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_7_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_7_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_7_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_7_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_8_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_8_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_8_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_8_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_8_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_8_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_8_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_9_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_9_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_9_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_9_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_9_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_9_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_9_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_10_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_10_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_10_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_10_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_10_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_10_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_10_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_11_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_11_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_11_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_11_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_11_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_11_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_11_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_12_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_12_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_12_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_12_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_12_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_12_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_12_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_13_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_13_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_13_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_13_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_13_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_13_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_13_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_14_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_14_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_14_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_14_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_14_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_14_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_14_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_15_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_15_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_15_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_15_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_15_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_15_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_15_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_16_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_16_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_16_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_16_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_16_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_16_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_16_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_17_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_17_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_17_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_17_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_17_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_17_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_17_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_18_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_18_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_18_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_18_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_18_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_18_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_18_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_19_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_19_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_19_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_19_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_19_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_19_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_19_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_20_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_20_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_20_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_20_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_20_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_20_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_20_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_21_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_21_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_21_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_21_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_21_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_21_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_21_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_22_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_22_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_22_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_22_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_22_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_22_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_22_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_23_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_23_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_23_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_23_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_23_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_23_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_23_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_24_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_24_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_24_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_24_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_24_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_24_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_24_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_25_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_25_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_25_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_25_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_25_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_25_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_25_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n", + "| ck_26_depthwise_conv (Conv2 |\n", + "| D) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n", + "| ck_26_layernorm (LayerNorma |\n", + "| lization) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n", + "| ck_26_pointwise_conv_1 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n", + "| ck_26_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n", + "| ck_26_pointwise_conv_2 (Den |\n", + "| se) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n", + "| ck_26_layer_scale (LayerSca |\n", + "| le) |\n", + "| |\n", + "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n", + "| ck_26_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n", + "| g_block_2 (Sequential) |\n", + "||Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n", + "|| g_layernorm_2 (LayerNormali ||\n", + "|| zation) ||\n", + "|| ||\n", + "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n", + "|| g_conv_2 (Conv2D) ||\n", + "|Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―|\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", + "| ck_0_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", + "| ck_0_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", + "| ck_0_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", + "| ck_0_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", + "| ck_0_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", + "| ck_0_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", + "| ck_0_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", + "| ck_1_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", + "| ck_1_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", + "| ck_1_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", + "| ck_1_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", + "| ck_1_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", + "| ck_1_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", + "| ck_1_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n", + "| ck_2_depthwise_conv (Conv2D |\n", + "| ) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n", + "| ck_2_layernorm (LayerNormal |\n", + "| ization) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n", + "| ck_2_pointwise_conv_1 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n", + "| ck_2_gelu (Activation) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n", + "| ck_2_pointwise_conv_2 (Dens |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n", + "| ck_2_layer_scale (LayerScal |\n", + "| e) |\n", + "| |\n", + "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n", + "| ck_2_identity (Activation) |\n", + "| |\n", + "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n", + "| OpLambda) |\n", + "| |\n", + "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n", + "| ormalization) |\n", + "Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―\n", + " global_average_pooling2d (G (None, 2048) 0 Y \n", + " lobalAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1049088 Y \n", + " \n", + " dropout (Dropout) (None, 512) 0 Y \n", + " \n", + " batch_normalization (BatchN (None, 512) 2048 Y \n", + " ormalization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 Y \n", + " \n", + " batch_normalization_1 (Batc (None, 512) 2048 Y \n", + " hNormalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 Y \n", + " \n", + "============================================================================\n", + "Total params: 349,529,730\n", + "Trainable params: 349,527,682\n", + "Non-trainable params: 2,048\n", + "____________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from keras.applications import ConvNeXtXLarge\n", + "from keras.layers import Lambda\n", + "#FUNC\n", + "def Eff_B7_NS():\n", + " # Add a Lambda layer at the beginning to scale the input\n", + " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n", + " x = Lambda(lambda image: image * 255)(input)\n", + " \n", + " base_model = ConvNeXtXLarge(include_top=False, weights='imagenet', classes=2, classifier_activation='softmax', include_preprocessing=True)(x)\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model)\n", + " Dense_L1 = Dense(512, activation='relu', kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1) \n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu', kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions) \n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " #OPT/compile\n", + " opt = SGD(momentum=0.9)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "model = Eff_B7_NS()\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### V(T) Beta4" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating the model...\n", + "Total layers in the base model: 806\n", + "Freezing 0 layers in the base model...\n", + "Percentage of the base model that is frozen: 0.00%\n", + "Total model layers: 814\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 65,428,818\n", + "Non-trainable params: 312,768\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "from efficientnet.keras import EfficientNetB7 as EFK_GN\n", + "# FUNC\n", + "def Eff_BS_NS(freeze_layers):\n", + " base_model = EFK_GN(input_shape=(\n", + " img_res[0], img_res[1], img_res[2]), weights='noisy-student', include_top=False)\n", + " print('Total layers in the base model: ', len(base_model.layers))\n", + " print(f'Freezing {freeze_layers} layers in the base model...')\n", + " # Freeze the specified number of layers\n", + " for layer in base_model.layers[:freeze_layers]:\n", + " layer.trainable = False\n", + "\n", + " # Unfreeze the rest\n", + " for layer in base_model.layers[freeze_layers:]:\n", + " layer.trainable = True\n", + "\n", + " # Calculate the percentage of the model that is frozen\n", + " frozen_percentage = ((freeze_layers + 1e-10) /\n", + " len(base_model.layers)) * 100\n", + " print(\n", + " f'Percentage of the base model that is frozen: {frozen_percentage:.2f}%')\n", + " # adding CDL\n", + " base_model_FT = GlobalAveragePooling2D()(base_model.output)\n", + " Dense_L1 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.02))(base_model_FT)\n", + " Dropout_L1 = Dropout(0.1)(Dense_L1)\n", + " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n", + " Dense_L2 = Dense(512, activation='relu',\n", + " kernel_regularizer=l2(0.01))(BatchNorm_L2)\n", + " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n", + " Dense_L3 = Dense(128, activation='relu')(BatchNorm_L3)\n", + " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n", + " predictions = Dense(2, activation='softmax')(Dense_L3)\n", + "\n", + " model_EfficientNetB7_NS = Model(\n", + " inputs=base_model.input, outputs=predictions)\n", + " print('Total model layers: ', len(model_EfficientNetB7_NS.layers))\n", + " # OPT/compile\n", + " opt = SGD(momentum=0.9, nesterov=False)\n", + " # opt = Nadam()\n", + " # opt = Adamax()\n", + " # opt = RMSprop(momentum=0.9)\n", + " # opt = Adagrad()\n", + " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n", + " # opt = Yogi()\n", + " model_EfficientNetB7_NS.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy']) # categorical_crossentropy / binary_crossentropy\n", + "\n", + " return model_EfficientNetB7_NS\n", + "\n", + "print('Creating the model...')\n", + "# Main\n", + "freeze_layers = 0\n", + "model = Eff_BS_NS(freeze_layers)\n", + "model.summary(show_trainable=True, expand_nested=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LR FINDER" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "#CONF/Other\n", + "LRF_OPT = SGD(momentum=0.9)\n", + "LFR_batch_size = 1 # or any other batch size that fits in your memory\n", + "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n", + "# Instantiate LrFinder\n", + "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n", + "\n", + "# Start range_test\n", + "lr_find.range_test(LRF_dataset)\n", + "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model vis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dot_img_file = 'model_1.png'\n", + "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the full model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92mLoading model done.\n", + "Compiling the AI model...\u001b[0m\n", + "Model: \"model\"\n", + "_____________________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to Trainable \n", + "=============================================================================================================\n", + " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n", + " )] \n", + " \n", + " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n", + " ) \n", + " \n", + " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n", + " ) \n", + " \n", + " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n", + " \n", + " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n", + " \n", + " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n", + " \n", + " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n", + " ) 'block1a_se_expand[0][0]'] \n", + " \n", + " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n", + " \n", + " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n", + " \n", + " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n", + " \n", + " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n", + " ) 'block1b_se_expand[0][0]'] \n", + " \n", + " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n", + " ) 'block1a_project_bn[0][0]'] \n", + " \n", + " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n", + " \n", + " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n", + " \n", + " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n", + " \n", + " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n", + " ) 'block1c_se_expand[0][0]'] \n", + " \n", + " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n", + " ) 'block1b_add[0][0]'] \n", + " \n", + " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n", + " D) ) \n", + " \n", + " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n", + " \n", + " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n", + " \n", + " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n", + " \n", + " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n", + " ) 'block1d_se_expand[0][0]'] \n", + " \n", + " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n", + " lization) ) \n", + " \n", + " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n", + " ) 'block1c_add[0][0]'] \n", + " \n", + " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n", + " 2) \n", + " \n", + " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n", + " ization) 2) \n", + " \n", + " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n", + " ivation) 2) \n", + " \n", + " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n", + " \n", + " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n", + " \n", + " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n", + " \n", + " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n", + " 'block2a_se_expand[0][0]'] \n", + " \n", + " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n", + " \n", + " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n", + " \n", + " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n", + " \n", + " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n", + " \n", + " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n", + " \n", + " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n", + " 'block2b_se_expand[0][0]'] \n", + " \n", + " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n", + " \n", + " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n", + " \n", + " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n", + " 'block2a_project_bn[0][0]'] \n", + " \n", + " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n", + " \n", + " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n", + " \n", + " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n", + " \n", + " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n", + " \n", + " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n", + " 'block2c_se_expand[0][0]'] \n", + " \n", + " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n", + " \n", + " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n", + " \n", + " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n", + " 'block2b_add[0][0]'] \n", + " \n", + " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n", + " \n", + " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n", + " \n", + " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n", + " \n", + " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n", + " \n", + " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n", + " 'block2d_se_expand[0][0]'] \n", + " \n", + " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n", + " \n", + " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n", + " \n", + " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n", + " 'block2c_add[0][0]'] \n", + " \n", + " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n", + " \n", + " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n", + " \n", + " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n", + " \n", + " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n", + " \n", + " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n", + " 'block2e_se_expand[0][0]'] \n", + " \n", + " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n", + " \n", + " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n", + " \n", + " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n", + " 'block2d_add[0][0]'] \n", + " \n", + " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n", + " \n", + " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n", + " \n", + " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n", + " \n", + " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n", + " \n", + " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n", + " 'block2f_se_expand[0][0]'] \n", + " \n", + " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n", + " \n", + " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n", + " \n", + " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n", + " 'block2e_add[0][0]'] \n", + " \n", + " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n", + " \n", + " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n", + " \n", + " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n", + " \n", + " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n", + " \n", + " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n", + " 'block2g_se_expand[0][0]'] \n", + " \n", + " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n", + " \n", + " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n", + " \n", + " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n", + " 'block2f_add[0][0]'] \n", + " \n", + " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n", + " \n", + " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n", + " \n", + " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n", + " \n", + " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n", + " \n", + " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n", + " 'block3a_se_expand[0][0]'] \n", + " \n", + " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n", + " \n", + " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n", + " \n", + " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n", + " \n", + " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n", + " \n", + " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n", + " \n", + " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n", + " 'block3b_se_expand[0][0]'] \n", + " \n", + " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n", + " \n", + " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n", + " \n", + " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n", + " 'block3a_project_bn[0][0]'] \n", + " \n", + " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n", + " \n", + " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n", + " \n", + " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n", + " \n", + " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n", + " \n", + " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n", + " 'block3c_se_expand[0][0]'] \n", + " \n", + " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n", + " \n", + " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n", + " \n", + " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n", + " 'block3b_add[0][0]'] \n", + " \n", + " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n", + " \n", + " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n", + " \n", + " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n", + " \n", + " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n", + " \n", + " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n", + " 'block3d_se_expand[0][0]'] \n", + " \n", + " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n", + " \n", + " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n", + " \n", + " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n", + " 'block3c_add[0][0]'] \n", + " \n", + " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n", + " \n", + " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n", + " \n", + " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n", + " \n", + " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n", + " \n", + " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n", + " 'block3e_se_expand[0][0]'] \n", + " \n", + " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n", + " \n", + " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n", + " \n", + " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n", + " 'block3d_add[0][0]'] \n", + " \n", + " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n", + " \n", + " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n", + " \n", + " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n", + " \n", + " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n", + " \n", + " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n", + " 'block3f_se_expand[0][0]'] \n", + " \n", + " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n", + " \n", + " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n", + " \n", + " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n", + " 'block3e_add[0][0]'] \n", + " \n", + " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n", + " \n", + " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n", + " \n", + " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n", + " \n", + " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n", + " \n", + " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n", + " 'block3g_se_expand[0][0]'] \n", + " \n", + " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n", + " \n", + " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n", + " \n", + " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n", + " 'block3f_add[0][0]'] \n", + " \n", + " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n", + " \n", + " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n", + " \n", + " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n", + " \n", + " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n", + " \n", + " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n", + " 'block4a_se_expand[0][0]'] \n", + " \n", + " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n", + " \n", + " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n", + " \n", + " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n", + " \n", + " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n", + " \n", + " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n", + " \n", + " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n", + " 'block4b_se_expand[0][0]'] \n", + " \n", + " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n", + " \n", + " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n", + " \n", + " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n", + " 'block4a_project_bn[0][0]'] \n", + " \n", + " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n", + " \n", + " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n", + " \n", + " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n", + " \n", + " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n", + " \n", + " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n", + " 'block4c_se_expand[0][0]'] \n", + " \n", + " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n", + " \n", + " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n", + " \n", + " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n", + " 'block4b_add[0][0]'] \n", + " \n", + " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n", + " \n", + " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n", + " \n", + " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n", + " \n", + " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n", + " \n", + " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n", + " 'block4d_se_expand[0][0]'] \n", + " \n", + " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n", + " \n", + " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n", + " \n", + " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n", + " 'block4c_add[0][0]'] \n", + " \n", + " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n", + " \n", + " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n", + " \n", + " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n", + " \n", + " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n", + " \n", + " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n", + " 'block4e_se_expand[0][0]'] \n", + " \n", + " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n", + " \n", + " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n", + " \n", + " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n", + " 'block4d_add[0][0]'] \n", + " \n", + " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n", + " \n", + " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n", + " \n", + " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n", + " \n", + " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n", + " \n", + " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n", + " 'block4f_se_expand[0][0]'] \n", + " \n", + " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n", + " \n", + " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n", + " \n", + " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n", + " 'block4e_add[0][0]'] \n", + " \n", + " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n", + " \n", + " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n", + " \n", + " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n", + " \n", + " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n", + " \n", + " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n", + " 'block4g_se_expand[0][0]'] \n", + " \n", + " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n", + " \n", + " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n", + " \n", + " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n", + " 'block4f_add[0][0]'] \n", + " \n", + " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n", + " \n", + " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n", + " \n", + " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n", + " \n", + " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n", + " \n", + " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n", + " 'block4h_se_expand[0][0]'] \n", + " \n", + " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n", + " \n", + " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n", + " \n", + " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n", + " 'block4g_add[0][0]'] \n", + " \n", + " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n", + " \n", + " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n", + " \n", + " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n", + " \n", + " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n", + " \n", + " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n", + " 'block4i_se_expand[0][0]'] \n", + " \n", + " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n", + " \n", + " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n", + " \n", + " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n", + " 'block4h_add[0][0]'] \n", + " \n", + " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n", + " \n", + " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n", + " \n", + " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n", + " \n", + " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n", + " \n", + " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n", + " 'block4j_se_expand[0][0]'] \n", + " \n", + " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n", + " \n", + " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n", + " \n", + " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n", + " 'block4i_add[0][0]'] \n", + " \n", + " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n", + " \n", + " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n", + " \n", + " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n", + " \n", + " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n", + " \n", + " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n", + " 'block5a_se_expand[0][0]'] \n", + " \n", + " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n", + " \n", + " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n", + " \n", + " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n", + " \n", + " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n", + " \n", + " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n", + " ) 'block5b_se_expand[0][0]'] \n", + " \n", + " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n", + " \n", + " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n", + " \n", + " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n", + " 'block5a_project_bn[0][0]'] \n", + " \n", + " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n", + " \n", + " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n", + " \n", + " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n", + " \n", + " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n", + " ) 'block5c_se_expand[0][0]'] \n", + " \n", + " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n", + " \n", + " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n", + " \n", + " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n", + " 'block5b_add[0][0]'] \n", + " \n", + " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n", + " \n", + " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n", + " \n", + " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n", + " \n", + " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n", + " ) 'block5d_se_expand[0][0]'] \n", + " \n", + " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n", + " \n", + " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n", + " \n", + " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n", + " 'block5c_add[0][0]'] \n", + " \n", + " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n", + " \n", + " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n", + " \n", + " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n", + " \n", + " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n", + " ) 'block5e_se_expand[0][0]'] \n", + " \n", + " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n", + " \n", + " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n", + " \n", + " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n", + " 'block5d_add[0][0]'] \n", + " \n", + " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n", + " \n", + " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n", + " \n", + " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n", + " \n", + " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n", + " ) 'block5f_se_expand[0][0]'] \n", + " \n", + " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n", + " \n", + " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n", + " \n", + " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n", + " 'block5e_add[0][0]'] \n", + " \n", + " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n", + " \n", + " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n", + " \n", + " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n", + " \n", + " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n", + " ) 'block5g_se_expand[0][0]'] \n", + " \n", + " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n", + " \n", + " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n", + " \n", + " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n", + " 'block5f_add[0][0]'] \n", + " \n", + " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n", + " \n", + " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n", + " \n", + " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n", + " \n", + " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n", + " ) 'block5h_se_expand[0][0]'] \n", + " \n", + " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n", + " \n", + " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n", + " \n", + " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n", + " 'block5g_add[0][0]'] \n", + " \n", + " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n", + " \n", + " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n", + " \n", + " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n", + " \n", + " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n", + " ) 'block5i_se_expand[0][0]'] \n", + " \n", + " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n", + " \n", + " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n", + " \n", + " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n", + " 'block5h_add[0][0]'] \n", + " \n", + " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n", + " ) \n", + " \n", + " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n", + " D) ) 0]'] \n", + " \n", + " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n", + " ) ) \n", + " \n", + " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n", + " \n", + " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n", + " \n", + " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n", + " \n", + " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n", + " ) 'block5j_se_expand[0][0]'] \n", + " \n", + " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n", + " \n", + " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n", + " \n", + " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n", + " 'block5i_add[0][0]'] \n", + " \n", + " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n", + " ization) ) \n", + " \n", + " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n", + " ivation) ) \n", + " \n", + " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n", + " \n", + " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n", + " \n", + " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n", + " \n", + " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n", + " 'block6a_se_expand[0][0]'] \n", + " \n", + " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n", + " \n", + " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n", + " \n", + " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n", + " \n", + " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n", + " \n", + " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n", + " \n", + " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n", + " 'block6b_se_expand[0][0]'] \n", + " \n", + " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n", + " \n", + " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n", + " \n", + " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n", + " 'block6a_project_bn[0][0]'] \n", + " \n", + " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n", + " \n", + " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n", + " \n", + " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n", + " \n", + " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n", + " \n", + " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n", + " 'block6c_se_expand[0][0]'] \n", + " \n", + " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n", + " \n", + " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n", + " \n", + " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n", + " 'block6b_add[0][0]'] \n", + " \n", + " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n", + " \n", + " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n", + " \n", + " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n", + " \n", + " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n", + " \n", + " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n", + " 'block6d_se_expand[0][0]'] \n", + " \n", + " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n", + " \n", + " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n", + " \n", + " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n", + " 'block6c_add[0][0]'] \n", + " \n", + " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n", + " \n", + " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n", + " \n", + " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n", + " \n", + " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n", + " \n", + " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n", + " 'block6e_se_expand[0][0]'] \n", + " \n", + " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n", + " \n", + " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n", + " \n", + " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n", + " 'block6d_add[0][0]'] \n", + " \n", + " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n", + " \n", + " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n", + " \n", + " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n", + " \n", + " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n", + " \n", + " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n", + " 'block6f_se_expand[0][0]'] \n", + " \n", + " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n", + " \n", + " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n", + " \n", + " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n", + " 'block6e_add[0][0]'] \n", + " \n", + " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n", + " \n", + " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n", + " \n", + " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n", + " \n", + " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n", + " \n", + " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n", + " 'block6g_se_expand[0][0]'] \n", + " \n", + " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n", + " \n", + " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n", + " \n", + " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n", + " 'block6f_add[0][0]'] \n", + " \n", + " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n", + " \n", + " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n", + " \n", + " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n", + " \n", + " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n", + " \n", + " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n", + " 'block6h_se_expand[0][0]'] \n", + " \n", + " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n", + " \n", + " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n", + " \n", + " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n", + " 'block6g_add[0][0]'] \n", + " \n", + " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n", + " \n", + " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n", + " \n", + " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n", + " \n", + " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n", + " \n", + " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n", + " 'block6i_se_expand[0][0]'] \n", + " \n", + " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n", + " \n", + " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n", + " \n", + " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n", + " 'block6h_add[0][0]'] \n", + " \n", + " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n", + " \n", + " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n", + " \n", + " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n", + " \n", + " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n", + " \n", + " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n", + " 'block6j_se_expand[0][0]'] \n", + " \n", + " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n", + " \n", + " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n", + " \n", + " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n", + " 'block6i_add[0][0]'] \n", + " \n", + " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n", + " \n", + " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n", + " \n", + " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n", + " \n", + " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n", + " \n", + " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n", + " 'block6k_se_expand[0][0]'] \n", + " \n", + " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n", + " \n", + " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n", + " \n", + " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n", + " 'block6j_add[0][0]'] \n", + " \n", + " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n", + " \n", + " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n", + " \n", + " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n", + " \n", + " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n", + " \n", + " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n", + " 'block6l_se_expand[0][0]'] \n", + " \n", + " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n", + " \n", + " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n", + " \n", + " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n", + " 'block6k_add[0][0]'] \n", + " \n", + " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n", + " \n", + " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n", + " \n", + " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n", + " \n", + " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n", + " \n", + " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n", + " 'block6m_se_expand[0][0]'] \n", + " \n", + " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n", + " \n", + " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n", + " \n", + " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n", + " 'block6l_add[0][0]'] \n", + " \n", + " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n", + " \n", + " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n", + " \n", + " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n", + " \n", + " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n", + " \n", + " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n", + " 'block7a_se_expand[0][0]'] \n", + " \n", + " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n", + " \n", + " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n", + " \n", + " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n", + " \n", + " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n", + " \n", + " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n", + " \n", + " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n", + " 'block7b_se_expand[0][0]'] \n", + " \n", + " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n", + " \n", + " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n", + " \n", + " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n", + " 'block7a_project_bn[0][0]'] \n", + " \n", + " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n", + " \n", + " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n", + " \n", + " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n", + " \n", + " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n", + " \n", + " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n", + " 'block7c_se_expand[0][0]'] \n", + " \n", + " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n", + " \n", + " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n", + " \n", + " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n", + " 'block7b_add[0][0]'] \n", + " \n", + " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n", + " \n", + " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n", + " ization) \n", + " \n", + " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n", + " ivation) \n", + " \n", + " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n", + " D) 0]'] \n", + " \n", + " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n", + " ) \n", + " \n", + " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n", + " agePooling2D) \n", + " \n", + " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n", + " \n", + " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n", + " \n", + " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n", + " \n", + " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n", + " 'block7d_se_expand[0][0]'] \n", + " \n", + " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n", + " \n", + " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n", + " lization) \n", + " \n", + " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n", + " \n", + " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n", + " 'block7c_add[0][0]'] \n", + " \n", + " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n", + " \n", + " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n", + " \n", + " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n", + " \n", + " global_average_pooling2d (Glob (None, 2560) 0 ['top_activation[0][0]'] Y \n", + " alAveragePooling2D) \n", + " \n", + " dense (Dense) (None, 512) 1311232 ['global_average_pooling2d[0][0 Y \n", + " ]'] \n", + " \n", + " dropout (Dropout) (None, 512) 0 ['dense[0][0]'] Y \n", + " \n", + " batch_normalization (BatchNorm (None, 512) 2048 ['dropout[0][0]'] Y \n", + " alization) \n", + " \n", + " dense_1 (Dense) (None, 512) 262656 ['batch_normalization[0][0]'] Y \n", + " \n", + " batch_normalization_1 (BatchNo (None, 512) 2048 ['dense_1[0][0]'] Y \n", + " rmalization) \n", + " \n", + " dense_2 (Dense) (None, 128) 65664 ['batch_normalization_1[0][0]'] Y \n", + " \n", + " dense_3 (Dense) (None, 2) 258 ['dense_2[0][0]'] Y \n", + " \n", + "=============================================================================================================\n", + "Total params: 65,741,586\n", + "Trainable params: 65,428,818\n", + "Non-trainable params: 312,768\n", + "_____________________________________________________________________________________________________________\n", + "done.\n" + ] + } + ], + "source": [ + "import efficientnet.tfkeras\n", + "# Configuration\n", + "PRMC = False\n", + "freeze_from_opposite = False\n", + "Extra_EXT = '_T'\n", + "freeze_layers = 0 \n", + "randomly_frozen_layers = 0 \n", + "freeze_last_seven = False \n", + "# CEC_opt = Adagrad()\n", + "# CEC_opt = Yogi()\n", + "# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n", + "CEC_opt = SGD(momentum=0.9, nesterov=False)\n", + "# CEC_opt = Adam()\n", + "# Main\n", + "try:\n", + " if SAVE_TYPE == 'TF':\n", + " model = load_model(f'PAI_model{Extra_EXT}', compile=PRMC)\n", + " else:\n", + " model = load_model(f'PAI_model{Extra_EXT}.h5', compile=PRMC)\n", + "except (ImportError, IOError) as e:\n", + " print(f'\\033[91mfailed to load the model ERROR:\\n{e}')\n", + "else:\n", + " print('\\033[92mLoading model done.')\n", + " if not PRMC:\n", + " print('Compiling the AI model...\\033[0m')\n", + " \n", + " for layer in model.layers:\n", + " layer.trainable = True\n", + " \n", + " # Select random layers to freeze\n", + " frozen_layer_indices = random.sample(range(len(model.layers)), randomly_frozen_layers)\n", + " \n", + " for i, layer in enumerate(model.layers):\n", + " if i in frozen_layer_indices:\n", + " layer.trainable = False\n", + " else:\n", + " if freeze_from_opposite and (i > len(model.layers) - freeze_layers):\n", + " layer.trainable = False\n", + " elif (not freeze_from_opposite) and i < freeze_layers:\n", + " layer.trainable = False\n", + " else:\n", + " layer.trainable = True\n", + " \n", + " for layer in model.layers[-7:]:\n", + " layer.trainable = not freeze_last_seven\n", + " \n", + " model.compile(optimizer=CEC_opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + " model.summary(show_trainable=True, expand_nested=True)\n", + " print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading model weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.load_weights('PAI_model_weights.h5')\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reset FC" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "for layer in model.layers[-7:]:\n", + " if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):\n", + " weight_initializer = layer.kernel_initializer\n", + " bias_initializer = layer.bias_initializer\n", + "\n", + " old_weights, old_biases = layer.get_weights()\n", + "\n", + " layer.set_weights([\n", + " weight_initializer(shape=old_weights.shape),\n", + " bias_initializer(shape=len(old_biases))\n", + " ])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Rev2 (THE BEST)\n", + "```\n", + "Working: βœ…\n", + "Other:\n", + " + Tensorboard works.\n", + " + Perverts overfitting.\n", + " + Lower memory usage.\n", + " - Slow training.\n", + " + Achieving higher acc.\n", + " - Some models dont work.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T07:04:23.573633300Z", + "start_time": "2023-12-28T02:31:32.468641900Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training the model...\n", + "\u001b[0;33m\n", + "Setup Verbose:\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m01_d01-h22_m20_s51]\u001b[0m\u001b[0;36m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n", + "\u001b[0;33mSetup Verbose END.\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n", + "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d01-h22_m25_s57\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 1/6\n", + " 83/512 [===>..........................] - ETA: 1:14 - loss: 22.1201 - accuracy: 0.9096\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 162s 287ms/step - loss: 16.1137 - accuracy: 0.9294 - val_loss: 7.3855 - val_accuracy: 0.9391\n", + "Epoch 2/6\n", + "512/512 [==============================] - 81s 159ms/step - loss: 3.2671 - accuracy: 0.9207 - val_loss: 1.1589 - val_accuracy: 0.9567\n", + "Epoch 3/6\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.6977 - accuracy: 0.9285 - val_loss: 0.3492 - val_accuracy: 0.9487\n", + "Epoch 4/6\n", + "173/512 [=========>....................] - ETA: 48s - loss: 0.3754 - accuracy: 0.9400\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 141s 276ms/step - loss: 0.3473 - accuracy: 0.9375 - val_loss: 0.3538 - val_accuracy: 0.9359\n", + "Epoch 5/6\n", + "357/512 [===================>..........] - ETA: 22s - loss: 0.2294 - accuracy: 0.9534\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 142s 279ms/step - loss: 0.2169 - accuracy: 0.9573 - val_loss: 0.3157 - val_accuracy: 0.9247\n", + "Epoch 6/6\n", + "361/512 [====================>.........] - ETA: 21s - loss: 0.1484 - accuracy: 0.9768\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 142s 278ms/step - loss: 0.1411 - accuracy: 0.9778 - val_loss: 0.3112 - val_accuracy: 0.9327\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-002-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.1589\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9567307829856873\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32minf \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m1.15892493724823\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m1075.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m750.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m324.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 7/12\n", + "512/512 [==============================] - 87s 163ms/step - loss: 0.9661 - accuracy: 0.9292 - val_loss: 0.6673 - val_accuracy: 0.9247\n", + "Epoch 8/12\n", + "387/512 [=====================>........] - ETA: 17s - loss: 0.4921 - accuracy: 0.9335\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 143s 279ms/step - loss: 0.4738 - accuracy: 0.9272 - val_loss: 0.3640 - val_accuracy: 0.9439\n", + "Epoch 9/12\n", + "487/512 [===========================>..] - ETA: 3s - loss: 0.3143 - accuracy: 0.9302\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 142s 277ms/step - loss: 0.3151 - accuracy: 0.9304 - val_loss: 0.2449 - val_accuracy: 0.9391\n", + "Epoch 10/12\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2833 - accuracy: 0.9417 - val_loss: 0.4342 - val_accuracy: 0.7885\n", + "Epoch 11/12\n", + "177/512 [=========>....................] - ETA: 47s - loss: 0.2635 - accuracy: 0.9456\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 142s 278ms/step - loss: 0.2145 - accuracy: 0.9580 - val_loss: 0.2247 - val_accuracy: 0.9311\n", + "Epoch 12/12\n", + "229/512 [============>.................] - ETA: 39s - loss: 0.1506 - accuracy: 0.9678\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 142s 277ms/step - loss: 0.1272 - accuracy: 0.9727 - val_loss: 0.2158 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-008-0.9439.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3640\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m1.15892493724823 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.36395618319511414\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m809.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m738.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.20 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 13/18\n", + "512/512 [==============================] - 87s 164ms/step - loss: 0.3633 - accuracy: 0.9158 - val_loss: 0.4034 - val_accuracy: 0.9087\n", + "Epoch 14/18\n", + "453/512 [=========================>....] - ETA: 8s - loss: 0.4066 - accuracy: 0.8949\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 141s 275ms/step - loss: 0.4076 - accuracy: 0.8923 - val_loss: 0.4474 - val_accuracy: 0.7869\n", + "Epoch 15/18\n", + "349/512 [===================>..........] - ETA: 23s - loss: 0.3081 - accuracy: 0.9208\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 141s 276ms/step - loss: 0.3111 - accuracy: 0.9231 - val_loss: 0.2624 - val_accuracy: 0.9263\n", + "Epoch 16/18\n", + "479/512 [===========================>..] - ETA: 4s - loss: 0.2480 - accuracy: 0.9423\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 143s 280ms/step - loss: 0.2471 - accuracy: 0.9431 - val_loss: 0.2339 - val_accuracy: 0.9455\n", + "Epoch 17/18\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2019 - accuracy: 0.9548 - val_loss: 0.1786 - val_accuracy: 0.9439\n", + "Epoch 18/18\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1278 - accuracy: 0.9753 - val_loss: 0.1961 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-018-0.9503.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1961\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.36395618319511414 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.19614851474761963\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m750.91 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m678.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 19/24\n", + "512/512 [==============================] - 87s 163ms/step - loss: 0.2687 - accuracy: 0.9233 - val_loss: 0.2169 - val_accuracy: 0.9535\n", + "Epoch 20/24\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.3036 - accuracy: 0.9187 - val_loss: 0.6531 - val_accuracy: 0.7212\n", + "Epoch 21/24\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.3466 - accuracy: 0.9033 - val_loss: 0.3687 - val_accuracy: 0.8958\n", + "Epoch 22/24\n", + "512/512 [==============================] - 79s 154ms/step - loss: 0.2888 - accuracy: 0.9238 - val_loss: 0.2060 - val_accuracy: 0.9503\n", + "Epoch 23/24\n", + "512/512 [==============================] - 80s 157ms/step - loss: 0.1832 - accuracy: 0.9583 - val_loss: 0.1931 - val_accuracy: 0.9471\n", + "Epoch 24/24\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1262 - accuracy: 0.9729 - val_loss: 0.1719 - val_accuracy: 0.9567\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-024-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1719\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.19614851474761963 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.17186278104782104\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.84 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 25/30\n", + "512/512 [==============================] - 84s 157ms/step - loss: 0.2452 - accuracy: 0.9280 - val_loss: 0.2506 - val_accuracy: 0.9391\n", + "Epoch 26/30\n", + "512/512 [==============================] - 79s 154ms/step - loss: 0.3161 - accuracy: 0.9199 - val_loss: 0.2607 - val_accuracy: 0.9311\n", + "Epoch 27/30\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2898 - accuracy: 0.9287 - val_loss: 0.3127 - val_accuracy: 0.9183\n", + "Epoch 28/30\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2450 - accuracy: 0.9453 - val_loss: 0.2576 - val_accuracy: 0.9375\n", + "Epoch 29/30\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1752 - accuracy: 0.9629 - val_loss: 0.2625 - val_accuracy: 0.9359\n", + "Epoch 30/30\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1121 - accuracy: 0.9785 - val_loss: 0.3048 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-025-0.9391.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2506\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m560.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m490.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 31/36\n", + "512/512 [==============================] - 88s 163ms/step - loss: 0.2783 - accuracy: 0.9263 - val_loss: 0.2717 - val_accuracy: 0.9087\n", + "Epoch 32/36\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2766 - accuracy: 0.9309 - val_loss: 0.2198 - val_accuracy: 0.9455\n", + "Epoch 33/36\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.3029 - accuracy: 0.9172 - val_loss: 0.3857 - val_accuracy: 0.9375\n", + "Epoch 34/36\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2681 - accuracy: 0.9299 - val_loss: 0.2557 - val_accuracy: 0.9279\n", + "Epoch 35/36\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2308 - accuracy: 0.9453 - val_loss: 0.2211 - val_accuracy: 0.9487\n", + "Epoch 36/36\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.1555 - accuracy: 0.9663 - val_loss: 0.2236 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-035-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2211\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m581.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 37/42\n", + "512/512 [==============================] - 86s 162ms/step - loss: 0.2614 - accuracy: 0.9258 - val_loss: 0.2696 - val_accuracy: 0.9455\n", + "Epoch 38/42\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.3130 - accuracy: 0.9199 - val_loss: 0.2501 - val_accuracy: 0.9439\n", + "Epoch 39/42\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2948 - accuracy: 0.9226 - val_loss: 0.4022 - val_accuracy: 0.9215\n", + "Epoch 40/42\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2720 - accuracy: 0.9275 - val_loss: 0.2985 - val_accuracy: 0.9038\n", + "Epoch 41/42\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2256 - accuracy: 0.9458 - val_loss: 0.2789 - val_accuracy: 0.9279\n", + "Epoch 42/42\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1319 - accuracy: 0.9712 - val_loss: 0.3209 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9455.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2696\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m565.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m493.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.27 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 43/48\n", + "512/512 [==============================] - 87s 163ms/step - loss: 0.2547 - accuracy: 0.9297 - val_loss: 0.2331 - val_accuracy: 0.9455\n", + "Epoch 44/48\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2930 - accuracy: 0.9185 - val_loss: 0.2536 - val_accuracy: 0.9407\n", + "Epoch 45/48\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2826 - accuracy: 0.9258 - val_loss: 0.2272 - val_accuracy: 0.9471\n", + "Epoch 46/48\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2522 - accuracy: 0.9319 - val_loss: 0.1899 - val_accuracy: 0.9439\n", + "Epoch 47/48\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1652 - accuracy: 0.9658 - val_loss: 0.1954 - val_accuracy: 0.9407\n", + "Epoch 48/48\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1035 - accuracy: 0.9795 - val_loss: 0.1904 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-045-0.9471.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2272\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m571.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 49/54\n", + "512/512 [==============================] - 87s 164ms/step - loss: 0.2935 - accuracy: 0.9138 - val_loss: 0.2586 - val_accuracy: 0.9295\n", + "Epoch 50/54\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.3354 - accuracy: 0.9172 - val_loss: 0.4145 - val_accuracy: 0.9231\n", + "Epoch 51/54\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2955 - accuracy: 0.9294 - val_loss: 0.4671 - val_accuracy: 0.8606\n", + "Epoch 52/54\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2894 - accuracy: 0.9314 - val_loss: 0.2932 - val_accuracy: 0.9535\n", + "Epoch 53/54\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1909 - accuracy: 0.9570 - val_loss: 0.2285 - val_accuracy: 0.9471\n", + "Epoch 54/54\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1304 - accuracy: 0.9736 - val_loss: 0.2813 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-052-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2932\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.11 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 55/60\n", + "512/512 [==============================] - 87s 163ms/step - loss: 0.3008 - accuracy: 0.9204 - val_loss: 0.3639 - val_accuracy: 0.9343\n", + "Epoch 56/60\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2970 - accuracy: 0.9231 - val_loss: 0.3717 - val_accuracy: 0.9087\n", + "Epoch 57/60\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2971 - accuracy: 0.9253 - val_loss: 0.2916 - val_accuracy: 0.9343\n", + "Epoch 58/60\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2197 - accuracy: 0.9434 - val_loss: 0.2453 - val_accuracy: 0.9311\n", + "Epoch 59/60\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1865 - accuracy: 0.9531 - val_loss: 0.2109 - val_accuracy: 0.9375\n", + "Epoch 60/60\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1095 - accuracy: 0.9778 - val_loss: 0.2099 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-060-0.9455.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2099\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 61/66\n", + "512/512 [==============================] - 88s 164ms/step - loss: 0.2355 - accuracy: 0.9336 - val_loss: 0.1893 - val_accuracy: 0.9471\n", + "Epoch 62/66\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2743 - accuracy: 0.9297 - val_loss: 0.2586 - val_accuracy: 0.9311\n", + "Epoch 63/66\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2302 - accuracy: 0.9446 - val_loss: 0.2575 - val_accuracy: 0.9375\n", + "Epoch 64/66\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1933 - accuracy: 0.9558 - val_loss: 0.3245 - val_accuracy: 0.9327\n", + "Epoch 65/66\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1570 - accuracy: 0.9714 - val_loss: 0.2169 - val_accuracy: 0.9423\n", + "Epoch 66/66\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1070 - accuracy: 0.9817 - val_loss: 0.2537 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-061-0.9471.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1893\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.43 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m497.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 67/72\n", + "512/512 [==============================] - 86s 161ms/step - loss: 0.2957 - accuracy: 0.9087 - val_loss: 0.2250 - val_accuracy: 0.9439\n", + "Epoch 68/72\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.3246 - accuracy: 0.9163 - val_loss: 0.3025 - val_accuracy: 0.9343\n", + "Epoch 69/72\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2869 - accuracy: 0.9268 - val_loss: 0.3096 - val_accuracy: 0.9151\n", + "Epoch 70/72\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2740 - accuracy: 0.9292 - val_loss: 0.2916 - val_accuracy: 0.9375\n", + "Epoch 71/72\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.2295 - accuracy: 0.9434 - val_loss: 0.2162 - val_accuracy: 0.9407\n", + "Epoch 72/72\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.1465 - accuracy: 0.9673 - val_loss: 0.2612 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-067-0.9439.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2250\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m502.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m73.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 73/78\n", + "512/512 [==============================] - 86s 161ms/step - loss: 0.2419 - accuracy: 0.9302 - val_loss: 0.3760 - val_accuracy: 0.9359\n", + "Epoch 74/78\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2922 - accuracy: 0.9209 - val_loss: 0.2300 - val_accuracy: 0.9455\n", + "Epoch 75/78\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2253 - accuracy: 0.9451 - val_loss: 0.2119 - val_accuracy: 0.9471\n", + "Epoch 76/78\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2234 - accuracy: 0.9397 - val_loss: 0.3418 - val_accuracy: 0.9407\n", + "Epoch 77/78\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1615 - accuracy: 0.9668 - val_loss: 0.2172 - val_accuracy: 0.9519\n", + "Epoch 78/78\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.1225 - accuracy: 0.9756 - val_loss: 0.1930 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-077-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2172\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m582.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m496.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 79/84\n", + "512/512 [==============================] - 86s 162ms/step - loss: 0.2487 - accuracy: 0.9307 - val_loss: 0.1886 - val_accuracy: 0.9519\n", + "Epoch 80/84\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2756 - accuracy: 0.9297 - val_loss: 0.3146 - val_accuracy: 0.8702\n", + "Epoch 81/84\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2448 - accuracy: 0.9370 - val_loss: 0.3448 - val_accuracy: 0.9359\n", + "Epoch 82/84\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2395 - accuracy: 0.9399 - val_loss: 0.3315 - val_accuracy: 0.9263\n", + "Epoch 83/84\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.1599 - accuracy: 0.9663 - val_loss: 0.3752 - val_accuracy: 0.9215\n", + "Epoch 84/84\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1241 - accuracy: 0.9734 - val_loss: 0.3453 - val_accuracy: 0.9295\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-079-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1886\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m491.68 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 85/90\n", + "512/512 [==============================] - 86s 161ms/step - loss: 0.2453 - accuracy: 0.9277 - val_loss: 0.2153 - val_accuracy: 0.9455\n", + "Epoch 86/90\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.3330 - accuracy: 0.9121 - val_loss: 0.2543 - val_accuracy: 0.9391\n", + "Epoch 87/90\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2985 - accuracy: 0.9258 - val_loss: 0.2262 - val_accuracy: 0.9455\n", + "Epoch 88/90\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2552 - accuracy: 0.9348 - val_loss: 0.2511 - val_accuracy: 0.9487\n", + "Epoch 89/90\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2027 - accuracy: 0.9556 - val_loss: 0.2189 - val_accuracy: 0.9439\n", + "Epoch 90/90\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1466 - accuracy: 0.9678 - val_loss: 0.2318 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-090-0.9503.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2318\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 91/96\n", + "512/512 [==============================] - 86s 162ms/step - loss: 0.2495 - accuracy: 0.9312 - val_loss: 0.2295 - val_accuracy: 0.9535\n", + "Epoch 92/96\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2776 - accuracy: 0.9265 - val_loss: 0.3416 - val_accuracy: 0.8734\n", + "Epoch 93/96\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2656 - accuracy: 0.9316 - val_loss: 0.2431 - val_accuracy: 0.9343\n", + "Epoch 94/96\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.2177 - accuracy: 0.9531 - val_loss: 0.3189 - val_accuracy: 0.9167\n", + "Epoch 95/96\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1752 - accuracy: 0.9580 - val_loss: 0.2598 - val_accuracy: 0.9327\n", + "Epoch 96/96\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.1333 - accuracy: 0.9697 - val_loss: 0.2331 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-091-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2295\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.25 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m494.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m81.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 97/102\n", + "512/512 [==============================] - 86s 162ms/step - loss: 0.2493 - accuracy: 0.9272 - val_loss: 0.2420 - val_accuracy: 0.9535\n", + "Epoch 98/102\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2654 - accuracy: 0.9355 - val_loss: 0.2742 - val_accuracy: 0.8830\n", + "Epoch 99/102\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2616 - accuracy: 0.9397 - val_loss: 0.2934 - val_accuracy: 0.9375\n", + "Epoch 100/102\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2273 - accuracy: 0.9475 - val_loss: 0.2164 - val_accuracy: 0.9359\n", + "Epoch 101/102\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1576 - accuracy: 0.9673 - val_loss: 0.1799 - val_accuracy: 0.9471\n", + "Epoch 102/102\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.1021 - accuracy: 0.9819 - val_loss: 0.1920 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-097-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2420\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m577.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m492.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 103/108\n", + "512/512 [==============================] - 86s 162ms/step - loss: 0.2579 - accuracy: 0.9246 - val_loss: 0.3319 - val_accuracy: 0.9247\n", + "Epoch 104/108\n", + "512/512 [==============================] - 81s 158ms/step - loss: 0.2793 - accuracy: 0.9260 - val_loss: 0.3001 - val_accuracy: 0.9151\n", + "Epoch 105/108\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2745 - accuracy: 0.9338 - val_loss: 0.3302 - val_accuracy: 0.9391\n", + "Epoch 106/108\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.2249 - accuracy: 0.9443 - val_loss: 0.3120 - val_accuracy: 0.9215\n", + "Epoch 107/108\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1681 - accuracy: 0.9644 - val_loss: 0.4372 - val_accuracy: 0.9311\n", + "Epoch 108/108\n", + "512/512 [==============================] - 81s 159ms/step - loss: 0.1235 - accuracy: 0.9746 - val_loss: 0.3658 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-105-0.9391.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3302\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m585.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m495.17 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m90.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 109/114\n", + "512/512 [==============================] - 87s 163ms/step - loss: 0.2705 - accuracy: 0.9263 - val_loss: 0.2374 - val_accuracy: 0.9359\n", + "Epoch 110/114\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2861 - accuracy: 0.9246 - val_loss: 0.2402 - val_accuracy: 0.9455\n", + "Epoch 111/114\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2683 - accuracy: 0.9365 - val_loss: 0.2723 - val_accuracy: 0.9359\n", + "Epoch 112/114\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2379 - accuracy: 0.9441 - val_loss: 0.1936 - val_accuracy: 0.9471\n", + "Epoch 113/114\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1877 - accuracy: 0.9580 - val_loss: 0.2324 - val_accuracy: 0.9439\n", + "Epoch 114/114\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.1311 - accuracy: 0.9736 - val_loss: 0.2345 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-112-0.9471.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1936\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.86 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m514.07 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 115/120\n", + "512/512 [==============================] - 91s 170ms/step - loss: 0.2582 - accuracy: 0.9299 - val_loss: 0.3217 - val_accuracy: 0.9311\n", + "Epoch 116/120\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2866 - accuracy: 0.9224 - val_loss: 0.2336 - val_accuracy: 0.9391\n", + "Epoch 117/120\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2524 - accuracy: 0.9424 - val_loss: 0.2395 - val_accuracy: 0.9423\n", + "Epoch 118/120\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2286 - accuracy: 0.9482 - val_loss: 0.2789 - val_accuracy: 0.9551\n", + "Epoch 119/120\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1637 - accuracy: 0.9634 - val_loss: 0.2793 - val_accuracy: 0.9567\n", + "Epoch 120/120\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1067 - accuracy: 0.9800 - val_loss: 0.2853 - val_accuracy: 0.9551\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-119-0.9567.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2793\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9567307829856873. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.17186278104782104. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m618.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m521.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 121/126\n", + "512/512 [==============================] - 91s 170ms/step - loss: 0.2549 - accuracy: 0.9287 - val_loss: 0.2443 - val_accuracy: 0.9519\n", + "Epoch 122/126\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2818 - accuracy: 0.9292 - val_loss: 0.2470 - val_accuracy: 0.9519\n", + "Epoch 123/126\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2809 - accuracy: 0.9326 - val_loss: 0.2941 - val_accuracy: 0.9439\n", + "Epoch 124/126\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2328 - accuracy: 0.9436 - val_loss: 0.1614 - val_accuracy: 0.9631\n", + "Epoch 125/126\n", + "512/512 [==============================] - 85s 165ms/step - loss: 0.1889 - accuracy: 0.9556 - val_loss: 0.2098 - val_accuracy: 0.9375\n", + "Epoch 126/126\n", + "512/512 [==============================] - 85s 165ms/step - loss: 0.1269 - accuracy: 0.9751 - val_loss: 0.1830 - val_accuracy: 0.9583\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-124-0.9631.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9631\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1614\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model accuracy from \u001b[0m\u001b[0;32m0.9567307829856873 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.9631410241127014\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.17186278104782104 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1614265888929367\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m622.04 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m516.75 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 127/132\n", + "512/512 [==============================] - 92s 171ms/step - loss: 0.2494 - accuracy: 0.9287 - val_loss: 0.3167 - val_accuracy: 0.9535\n", + "Epoch 128/132\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2697 - accuracy: 0.9290 - val_loss: 0.2598 - val_accuracy: 0.9407\n", + "Epoch 129/132\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2950 - accuracy: 0.9292 - val_loss: 0.2314 - val_accuracy: 0.9519\n", + "Epoch 130/132\n", + "512/512 [==============================] - 86s 167ms/step - loss: 0.2274 - accuracy: 0.9497 - val_loss: 0.1929 - val_accuracy: 0.9503\n", + "Epoch 131/132\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.1758 - accuracy: 0.9583 - val_loss: 0.1932 - val_accuracy: 0.9471\n", + "Epoch 132/132\n", + "512/512 [==============================] - 85s 165ms/step - loss: 0.1173 - accuracy: 0.9780 - val_loss: 0.2255 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-127-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3167\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m621.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m518.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 133/138\n", + "512/512 [==============================] - 92s 171ms/step - loss: 0.2436 - accuracy: 0.9304 - val_loss: 0.2788 - val_accuracy: 0.9503\n", + "Epoch 134/138\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2451 - accuracy: 0.9390 - val_loss: 0.2871 - val_accuracy: 0.9487\n", + "Epoch 135/138\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2731 - accuracy: 0.9348 - val_loss: 0.2828 - val_accuracy: 0.9279\n", + "Epoch 136/138\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2215 - accuracy: 0.9497 - val_loss: 0.2114 - val_accuracy: 0.9487\n", + "Epoch 137/138\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.1595 - accuracy: 0.9678 - val_loss: 0.2340 - val_accuracy: 0.9535\n", + "Epoch 138/138\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.1248 - accuracy: 0.9727 - val_loss: 0.2349 - val_accuracy: 0.9535\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-137-0.9535.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2340\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m623.28 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m520.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.61 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 139/144\n", + "512/512 [==============================] - 92s 172ms/step - loss: 0.2535 - accuracy: 0.9307 - val_loss: 0.2297 - val_accuracy: 0.9295\n", + "Epoch 140/144\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2881 - accuracy: 0.9258 - val_loss: 0.2479 - val_accuracy: 0.9487\n", + "Epoch 141/144\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2874 - accuracy: 0.9268 - val_loss: 0.3033 - val_accuracy: 0.9343\n", + "Epoch 142/144\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2872 - accuracy: 0.9246 - val_loss: 0.2649 - val_accuracy: 0.9439\n", + "Epoch 143/144\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2038 - accuracy: 0.9507 - val_loss: 0.2492 - val_accuracy: 0.9199\n", + "Epoch 144/144\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1476 - accuracy: 0.9683 - val_loss: 0.2257 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-140-0.9487.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2479\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m628.67 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m522.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.05 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Learning the patterns]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.011\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 145/150\n", + "512/512 [==============================] - 87s 164ms/step - loss: 0.2732 - accuracy: 0.9285 - val_loss: 0.2106 - val_accuracy: 0.9551\n", + "Epoch 146/150\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2830 - accuracy: 0.9233 - val_loss: 0.2477 - val_accuracy: 0.9583\n", + "Epoch 147/150\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2609 - accuracy: 0.9414 - val_loss: 0.2034 - val_accuracy: 0.9535\n", + "Epoch 148/150\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2055 - accuracy: 0.9546 - val_loss: 0.5101 - val_accuracy: 0.8173\n", + "Epoch 149/150\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1713 - accuracy: 0.9634 - val_loss: 0.2369 - val_accuracy: 0.9423\n", + "Epoch 150/150\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1163 - accuracy: 0.9753 - val_loss: 0.2704 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-146-0.9583.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9583\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2477\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m610.83 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m499.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01094\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 151/156\n", + "512/512 [==============================] - 88s 164ms/step - loss: 0.3050 - accuracy: 0.9189 - val_loss: 0.2298 - val_accuracy: 0.9263\n", + "Epoch 152/156\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2837 - accuracy: 0.9243 - val_loss: 0.1924 - val_accuracy: 0.9471\n", + "Epoch 153/156\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2482 - accuracy: 0.9419 - val_loss: 0.2488 - val_accuracy: 0.9487\n", + "Epoch 154/156\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2033 - accuracy: 0.9556 - val_loss: 0.2723 - val_accuracy: 0.9519\n", + "Epoch 155/156\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1600 - accuracy: 0.9634 - val_loss: 0.2178 - val_accuracy: 0.9487\n", + "Epoch 156/156\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1309 - accuracy: 0.9705 - val_loss: 0.2520 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0;33mLoading the best weights...\u001b[0m\n", + "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-154-0.9519.h5...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2723\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m502.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01088\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 157/162\n", + "512/512 [==============================] - 87s 164ms/step - loss: 0.2530 - accuracy: 0.9314 - val_loss: 0.4695 - val_accuracy: 0.9327\n", + "Epoch 158/162\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2881 - accuracy: 0.9321 - val_loss: 0.3741 - val_accuracy: 0.9359\n", + "Epoch 159/162\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2420 - accuracy: 0.9399 - val_loss: 0.3475 - val_accuracy: 0.9375\n", + "Epoch 160/162\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1993 - accuracy: 0.9568 - val_loss: 0.1921 - val_accuracy: 0.9583\n", + "Epoch 161/162\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1561 - accuracy: 0.9644 - val_loss: 0.2095 - val_accuracy: 0.9471\n", + "Epoch 162/162\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1284 - accuracy: 0.9695 - val_loss: 0.2050 - val_accuracy: 0.9519\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9583}, \u001b[0m\u001b[0;33mloss{0.1921}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2051\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m601.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.55 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01082\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 163/168\n", + "512/512 [==============================] - 87s 164ms/step - loss: 0.2361 - accuracy: 0.9348 - val_loss: 0.2340 - val_accuracy: 0.9439\n", + "Epoch 164/168\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2722 - accuracy: 0.9319 - val_loss: 0.2842 - val_accuracy: 0.9503\n", + "Epoch 165/168\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2600 - accuracy: 0.9399 - val_loss: 0.2952 - val_accuracy: 0.9295\n", + "Epoch 166/168\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.2447 - accuracy: 0.9438 - val_loss: 0.2630 - val_accuracy: 0.9263\n", + "Epoch 167/168\n", + "512/512 [==============================] - 82s 159ms/step - loss: 0.1589 - accuracy: 0.9673 - val_loss: 0.2470 - val_accuracy: 0.9423\n", + "Epoch 168/168\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1163 - accuracy: 0.9783 - val_loss: 0.2212 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2212}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2212\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m600.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m498.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01076\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 169/174\n", + "512/512 [==============================] - 88s 164ms/step - loss: 0.2650 - accuracy: 0.9272 - val_loss: 0.2829 - val_accuracy: 0.9263\n", + "Epoch 170/174\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2693 - accuracy: 0.9373 - val_loss: 0.3897 - val_accuracy: 0.9391\n", + "Epoch 171/174\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2435 - accuracy: 0.9502 - val_loss: 0.3412 - val_accuracy: 0.9231\n", + "Epoch 172/174\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1985 - accuracy: 0.9565 - val_loss: 0.2695 - val_accuracy: 0.9311\n", + "Epoch 173/174\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1515 - accuracy: 0.9680 - val_loss: 0.2574 - val_accuracy: 0.9375\n", + "Epoch 174/174\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1211 - accuracy: 0.9756 - val_loss: 0.2405 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2405\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m603.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.79 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0107\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 175/180\n", + "512/512 [==============================] - 88s 164ms/step - loss: 0.2395 - accuracy: 0.9290 - val_loss: 0.2182 - val_accuracy: 0.9327\n", + "Epoch 176/180\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2648 - accuracy: 0.9360 - val_loss: 0.3920 - val_accuracy: 0.9151\n", + "Epoch 177/180\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.2603 - accuracy: 0.9399 - val_loss: 0.2183 - val_accuracy: 0.9391\n", + "Epoch 178/180\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2106 - accuracy: 0.9551 - val_loss: 0.2085 - val_accuracy: 0.9455\n", + "Epoch 179/180\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1751 - accuracy: 0.9626 - val_loss: 0.2304 - val_accuracy: 0.9455\n", + "Epoch 180/180\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.1163 - accuracy: 0.9780 - val_loss: 0.2240 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.2085}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2240\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m604.51 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.48 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 181/186\n", + "512/512 [==============================] - 92s 171ms/step - loss: 0.2400 - accuracy: 0.9346 - val_loss: 0.2400 - val_accuracy: 0.9343\n", + "Epoch 182/186\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2529 - accuracy: 0.9395 - val_loss: 0.2746 - val_accuracy: 0.9295\n", + "Epoch 183/186\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2448 - accuracy: 0.9421 - val_loss: 0.2956 - val_accuracy: 0.9503\n", + "Epoch 184/186\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2068 - accuracy: 0.9539 - val_loss: 0.2435 - val_accuracy: 0.9439\n", + "Epoch 185/186\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.1489 - accuracy: 0.9680 - val_loss: 0.2623 - val_accuracy: 0.9455\n", + "Epoch 186/186\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1060 - accuracy: 0.9778 - val_loss: 0.2387 - val_accuracy: 0.9455\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2387}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2387\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m626.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m521.82 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.15 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33m└───Shuffling data...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 187/192\n", + "512/512 [==============================] - 92s 172ms/step - loss: 0.2255 - accuracy: 0.9358 - val_loss: 0.2049 - val_accuracy: 0.9359\n", + "Epoch 188/192\n", + "512/512 [==============================] - 87s 171ms/step - loss: 0.2452 - accuracy: 0.9355 - val_loss: 0.2099 - val_accuracy: 0.9471\n", + "Epoch 189/192\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2326 - accuracy: 0.9512 - val_loss: 0.2640 - val_accuracy: 0.9455\n", + "Epoch 190/192\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.2011 - accuracy: 0.9595 - val_loss: 0.2538 - val_accuracy: 0.9471\n", + "Epoch 191/192\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.2197 - accuracy: 0.9609 - val_loss: 0.2370 - val_accuracy: 0.9519\n", + "Epoch 192/192\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.1511 - accuracy: 0.9775 - val_loss: 0.2349 - val_accuracy: 0.9551\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.2049}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2349\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m650.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m526.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.59 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 193/198\n", + "173/512 [=========>....................] - ETA: 49s - loss: 0.2603 - accuracy: 0.9364\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 153s 290ms/step - loss: 0.2699 - accuracy: 0.9292 - val_loss: 0.2112 - val_accuracy: 0.9295\n", + "Epoch 194/198\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.2731 - accuracy: 0.9248 - val_loss: 0.3021 - val_accuracy: 0.9455\n", + "Epoch 195/198\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2551 - accuracy: 0.9392 - val_loss: 0.3150 - val_accuracy: 0.9247\n", + "Epoch 196/198\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2326 - accuracy: 0.9399 - val_loss: 0.3005 - val_accuracy: 0.9343\n", + "Epoch 197/198\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1818 - accuracy: 0.9585 - val_loss: 0.3222 - val_accuracy: 0.9407\n", + "Epoch 198/198\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.1226 - accuracy: 0.9771 - val_loss: 0.3274 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2112}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3274\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m696.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m584.00 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01046\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 199/204\n", + "512/512 [==============================] - 93s 173ms/step - loss: 0.2488 - accuracy: 0.9299 - val_loss: 0.2098 - val_accuracy: 0.9487\n", + "Epoch 200/204\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2554 - accuracy: 0.9363 - val_loss: 0.2605 - val_accuracy: 0.9503\n", + "Epoch 201/204\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.2308 - accuracy: 0.9448 - val_loss: 0.3346 - val_accuracy: 0.9263\n", + "Epoch 202/204\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2659 - accuracy: 0.9324 - val_loss: 0.4138 - val_accuracy: 0.8926\n", + "Epoch 203/204\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1899 - accuracy: 0.9612 - val_loss: 0.2993 - val_accuracy: 0.9439\n", + "Epoch 204/204\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1459 - accuracy: 0.9666 - val_loss: 0.3281 - val_accuracy: 0.9375\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2098}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3281\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m639.02 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.69 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.33 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0104\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 205/210\n", + "512/512 [==============================] - 92s 172ms/step - loss: 0.2516 - accuracy: 0.9299 - val_loss: 0.2961 - val_accuracy: 0.9391\n", + "Epoch 206/210\n", + "512/512 [==============================] - 87s 171ms/step - loss: 0.2614 - accuracy: 0.9370 - val_loss: 0.2434 - val_accuracy: 0.9487\n", + "Epoch 207/210\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2402 - accuracy: 0.9453 - val_loss: 0.4394 - val_accuracy: 0.8894\n", + "Epoch 208/210\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.1976 - accuracy: 0.9561 - val_loss: 0.2486 - val_accuracy: 0.9439\n", + "Epoch 209/210\n", + "512/512 [==============================] - 85s 167ms/step - loss: 0.1348 - accuracy: 0.9688 - val_loss: 0.3123 - val_accuracy: 0.9455\n", + "Epoch 210/210\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1049 - accuracy: 0.9753 - val_loss: 0.2694 - val_accuracy: 0.9487\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2434}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2694\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m638.65 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m523.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.30 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01034\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 211/216\n", + "512/512 [==============================] - 92s 171ms/step - loss: 0.2418 - accuracy: 0.9307 - val_loss: 0.2496 - val_accuracy: 0.9455\n", + "Epoch 212/216\n", + "512/512 [==============================] - 86s 167ms/step - loss: 0.2612 - accuracy: 0.9307 - val_loss: 0.3104 - val_accuracy: 0.9071\n", + "Epoch 213/216\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.2478 - accuracy: 0.9431 - val_loss: 0.3774 - val_accuracy: 0.9359\n", + "Epoch 214/216\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.2076 - accuracy: 0.9546 - val_loss: 0.3438 - val_accuracy: 0.9391\n", + "Epoch 215/216\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1539 - accuracy: 0.9675 - val_loss: 0.4097 - val_accuracy: 0.9359\n", + "Epoch 216/216\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.1075 - accuracy: 0.9802 - val_loss: 0.4911 - val_accuracy: 0.9247\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2496}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4912\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m624.52 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m512.78 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m111.74 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01028\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 217/222\n", + "512/512 [==============================] - 89s 166ms/step - loss: 0.2197 - accuracy: 0.9365 - val_loss: 0.2334 - val_accuracy: 0.9471\n", + "Epoch 218/222\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2295 - accuracy: 0.9402 - val_loss: 0.3119 - val_accuracy: 0.8958\n", + "Epoch 219/222\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2134 - accuracy: 0.9561 - val_loss: 0.2296 - val_accuracy: 0.9311\n", + "Epoch 220/222\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1859 - accuracy: 0.9602 - val_loss: 0.2600 - val_accuracy: 0.9327\n", + "Epoch 221/222\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1649 - accuracy: 0.9680 - val_loss: 0.2953 - val_accuracy: 0.9375\n", + "Epoch 222/222\n", + "512/512 [==============================] - 82s 160ms/step - loss: 0.1140 - accuracy: 0.9792 - val_loss: 0.2859 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.2296}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2859\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m612.92 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m501.94 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01022\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 223/228\n", + "512/512 [==============================] - 89s 167ms/step - loss: 0.2312 - accuracy: 0.9331 - val_loss: 0.2373 - val_accuracy: 0.9407\n", + "Epoch 224/228\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2420 - accuracy: 0.9348 - val_loss: 0.2742 - val_accuracy: 0.9407\n", + "Epoch 225/228\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.2070 - accuracy: 0.9480 - val_loss: 0.2802 - val_accuracy: 0.9375\n", + "Epoch 226/228\n", + "512/512 [==============================] - 82s 161ms/step - loss: 0.1753 - accuracy: 0.9653 - val_loss: 0.3583 - val_accuracy: 0.9343\n", + "Epoch 227/228\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.1362 - accuracy: 0.9697 - val_loss: 0.3364 - val_accuracy: 0.9407\n", + "Epoch 228/228\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.0999 - accuracy: 0.9800 - val_loss: 0.2650 - val_accuracy: 0.9423\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2373}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2650\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m614.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.70 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.29 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01016\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 229/234\n", + "512/512 [==============================] - 89s 166ms/step - loss: 0.2398 - accuracy: 0.9309 - val_loss: 0.2423 - val_accuracy: 0.9439\n", + "Epoch 230/234\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.2820 - accuracy: 0.9268 - val_loss: 0.3899 - val_accuracy: 0.9327\n", + "Epoch 231/234\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2472 - accuracy: 0.9421 - val_loss: 0.2796 - val_accuracy: 0.9327\n", + "Epoch 232/234\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.2008 - accuracy: 0.9536 - val_loss: 0.2522 - val_accuracy: 0.9455\n", + "Epoch 233/234\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1747 - accuracy: 0.9631 - val_loss: 0.2504 - val_accuracy: 0.9407\n", + "Epoch 234/234\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1318 - accuracy: 0.9717 - val_loss: 0.3184 - val_accuracy: 0.9343\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2423}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3184\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m614.31 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m504.21 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.10 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0101\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 235/240\n", + "512/512 [==============================] - 89s 166ms/step - loss: 0.2393 - accuracy: 0.9336 - val_loss: 0.5106 - val_accuracy: 0.8878\n", + "Epoch 236/240\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2378 - accuracy: 0.9358 - val_loss: 0.2732 - val_accuracy: 0.9423\n", + "Epoch 237/240\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2494 - accuracy: 0.9429 - val_loss: 0.2851 - val_accuracy: 0.9407\n", + "Epoch 238/240\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1862 - accuracy: 0.9624 - val_loss: 0.3868 - val_accuracy: 0.9167\n", + "Epoch 239/240\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1377 - accuracy: 0.9707 - val_loss: 0.1950 - val_accuracy: 0.9375\n", + "Epoch 240/240\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.0853 - accuracy: 0.9844 - val_loss: 0.2666 - val_accuracy: 0.9359\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1950}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2666\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m620.49 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m506.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.85 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01004\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 241/246\n", + "512/512 [==============================] - 90s 167ms/step - loss: 0.1995 - accuracy: 0.9424 - val_loss: 0.2085 - val_accuracy: 0.9359\n", + "Epoch 242/246\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2204 - accuracy: 0.9478 - val_loss: 0.2644 - val_accuracy: 0.9487\n", + "Epoch 243/246\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.1884 - accuracy: 0.9580 - val_loss: 0.2390 - val_accuracy: 0.9391\n", + "Epoch 244/246\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2060 - accuracy: 0.9634 - val_loss: 0.2529 - val_accuracy: 0.9471\n", + "Epoch 245/246\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1437 - accuracy: 0.9773 - val_loss: 0.3027 - val_accuracy: 0.9487\n", + "Epoch 246/246\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1003 - accuracy: 0.9834 - val_loss: 0.2626 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2085}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2625\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m621.35 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m507.71 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m01_d02-h05_m28_s20\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00998\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 247/252\n", + "512/512 [==============================] - 89s 166ms/step - loss: 0.2291 - accuracy: 0.9373 - val_loss: 0.2281 - val_accuracy: 0.9503\n", + "Epoch 248/252\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2351 - accuracy: 0.9370 - val_loss: 0.2358 - val_accuracy: 0.9359\n", + "Epoch 249/252\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.2046 - accuracy: 0.9553 - val_loss: 0.3557 - val_accuracy: 0.9439\n", + "Epoch 250/252\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.2004 - accuracy: 0.9583 - val_loss: 0.3836 - val_accuracy: 0.9247\n", + "Epoch 251/252\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1540 - accuracy: 0.9683 - val_loss: 0.3067 - val_accuracy: 0.9407\n", + "Epoch 252/252\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.1198 - accuracy: 0.9780 - val_loss: 0.2948 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2281}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2948\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m642.18 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m516.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.96 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00992\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 253/258\n", + "512/512 [==============================] - 92s 172ms/step - loss: 0.2299 - accuracy: 0.9380 - val_loss: 0.4194 - val_accuracy: 0.9359\n", + "Epoch 254/258\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2248 - accuracy: 0.9448 - val_loss: 0.2327 - val_accuracy: 0.9423\n", + "Epoch 255/258\n", + "512/512 [==============================] - 87s 171ms/step - loss: 0.2172 - accuracy: 0.9526 - val_loss: 0.2595 - val_accuracy: 0.9439\n", + "Epoch 256/258\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2070 - accuracy: 0.9568 - val_loss: 0.3004 - val_accuracy: 0.9471\n", + "Epoch 257/258\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.1542 - accuracy: 0.9663 - val_loss: 0.2271 - val_accuracy: 0.9487\n", + "Epoch 258/258\n", + "512/512 [==============================] - 88s 172ms/step - loss: 0.1053 - accuracy: 0.9805 - val_loss: 0.2649 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.2271}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2649\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m654.38 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m530.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00986\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 259/264\n", + "119/512 [=====>........................] - ETA: 56s - loss: 0.2614 - accuracy: 0.9296\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 152s 289ms/step - loss: 0.2454 - accuracy: 0.9285 - val_loss: 0.2276 - val_accuracy: 0.9375\n", + "Epoch 260/264\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2876 - accuracy: 0.9326 - val_loss: 0.3372 - val_accuracy: 0.9455\n", + "Epoch 261/264\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2292 - accuracy: 0.9490 - val_loss: 0.3965 - val_accuracy: 0.9327\n", + "Epoch 262/264\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1709 - accuracy: 0.9634 - val_loss: 0.2338 - val_accuracy: 0.9519\n", + "Epoch 263/264\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1755 - accuracy: 0.9705 - val_loss: 0.2991 - val_accuracy: 0.9487\n", + "Epoch 264/264\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1390 - accuracy: 0.9792 - val_loss: 0.3091 - val_accuracy: 0.9439\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2276}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3090\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m713.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m586.60 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m126.64 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0098\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 265/270\n", + "512/512 [==============================] - 93s 174ms/step - loss: 0.2435 - accuracy: 0.9421 - val_loss: 0.2473 - val_accuracy: 0.9423\n", + "Epoch 266/270\n", + "512/512 [==============================] - 87s 171ms/step - loss: 0.2490 - accuracy: 0.9397 - val_loss: 0.2585 - val_accuracy: 0.9455\n", + "Epoch 267/270\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.2312 - accuracy: 0.9441 - val_loss: 0.3069 - val_accuracy: 0.9311\n", + "Epoch 268/270\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1901 - accuracy: 0.9558 - val_loss: 0.4075 - val_accuracy: 0.8830\n", + "Epoch 269/270\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1472 - accuracy: 0.9688 - val_loss: 0.3201 - val_accuracy: 0.9343\n", + "Epoch 270/270\n", + "512/512 [==============================] - 86s 168ms/step - loss: 0.1057 - accuracy: 0.9775 - val_loss: 0.3155 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.2473}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3155\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m656.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m528.77 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.26 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00974\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 271/276\n", + "149/512 [=======>......................] - ETA: 53s - loss: 0.1901 - accuracy: 0.9455\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 154s 292ms/step - loss: 0.2038 - accuracy: 0.9377 - val_loss: 0.5999 - val_accuracy: 0.8958\n", + "Epoch 272/276\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.2129 - accuracy: 0.9421 - val_loss: 0.3347 - val_accuracy: 0.9183\n", + "Epoch 273/276\n", + "512/512 [==============================] - 88s 172ms/step - loss: 0.2193 - accuracy: 0.9543 - val_loss: 0.2374 - val_accuracy: 0.9535\n", + "Epoch 274/276\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1566 - accuracy: 0.9619 - val_loss: 0.1854 - val_accuracy: 0.9503\n", + "Epoch 275/276\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1191 - accuracy: 0.9719 - val_loss: 0.2512 - val_accuracy: 0.9503\n", + "Epoch 276/276\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.0937 - accuracy: 0.9807 - val_loss: 0.3150 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1854}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3150\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m719.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m589.87 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.37 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00968\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 277/282\n", + "512/512 [==============================] - 93s 174ms/step - loss: 0.2085 - accuracy: 0.9377 - val_loss: 0.2248 - val_accuracy: 0.9423\n", + "Epoch 278/282\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2104 - accuracy: 0.9468 - val_loss: 0.3161 - val_accuracy: 0.9535\n", + "Epoch 279/282\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1777 - accuracy: 0.9631 - val_loss: 0.3394 - val_accuracy: 0.9359\n", + "Epoch 280/282\n", + "512/512 [==============================] - 88s 172ms/step - loss: 0.1579 - accuracy: 0.9670 - val_loss: 0.3100 - val_accuracy: 0.9567\n", + "Epoch 281/282\n", + "512/512 [==============================] - 85s 165ms/step - loss: 0.1234 - accuracy: 0.9778 - val_loss: 0.4045 - val_accuracy: 0.9247\n", + "Epoch 282/282\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.0810 - accuracy: 0.9878 - val_loss: 0.3237 - val_accuracy: 0.9391\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9567}, \u001b[0m\u001b[0;33mloss{0.2248}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3237\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m649.32 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.24 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.09 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00962\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 283/288\n", + "512/512 [==============================] - 90s 167ms/step - loss: 0.2240 - accuracy: 0.9316 - val_loss: 0.2660 - val_accuracy: 0.9503\n", + "Epoch 284/288\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1951 - accuracy: 0.9448 - val_loss: 0.2729 - val_accuracy: 0.9343\n", + "Epoch 285/288\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2376 - accuracy: 0.9524 - val_loss: 0.3847 - val_accuracy: 0.9423\n", + "Epoch 286/288\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1763 - accuracy: 0.9658 - val_loss: 0.3685 - val_accuracy: 0.9423\n", + "Epoch 287/288\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.1385 - accuracy: 0.9685 - val_loss: 0.2170 - val_accuracy: 0.9519\n", + "Epoch 288/288\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.1054 - accuracy: 0.9773 - val_loss: 0.2134 - val_accuracy: 0.9503\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.2134}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2134\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.99 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m508.63 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00956\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 289/294\n", + "512/512 [==============================] - 89s 167ms/step - loss: 0.2156 - accuracy: 0.9392 - val_loss: 0.3535 - val_accuracy: 0.9455\n", + "Epoch 290/294\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2335 - accuracy: 0.9390 - val_loss: 0.2257 - val_accuracy: 0.9487\n", + "Epoch 291/294\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.1945 - accuracy: 0.9519 - val_loss: 0.2495 - val_accuracy: 0.9503\n", + "Epoch 292/294\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1647 - accuracy: 0.9592 - val_loss: 0.1974 - val_accuracy: 0.9487\n", + "Epoch 293/294\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.1159 - accuracy: 0.9719 - val_loss: 0.1649 - val_accuracy: 0.9535\n", + "Epoch 294/294\n", + "512/512 [==============================] - 85s 166ms/step - loss: 0.0944 - accuracy: 0.9792 - val_loss: 0.1747 - val_accuracy: 0.9551\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1649}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9551\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1747\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m511.14 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.76 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0095\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 295/300\n", + "512/512 [==============================] - 89s 167ms/step - loss: 0.2244 - accuracy: 0.9324 - val_loss: 0.2936 - val_accuracy: 0.9439\n", + "Epoch 296/300\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2622 - accuracy: 0.9272 - val_loss: 0.2860 - val_accuracy: 0.9407\n", + "Epoch 297/300\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2746 - accuracy: 0.9451 - val_loss: 0.4849 - val_accuracy: 0.9071\n", + "Epoch 298/300\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.2036 - accuracy: 0.9556 - val_loss: 0.2450 - val_accuracy: 0.9375\n", + "Epoch 299/300\n", + "512/512 [==============================] - 83s 161ms/step - loss: 0.1328 - accuracy: 0.9712 - val_loss: 0.2686 - val_accuracy: 0.9327\n", + "Epoch 300/300\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.0898 - accuracy: 0.9807 - val_loss: 0.3176 - val_accuracy: 0.9279\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.2450}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3176\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m625.66 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m505.22 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.44 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00944\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 301/306\n", + "512/512 [==============================] - 90s 168ms/step - loss: 0.2080 - accuracy: 0.9434 - val_loss: 0.2541 - val_accuracy: 0.9439\n", + "Epoch 302/306\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.2532 - accuracy: 0.9343 - val_loss: 0.2347 - val_accuracy: 0.9375\n", + "Epoch 303/306\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.2141 - accuracy: 0.9495 - val_loss: 0.2215 - val_accuracy: 0.9519\n", + "Epoch 304/306\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.1817 - accuracy: 0.9597 - val_loss: 0.2861 - val_accuracy: 0.9407\n", + "Epoch 305/306\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.1299 - accuracy: 0.9766 - val_loss: 0.1812 - val_accuracy: 0.9455\n", + "Epoch 306/306\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.0898 - accuracy: 0.9844 - val_loss: 0.2148 - val_accuracy: 0.9407\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1812}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2148\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m627.62 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m509.40 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.23 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00938\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 307/312\n", + "512/512 [==============================] - 89s 167ms/step - loss: 0.1939 - accuracy: 0.9478 - val_loss: 0.2203 - val_accuracy: 0.9439\n", + "Epoch 308/312\n", + "512/512 [==============================] - 84s 164ms/step - loss: 0.2045 - accuracy: 0.9504 - val_loss: 0.2867 - val_accuracy: 0.9487\n", + "Epoch 309/312\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1870 - accuracy: 0.9624 - val_loss: 0.2737 - val_accuracy: 0.9487\n", + "Epoch 310/312\n", + "512/512 [==============================] - 83s 162ms/step - loss: 0.1676 - accuracy: 0.9653 - val_loss: 0.2592 - val_accuracy: 0.9423\n", + "Epoch 311/312\n", + "512/512 [==============================] - 83s 163ms/step - loss: 0.1200 - accuracy: 0.9749 - val_loss: 0.2440 - val_accuracy: 0.9487\n", + "Epoch 312/312\n", + "512/512 [==============================] - 84s 163ms/step - loss: 0.0816 - accuracy: 0.9839 - val_loss: 0.2292 - val_accuracy: 0.9471\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.2203}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2292\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m629.90 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m506.93 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.97 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00932\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 313/318\n", + "512/512 [==============================] - 90s 167ms/step - loss: 0.1826 - accuracy: 0.9436 - val_loss: 0.2867 - val_accuracy: 0.9279\n", + "Epoch 314/318\n", + "512/512 [==============================] - 84s 165ms/step - loss: 0.2192 - accuracy: 0.9497 - val_loss: 0.3617 - val_accuracy: 0.9311\n", + "Epoch 315/318\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1704 - accuracy: 0.9583 - val_loss: 0.3324 - val_accuracy: 0.9423\n", + "Epoch 316/318\n", + "512/512 [==============================] - 88s 172ms/step - loss: 0.1448 - accuracy: 0.9727 - val_loss: 0.4824 - val_accuracy: 0.9022\n", + "Epoch 317/318\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1045 - accuracy: 0.9792 - val_loss: 0.3307 - val_accuracy: 0.9359\n", + "Epoch 318/318\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.0721 - accuracy: 0.9851 - val_loss: 0.3812 - val_accuracy: 0.9311\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2867}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3813\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m646.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m524.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.12 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00926\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 319/324\n", + "167/512 [========>.....................] - ETA: 50s - loss: 0.2437 - accuracy: 0.9341\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\u001b[0;33mResuming training...\u001b[0m\n", + "512/512 [==============================] - 153s 291ms/step - loss: 0.2265 - accuracy: 0.9348 - val_loss: 0.4225 - val_accuracy: 0.9135\n", + "Epoch 320/324\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.2323 - accuracy: 0.9395 - val_loss: 0.3071 - val_accuracy: 0.9359\n", + "Epoch 321/324\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.2141 - accuracy: 0.9475 - val_loss: 0.4155 - val_accuracy: 0.9391\n", + "Epoch 322/324\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1850 - accuracy: 0.9578 - val_loss: 0.6766 - val_accuracy: 0.9199\n", + "Epoch 323/324\n", + "512/512 [==============================] - 86s 169ms/step - loss: 0.1652 - accuracy: 0.9595 - val_loss: 0.3307 - val_accuracy: 0.9247\n", + "Epoch 324/324\n", + "512/512 [==============================] - 88s 171ms/step - loss: 0.1119 - accuracy: 0.9768 - val_loss: 0.4258 - val_accuracy: 0.9215\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.3071}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4258\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m721.36 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m590.16 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.19 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.0092\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 325/330\n", + "512/512 [==============================] - 94s 175ms/step - loss: 0.2087 - accuracy: 0.9353 - val_loss: 0.3861 - val_accuracy: 0.9279\n", + "Epoch 326/330\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2161 - accuracy: 0.9392 - val_loss: 0.2966 - val_accuracy: 0.9199\n", + "Epoch 327/330\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.2025 - accuracy: 0.9521 - val_loss: 0.5782 - val_accuracy: 0.8029\n", + "Epoch 328/330\n", + "512/512 [==============================] - 87s 170ms/step - loss: 0.1962 - accuracy: 0.9507 - val_loss: 0.4708 - val_accuracy: 0.9263\n", + "Epoch 329/330\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.1362 - accuracy: 0.9670 - val_loss: 0.2955 - val_accuracy: 0.9279\n", + "Epoch 330/330\n", + "512/512 [==============================] - 87s 169ms/step - loss: 0.0947 - accuracy: 0.9785 - val_loss: 0.4636 - val_accuracy: 0.9215\n", + "\u001b[0;32mSubset training done.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9279}, \u001b[0m\u001b[0;33mloss{0.2955}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9631}, loss{0.9631}]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4636\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9631410241127014. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1614265888929367. Not saving model.\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m665.01 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m528.98 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m136.03 \u001b[0m\u001b[0;36msec\u001b[0m\n", + "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n", + "\u001b[0m\n", + "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m486 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;33mPreparing train data...\u001b[0m\n", + "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n", + "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.00914\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n", + "\u001b[0;32mTraining on subset...\u001b[0m\n", + "Epoch 331/336\n", + "119/512 [=====>........................] - ETA: 57s - loss: 0.2186 - accuracy: 0.9401\u001b[0;31m\n", + "Pausing training due to high GPU temperature! (for [60]sec)\u001b[0m\n", + "\n", + "KeyboardInterrupt.\n", + "Training done.\n", + "\n" + ] + } + ], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "# CONF <-------------------------------------------------------------------------->\n", + "# Hyperparameters for training the model:\n", + "max_epoch = 486 # max_epoch: Maximum number of epochs to train for. Use >=256 for full fine-tuning of large models.\n", + "subset_epoch = 6 # subset_epoch: Number of epochs to train each subset.\n", + "subset_epoch_FT = 6 # subset_epoch_FT: subset_epoch after pre-training epochs.\n", + "PL_epoch = 26 # PL_epoch: Number of pre-training epochs. Use >=24 for large models or 0/1 for fine-tuning only.\n", + "subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 8192.\n", + "Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n", + "RES_Train = False # RES_Train: Resume training if True.\n", + "MAX_LR = 0.011 # MAX_LR: Maximum learning rate.\n", + "DEC_LR = 0.00006 # DEC_LR: Learning rate decay.\n", + "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n", + "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n", + "OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n", + "Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n", + "Debug_OUTPUT_DPS_freq = 42 # Debug_OUTPUT_DPS_freq: Debug image output frequency(epoch).\n", + "TerminateOnHighTemp_M = True # TerminateOnHighTemp_M: Terminate training on high GPU temp to prevent damage.\n", + "SAVE_FULLM = True # SAVE_FULLM: Save full model if True.\n", + "USE_REV2_DP = False # USE_REV2_DP: Use Rev2 data preprocessing if True.\n", + "AdvSubsetC = True # AdvSubsetC: Use advanced subset sampling to prevent overfitting if True.\n", + "AdvSubsetC_SHR = 32 # AdvSubsetC_SHR: Parameter for advanced subset sampling (shuffling data after n epochs).\n", + "load_SUB_BRW = True # load_SUB_BRW: Load previous subset weights to speed up training if True. May reduce max accuracy.\n", + "load_SUB_BRW_MODE = 'val_accuracy' # load_SUB_BRW_MODE: Previous subset weights loading mode - 'val_accuracy' or 'val_loss'.\n", + "load_SUB_BRW_LMODE = 0 # load_SUB_BRW_LMODE: Previous subset weights loading mode parameter (1 for only on imp and !1 for normal mode (for subset_epoch > 6 normal mode is better)).\n", + "load_SUB_BRW_LMODE_FN = True # load_SUB_BRW_LMODE_FN: Set load_SUB_BRW_LMODE=1 during fine-tuning if True.\n", + "ModelCheckpoint_mode = 'auto' # ModelCheckpoint_mode: 'auto', 'min', or 'max' - how to monitor ModelCheckpoint.\n", + "ModelCheckpoint_Reset_TO = 0.6251 # ModelCheckpoint_Reset_TO: Reset ModelCheckpoint monitor to this value, e.g. 0 or float('inf').\n", + "Auto_clear_cache = True # Auto_clear_cache: Clear cache during training if True to reduce memory usage.\n", + "Use_ES_ONSUBT = False # Use_ES_ONSUBT: Early stopping per subset (⚠️deprecated⚠️).\n", + "EarlyStopping_P = 5 # EarlyStopping_P: Early stopping patience (⚠️deprecated⚠️).\n", + "Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n", + "Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n", + "BEST_RSN = 'PAI_model_T' # Best model save name prefix.\n", + "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n", + "IMAGE_GEN_PATH = 'Data\\\\image_SUB_generator.pkl'\n", + "# CONF END <---------------------------------------------------------------------->\n", + "#Prep\n", + "if RES_Train:\n", + " MAX_LR = RES_LR\n", + " PL_epoch = 1\n", + "#VAR\n", + "Total_SUB_epoch_C = 0 # TO FIX TensorBoard\n", + "CU_LR = MAX_LR\n", + "all_histories = []\n", + "chosen_indices = []\n", + "subset_sizes = []\n", + "best_acc = 0\n", + "best_loss = float('inf')\n", + "#Funcs\n", + "def normalize_TO_RANGE(arr, min_val, max_val):\n", + " arr = arr.astype('float32')\n", + " arr = (arr - arr.min()) / (arr.max() - arr.min())\n", + " arr = arr * (max_val - min_val) + min_val\n", + " return arr\n", + "\n", + "def Z_SCORE_normalize(arr):\n", + " arr = arr.astype('float32')\n", + " mean = np.mean(arr)\n", + " std_dev = np.std(arr)\n", + " arr = (arr - mean) / std_dev\n", + " return arr\n", + "\n", + "def add_image_grain_TRLRev2(image, intensity = 0.01):\n", + " # Generate random noise array\n", + " noise = (np.random.randint(-255, 255, size=image.shape, dtype=np.int16) \\\n", + " + np.random.randint(-255, 255, size=image.shape, dtype=np.int16)) / 2\n", + "\n", + " # Scale the noise array\n", + " scaled_noise = (noise * intensity).astype(np.float32)\n", + " # Add the noise to the image\n", + " noisy_image = cv2.add(image, scaled_noise)\n", + "\n", + " return noisy_image\n", + "# noise_func_TRLRev2 ([REV1 OLD])\n", + "if not USE_REV2_DP:\n", + " def noise_func_TRLRev2(image): \n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.08, 0.08)\n", + " intensityL1 = random.uniform(-0.05, 0.05)\n", + " else:\n", + " intensityL2 = random.uniform(-0.09, 0.09)\n", + " intensityL1 = random.uniform(-0.06, 0.06)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 112)\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image.shape[0], block_size_L2):\n", + " for j in range(0, image.shape[1], block_size_L2):\n", + " block = image[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image = new_image \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image.shape[0], block_size_L1):\n", + " for j in range(0, image.shape[1], block_size_L1):\n", + " block = image[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.07) # Random intensity \n", + " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", + " return new_image\n", + "# noise_func_TRLRev2 ([REV2 NEW])\n", + "else:\n", + " def noise_func_TRLRev2(image):\n", + " noise_type = np.random.choice(['L1', 'L2', 'L3', 'none'])\n", + " new_image = np.copy(image)\n", + " \n", + " if noise_type == 'L3':\n", + " intensityL2 = random.uniform(-0.07, 0.07)\n", + " intensityL1 = random.uniform(-0.06, 0.06)\n", + " else:\n", + " intensityL2 = random.uniform(-0.09, 0.09)\n", + " intensityL1 = random.uniform(-0.07, 0.07)\n", + " \n", + " block_size_L1 = random.randint(16, 32)\n", + " block_size_L2 = random.randint(32, 112)\n", + " \n", + " for channel in range(3): # Iterate over each RGB channel\n", + " image_channel = image[:, :, channel]\n", + " new_image_channel = new_image[:, :, channel]\n", + " \n", + " if noise_type == 'L2' or noise_type == 'L3':\n", + " for i in range(0, image_channel.shape[0], block_size_L2):\n", + " for j in range(0, image_channel.shape[1], block_size_L2):\n", + " block = image_channel[i:i+block_size_L2, j:j+block_size_L2]\n", + " block = (np.random.rand() * intensityL2 + 1) * block\n", + " new_image_channel[i:i+block_size_L2, j:j+block_size_L2] = block\n", + " image_channel = new_image_channel \n", + " \n", + " if noise_type == 'L1' or noise_type == 'L3': \n", + " for i in range(0, image_channel.shape[0], block_size_L1):\n", + " for j in range(0, image_channel.shape[1], block_size_L1):\n", + " block = image_channel[i:i+block_size_L1, j:j+block_size_L1]\n", + " block = (np.random.rand() * intensityL1 + 1) * block\n", + " new_image_channel[i:i+block_size_L1, j:j+block_size_L1] = block\n", + " \n", + " new_image[:, :, channel] = new_image_channel\n", + " \n", + " if add_img_grain:\n", + " intensity = random.uniform(0, 0.05) # Random intensity \n", + " new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n", + " return new_image\n", + "#CONST\n", + "train_SUB_datagen = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " rotation_range=179,\n", + " zoom_range=0.18, \n", + " shear_range=0.18,\n", + " width_shift_range=0.18,\n", + " brightness_range=(0.82, 1.18),\n", + " height_shift_range=0.18,\n", + " channel_shift_range=100,\n", + " featurewise_center=True,\n", + " featurewise_std_normalization=True,\n", + " zca_whitening=False,\n", + " interpolation_order=2,\n", + " fill_mode='nearest',\n", + " preprocessing_function=noise_func_TRLRev2\n", + " )\n", + "class TerminateOnHighTemp(tf.keras.callbacks.Callback):\n", + " def __init__(self, active=True, check_every_n_batches=2, high_temp=75, low_temp=60, pause_time=60):\n", + " super().__init__()\n", + " self.active = active\n", + " self.check_every_n_batches = check_every_n_batches\n", + " self.high_temp = high_temp\n", + " self.low_temp = low_temp\n", + " self.pause_time = pause_time\n", + " self.batch_counter = 0\n", + "\n", + " def on_batch_end(self, batch, logs=None):\n", + " if not self.active:\n", + " return\n", + " self.batch_counter += 1\n", + " if self.batch_counter % self.check_every_n_batches == 0:\n", + " temperature = gpu_control.get_temperature()\n", + " if temperature > self.high_temp:\n", + " print_Color(f'\\nPausing training due to high GPU temperature! (for [{self.pause_time}]sec)', ['red'], advanced_mode=False)\n", + " time.sleep(self.pause_time) \n", + " while gpu_control.get_temperature() > self.low_temp:\n", + " time.sleep(4)\n", + " print_Color('Resuming training...', ['yellow'])\n", + "class ExtendedTensorBoard(TensorBoard):\n", + " def on_epoch_end(self, epoch, logs=None):\n", + " logs = logs or {}\n", + " logs['lr'] = tf.keras.backend.get_value(self.model.optimizer.lr)\n", + " logs['momentum'] = self.model.optimizer.momentum \n", + " super().on_epoch_end(epoch, logs)\n", + "class DummyCallback(Callback):\n", + " pass\n", + "steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2\n", + "#callbacks>>>\n", + "# EarlyStopping\n", + "early_stopping = EarlyStopping(monitor='val_accuracy',\n", + " patience=EarlyStopping_P,\n", + " verbose=1, restore_best_weights=True,\n", + " mode='max'\n", + " ) if Use_ES_ONSUBT else DummyCallback()\n", + "# ModelCheckpoint \n", + "checkpoint_SUB = ModelCheckpoint(f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', # f'cache\\\\model_SUB_checkpoint-{{epoch:03d}}-{{{load_SUB_BRW_MODE}:.4f}}.h5', \n", + " monitor=load_SUB_BRW_MODE,\n", + " save_best_only=True, mode=ModelCheckpoint_mode,\n", + " save_weights_only = True\n", + " ) if load_SUB_BRW else DummyCallback()\n", + "checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", + "# TerminateOnHighTemp\n", + "TerminateOnHighTemp_CB = TerminateOnHighTemp(active=TerminateOnHighTemp_M,\n", + " check_every_n_batches=6,\n", + " high_temp=72,\n", + " low_temp=58,\n", + " pause_time=60)\n", + "# TensorBoard\n", + "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + "if Use_extended_tensorboard:\n", + " tensorboard_callback = ExtendedTensorBoard(\n", + " log_dir=log_dir,\n", + " write_images=False, # Uses a lot of memory\n", + " histogram_freq=1,\n", + " update_freq='epoch',\n", + " write_grads=True,\n", + " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", + " )\n", + "else:\n", + " tensorboard_callback = TensorBoard(\n", + " log_dir=log_dir,\n", + " write_images=False, # Uses a lot of memory\n", + " histogram_freq=1,\n", + " update_freq='epoch',\n", + " write_grads=True,\n", + " profile_batch='256,512' if Use_tensorboard_profiler else 0\n", + " )\n", + "# OneCycleLr\n", + "if OneCycleLr_UFTS: \n", + " learning_rate_schedule_SUB = OneCycleLr(max_lr=MAX_LR,\n", + " steps_per_epoch=steps_per_epoch_train_SUB,\n", + " epochs=(PL_epoch * subset_epoch) + ((max_epoch - PL_epoch) * subset_epoch_FT)) \n", + "#PRES\n", + "# ...\n", + "#MAIN\n", + "print('Training the model...')\n", + "# INFOp\n", + "print_Color('\\nSetup Verbose:', ['yellow'])\n", + "print_Color(f'~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*Use_extended_tensorboard ~*[{Use_extended_tensorboard}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*Debug_OUTPUT_DPS ~*[{Debug_OUTPUT_DPS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "print_Color(f'~*OneCycleLr_UFTS ~*[{OneCycleLr_UFTS}]~*.', ['cyan', 'green', 'cyan'], advanced_mode=True)\n", + "#warnings\n", + "P_warning('RES_Train is True.') if RES_Train else None\n", + "print_Color('Setup Verbose END.', ['yellow'])\n", + "# MAIN LOOP\n", + "try:\n", + " for epoch in range(1, max_epoch):\n", + " # Start Epoch\n", + " STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning'\n", + " C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT\n", + " if epoch > PL_epoch and load_SUB_BRW_LMODE_FN: load_SUB_BRW_LMODE = 1\n", + " start_FULL_time = time.time()\n", + " if Auto_clear_cache:\n", + " subprocess.run([\"Cache_clear.cmd\"], shell=True)\n", + " # TSEC: Total-Subset-Epoch-Count\n", + " print_Color(f'\\n~*Epoch: ~*{epoch}~*/~*{max_epoch} (TSEC: {Total_SUB_epoch_C})~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'blue', 'green'], advanced_mode=True)\n", + " # DP\n", + " if not AdvSubsetC:\n", + " print_Color('Shuffling data...', ['yellow'])\n", + " x_train, y_train = shuffle_data(x_train, y_train)\n", + " print_Color(f'~*Taking a subset of ~*[|{subset_size}|AdvSubset:{AdvSubsetC}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", + " if AdvSubsetC:\n", + " if AdvSubsetC_SHR > 0 and epoch % AdvSubsetC_SHR == 0:\n", + " print_Color('└───Shuffling data...', ['yellow'])\n", + " x_train, y_train = shuffle_data(x_train, y_train)\n", + " chosen_indices = [] # Reset chosen_indices\n", + "\n", + " available_indices = list(set(range(x_train.shape[0])) - set(chosen_indices))\n", + " \n", + " if len(available_indices) < subset_size:\n", + " #DEBUG\n", + " # print('[DEBUG]-[AdvSubset]: Not enough available indices using the indices that were chosen the longest time ago.')\n", + " # If there are not enough available indices, choose from the indices that were chosen the longest time ago\n", + " old_indices = chosen_indices[:subset_size - len(available_indices)]\n", + " subset_indices = old_indices + list(np.random.choice(available_indices, len(available_indices), replace=False))\n", + " \n", + " # Update the list of chosen indices and their sizes\n", + " chosen_indices = chosen_indices[len(old_indices):] + subset_indices\n", + " subset_sizes = subset_sizes[len(old_indices):] + [subset_size] * len(subset_indices)\n", + " else:\n", + " subset_indices = list(np.random.choice(available_indices, subset_size, replace=False))\n", + " \n", + " # Add the chosen indices to the list of already chosen indices\n", + " chosen_indices += subset_indices\n", + " subset_sizes += [subset_size] * len(subset_indices)\n", + " else:\n", + " subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False)\n", + " # Taking the subset\n", + " x_SUB_train = x_train[subset_indices]\n", + " y_SUB_train = y_train[subset_indices]\n", + " x_SUB_train, y_SUB_train = shuffle_data(x_SUB_train, y_SUB_train)\n", + " assert len(x_SUB_train) == subset_size, f'Expected subset size of {subset_size}, but got {len(x_SUB_train)}'\n", + " print_Color('Preparing train data...', ['yellow']) \n", + " # if epoch == 1: # OLD\n", + " # print_Color('- ImageDataGenerator fit...', ['yellow']) \n", + " # train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", + " # print_Color('- ImageDataGenerator fit done.', ['yellow'])\n", + " if epoch == 1 or ALWAYS_REFIT_IDG == 2:\n", + " if os.path.exists(IMAGE_GEN_PATH) and not ALWAYS_REFIT_IDG:\n", + " print_Color('- Loading fitted ImageDataGenerator...', ['yellow'])\n", + " train_SUB_datagen = pickle.load(open(IMAGE_GEN_PATH, 'rb')) \n", + " else:\n", + " print_Color('- Fitting ImageDataGenerator...', ['yellow'])\n", + " IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n", + " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n", + " pickle.dump(train_SUB_datagen, open(IMAGE_GEN_PATH, 'wb'))\n", + " print_Color('- ImageDataGenerator fit done.', ['yellow']) \n", + "\n", + " print_Color('- Augmenting Image Data...', ['yellow']) \n", + " train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255,\n", + " y_SUB_train,\n", + " shuffle=False,\n", + " batch_size=len(x_SUB_train)\n", + " ).next()\n", + " print_Color('- Normalizing Image Data...', ['yellow'])\n", + " x_SUB_train = normalize_TO_RANGE(train_SUB_augmented_images[0], 0, 255)\n", + " x_SUB_train = apply_clahe_rgb_array(x_SUB_train, 0.5) / 255\n", + " # x_SUB_train = x_SUB_train / 255\n", + " x_SUB_train = normalize_TO_RANGE(Z_SCORE_normalize(x_SUB_train), 0, 1)\n", + " y_SUB_train = train_SUB_augmented_images[1]\n", + " # DEBUG\n", + " if Debug_OUTPUT_DPS and (epoch % Debug_OUTPUT_DPS_freq == 0 or epoch == 1):\n", + " SITD = np.random.choice(subset_size, size=400, replace=False)\n", + " S_dir = 'Samples/TSR_SUB_400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + " print_Color(f'~*- Debug DP Sample dir: ~*{S_dir}', ['red', 'green'], advanced_mode=True)\n", + " save_images_to_dir(np.clip(x_SUB_train[SITD], 0, 1), y_SUB_train[SITD], S_dir)\n", + " # learning_rate_schedule_SUB\n", + " if PL_epoch == 0:\n", + " CU_LR = MIN_LR\n", + " elif epoch >= PL_epoch and CU_LR > MIN_LR:\n", + " if (CU_LR - DEC_LR) < MIN_LR:\n", + " CU_LR = MIN_LR\n", + " else:\n", + " CU_LR -= DEC_LR\n", + " if not OneCycleLr_UFTS: \n", + " learning_rate_schedule_SUB = OneCycleLr(max_lr=CU_LR,\n", + " steps_per_epoch=steps_per_epoch_train_SUB,\n", + " epochs=C_subset_epoch)\n", + " #FV\n", + " print_Color(f'~*Setting training OneCycleLr::maxlr to ~*[{(str(round(CU_LR, 8)) + \"~*~*\") if not OneCycleLr_UFTS else \"~*OneCycleLr_UFTS Is ON~*\"}]~*...',\n", + " ['yellow', 'green', 'red', 'green', 'yellow'], advanced_mode=True)\n", + " print_Color(f'~*Setting training subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True)\n", + " # Train\n", + " print_Color('Training on subset...', ['green'])\n", + " start_SUBO_time = time.time()\n", + " SUB_history = model.fit(x_SUB_train,\n", + " y_SUB_train,\n", + " epochs=C_subset_epoch + Total_SUB_epoch_C, # TO FIX TensorBoard (Total_SUB_epoch_C)\n", + " batch_size=Conf_batch_size_REV2,\n", + " validation_data=(x_test, y_test),\n", + " verbose='auto',\n", + " initial_epoch=Total_SUB_epoch_C, # TO FIX TensorBoard\n", + " callbacks=[\n", + " learning_rate_schedule_SUB,\n", + " TerminateOnHighTemp_CB,\n", + " checkpoint_SUB,\n", + " early_stopping,\n", + " tensorboard_callback\n", + " ]\n", + " )\n", + " end_SUBO_time = time.time()\n", + " print_Color('Subset training done.', ['green'])\n", + " if load_SUB_BRW_LMODE == 1:\n", + " if max(SUB_history.history['val_accuracy']) > best_acc: \n", + " load_weights = True \n", + " elif min(SUB_history.history['val_loss']) < best_loss:\n", + " load_weights = True \n", + " else:\n", + " load_weights = False \n", + " else: \n", + " load_weights = True \n", + " \n", + " if load_SUB_BRW and load_weights:\n", + " print_Color('Loading the best weights...', ['yellow'])\n", + " # Get the filename of the best weights file\n", + " list_of_files = glob.glob('cache\\\\*.h5') \n", + " try:\n", + " best_weights_filename = max(list_of_files, key=os.path.getctime)\n", + " print_Color(f'Loading weights from file {best_weights_filename}...', ['yellow'])\n", + " model.load_weights(best_weights_filename)\n", + " except Exception as Err:\n", + " print_Color(f'ERROR: Failed to load weights. Error: {Err}', ['red'])\n", + " elif load_SUB_BRW and (not load_weights):\n", + " # print_Color(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_acc:.4f}}}]',\n", + " # ['yellow']) # OLD\n", + " print_Color_V2(f'Not loading weights[BSR:acc{{{max(SUB_history.history[\"val_accuracy\"]):.4f}}}, loss{{{min(SUB_history.history[\"val_loss\"]):.4f}}}|BTR:acc{{{best_acc:.4f}}}, loss{{{best_acc:.4f}}}]')\n", + " all_histories.append(SUB_history.history)\n", + " checkpoint_SUB.best = ModelCheckpoint_Reset_TO\n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " tf.keras.backend.clear_session() \n", + " # Evaluate the model on the test data\n", + " evaluation = model.evaluate(x_test, y_test, verbose=0)\n", + " \n", + " # Extract the loss and accuracy from the evaluation results\n", + " loss = evaluation[0]\n", + " acc = evaluation[1]\n", + " print_Color(f'~*Model Test acc: ~*{acc:.4f}', ['yellow', 'green'], advanced_mode=True)\n", + " print_Color(f'~*Model Test loss: ~*{loss:.4f}', ['yellow', 'green'], advanced_mode=True)\n", + " # If the accuracy is higher than the best_acc\n", + " if acc > best_acc:\n", + " print_Color_V2(f'Improved model accuracy from {best_acc} to {acc}. Saving model.')\n", + " # Update the best_acc\n", + " best_acc = acc\n", + " if SAVE_FULLM:\n", + " # Save the model\n", + " if SAVE_TYPE == 'TF':\n", + " print_Color_V2(f'Saving full model tf format...')\n", + " model.save(BEST_RSN, save_format='tf')\n", + " else:\n", + " print_Color_V2(f'Saving full model H5 format...')\n", + " model.save(f'{BEST_RSN}.h5')\n", + " model.save_weights('PAI_model_weights.h5')\n", + " else:\n", + " print_Color_V2(f'Model accuracy did not improve from {best_acc}. Not saving model.')\n", + " \n", + " # If the loss is higher than the best_loss\n", + " if loss < best_loss:\n", + " print_Color_V2(f'Improved model loss from {best_loss} to {loss}. Saving model.')\n", + " \n", + " # Update the best_acc\n", + " best_loss = loss\n", + " \n", + " if SAVE_FULLM:\n", + " # Save the model\n", + " if SAVE_TYPE == 'TF':\n", + " print_Color_V2(f'Saving full model tf format...')\n", + " model.save(BEST_RSN + '_BL', save_format='tf')\n", + " else:\n", + " print_Color_V2(f'Saving full model H5 format...')\n", + " model.save(f'{BEST_RSN}_BL.h5')\n", + " model.save_weights('PAI_model_weights_BL.h5')\n", + " else:\n", + " print_Color_V2(f'Model loss did not improve from {best_loss}. Not saving model.') \n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " tf.keras.backend.clear_session() \n", + " # Epoch end\n", + " end_time = time.time()\n", + " epoch_time = end_time - start_FULL_time\n", + " print_Color_V2(f'Time taken for epoch(FULL): {epoch_time:.2f} sec')\n", + " epoch_SUB_time = end_SUBO_time - start_SUBO_time\n", + " print_Color_V2(f'Time taken for epoch(SUBo): {epoch_SUB_time:.2f} sec')\n", + " epoch_OTHERO_time = epoch_time - epoch_SUB_time\n", + " print_Color_V2(f'Time taken for epoch(OTHERo): {epoch_OTHERO_time:.2f} sec')\n", + " print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan'])\n", + " Total_SUB_epoch_C += C_subset_epoch # TO FIX TensorBoard\n", + "except KeyboardInterrupt:\n", + " print('\\nKeyboardInterrupt.')\n", + "# End\n", + "try:\n", + " history = {}\n", + " for key in all_histories[0].keys():\n", + " # For each metric, concatenate the values from all histories\n", + " history[key] = np.concatenate([h[key] for h in all_histories])\n", + "except Exception as Err:\n", + " print(f'Failed to make model `history` var.\\nERROR: {Err}')\n", + " \n", + "print('Training done.\\n')\n", + "# del vars\n", + "try:\n", + " del train_SUB_datagen\n", + " del train_SUB_augmented_images\n", + "except NameError:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Rev1 (⚠️deprecated⚠️)\n", + "```\n", + "Working: βœ…\n", + "Other:\n", + " + Tensorboard works.\n", + " - Can cause overfitting.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()\n", + "#CONF\n", + "Conf_batch_size = 8 \n", + "OneCycleLr_epoch = 20\n", + "Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training)\n", + "#TensorBoard conf\n", + "TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining)\n", + "# Learning rate configuration\n", + "Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr\n", + "MAX_LR = 0.0174\n", + "# First time\n", + "if Learning_rate_conf == 1:\n", + " learning_rate_start = 8e-04\n", + " learning_rate_max = 5e-03\n", + " learning_rate_min = 5e-05\n", + " learning_rate_rampup_epochs = 5\n", + " learning_rate_sustain_epochs = 1\n", + " learning_rate_exp_decay = .3\n", + " #TEMP\n", + " # learning_rate_start = 8e-04\n", + " # learning_rate_max = 1e-02\n", + " # learning_rate_min = 8e-04\n", + " # learning_rate_rampup_epochs = 5\n", + " # learning_rate_sustain_epochs = 3\n", + " # learning_rate_exp_decay = .45\n", + "# 2th time\n", + "if Learning_rate_conf == 2:\n", + " if Learning_rate_conf_SET2C == 1:\n", + " learning_rate_start = 4.10e-06\n", + " learning_rate_max = 4.10e-06\n", + " learning_rate_min = 4.10e-06\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + " \n", + " elif Learning_rate_conf_SET2C == 2:\n", + " learning_rate_start = 4e-07\n", + " learning_rate_max = 4e-07\n", + " learning_rate_min = 4e-07\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + " \n", + " elif Learning_rate_conf_SET2C == 3:\n", + " learning_rate_start = 5e-04\n", + " learning_rate_max = 5e-04\n", + " learning_rate_min = 5e-04\n", + " learning_rate_rampup_epochs = 0\n", + " learning_rate_sustain_epochs = 0\n", + " learning_rate_exp_decay = .1\n", + "# Function to build learning rate schedule\n", + "if Learning_rate_conf in [1,2]:\n", + " def build_learning_rate_fn(lr_start=learning_rate_start,\n", + " lr_max=learning_rate_max,\n", + " lr_min=learning_rate_min,\n", + " lr_rampup_epochs=learning_rate_rampup_epochs,\n", + " lr_sustain_epochs=learning_rate_sustain_epochs,\n", + " lr_exp_decay=learning_rate_exp_decay): \n", + " lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync\n", + " def learning_rate_fn(epoch):\n", + " if epoch < lr_rampup_epochs:\n", + " lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n", + " elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n", + " lr = lr_max\n", + " else:\n", + " lr = (lr_max - lr_min) *\\\n", + " lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min\n", + " return lr\n", + " return learning_rate_fn\n", + " \n", + "# Calculate steps per epoch\n", + "steps_per_epoch_train = len(x_train) // Conf_batch_size\n", + "\n", + "# Set up callbacks\n", + "class EpochEndMON(tf.keras.callbacks.Callback):\n", + " def on_epoch_end(self, epoch, logs=None):\n", + " optimizer = self.model.optimizer\n", + " if hasattr(optimizer, 'lr'):\n", + " lr = tf.keras.backend.get_value(optimizer.lr)\n", + " print(f'\\nLearning rate for epoch {epoch+1} is {lr}')\n", + " if hasattr(optimizer, 'momentum'):\n", + " momentum = tf.keras.backend.get_value(optimizer.momentum)\n", + " print(f'Momentum for epoch {epoch+1} is {momentum}')\n", + " if logs:\n", + " val_loss = logs.get('val_loss')\n", + " val_acc = logs.get('val_accuracy')\n", + " print(f'Validation loss for epoch {epoch+1} is {val_loss}')\n", + " print(f'Validation accuracy for epoch {epoch+1} is {val_acc}')\n", + "\n", + " print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`')\n", + "\n", + "# Instantiate the callback\n", + "EpochEndMON_callback = EpochEndMON()\n", + "if Learning_rate_conf in [1,2]:\n", + " learning_rate_fn = build_learning_rate_fn()\n", + " learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1)\n", + "else:\n", + " learning_rate_schedule = OneCycleLr(max_lr=MAX_LR, steps_per_epoch=steps_per_epoch_train, epochs=OneCycleLr_epoch)\n", + "if SAVE_TYPE == 'TF':\n", + " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", + " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", + "else:\n", + " checkpoint_BVAC = ModelCheckpoint('models\\\\Temp\\\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1)\n", + " checkpoint_BVL = ModelCheckpoint('models\\\\Temp\\\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1)\n", + "early_stopping = EarlyStopping(monitor='val_accuracy', patience=2, verbose=1, restore_best_weights=True)\n", + "log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S')\n", + "TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch'\n", + "tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq, write_grads=True)\n", + "\n", + "# Train the model\n", + "print('Log dir:', log_dir)\n", + "#MInfo\n", + "print('Input Shape:', model.input_shape)\n", + "print('Output Shape:', model.output_shape)\n", + "print('Loss Function:', model.loss)\n", + "print('Training the model...\\n')\n", + "history = model.fit(x_train,\n", + " y_train,\n", + " epochs=256,\n", + " batch_size=Conf_batch_size,\n", + " validation_data=(x_test, y_test),\n", + " verbose='auto',\n", + " callbacks=[early_stopping,\n", + " tensorboard_callback,\n", + " learning_rate_schedule,\n", + " checkpoint_BVAC,\n", + " checkpoint_BVL,\n", + " EpochEndMON_callback])\n", + "print('Training done.\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving model weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [], + "source": [ + "Extra_EXT = '_T'\n", + "# Save the weights\n", + "print('Saving weights...')\n", + "model.save_weights('PAI_model_weights.h5')\n", + "print('Saving full model...')\n", + "if SAVE_TYPE == 'TF':\n", + " print('Saving full model tf format...')\n", + " model.save(f'PAI_model{Extra_EXT}', save_format='tf')\n", + "else:\n", + " try:\n", + " model.save(f'PAI_model{Extra_EXT}.h5')\n", + " except ValueError:\n", + " print('failed to save in .h5 format!')\n", + " print('Saving full model in tf format...')\n", + " model.save(f'PAI_model{Extra_EXT}', save_format='tf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Garbage Collection (memory)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse model Training performance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Save history\n", + "save_list(history, 'history\\\\model_history.pkl.gz', compress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load history\n", + "history = load_list('history\\\\model_history.pkl.gz', compressed=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-28T07:04:52.565658900Z", + "start_time": "2023-12-28T07:04:51.032425100Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import seaborn as sns\n", + "\n", + "# Chunk size for 3D plot\n", + "chunk_size = 6 # Change this to your desired chunk size\n", + " \n", + "def convert_history(history):\n", + " if isinstance(history, tf.keras.callbacks.History):\n", + " return history.history\n", + " else:\n", + " return history\n", + " \n", + "def chunked_data(data, chunk_size):\n", + " return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n", + "\n", + "\n", + "try:\n", + " EPM = 'Epoch(Subset)' if not isinstance(history, tf.keras.callbacks.History) else 'Epoch' \n", + " history = convert_history(history)\n", + "\n", + " # Calculate deltas\n", + " delta_loss = np.diff(history['loss'])\n", + " delta_accuracy = np.diff(history['accuracy'])\n", + "\n", + " try:\n", + " delta_val_loss = np.diff(history['val_loss'])\n", + " delta_val_accuracy = np.diff(history['val_accuracy'])\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss or val_accuracy for delta calculation.')\n", + "\n", + " plt.figure(figsize=(16, 10))\n", + " # Loss\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history['loss'], label='loss')\n", + " try:\n", + " plt.plot(history['val_loss'], label='val_loss', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss.')\n", + " plt.title('Model Loss')\n", + " plt.ylabel('Loss')\n", + " plt.xlabel(EPM)\n", + " plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", + " plt.grid(True)\n", + " \n", + " # Density plot for loss\n", + " plt.subplot(2, 2, 2)\n", + " plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100)\n", + " try:\n", + " plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100)\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_loss (density plot).')\n", + " plt.title('Density Plot for Loss')\n", + " plt.xlabel('Loss')\n", + " plt.xlim(right=max(history['val_loss'][10:])) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2\n", + " plt.grid(True)\n", + " \n", + " \n", + " # Accuracy\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history['accuracy'], label='accuracy')\n", + " try:\n", + " plt.plot(history['val_accuracy'], label='val_accuracy', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_accuracy.')\n", + " plt.title('Model Accuracy')\n", + " plt.ylabel('Accuracy')\n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + " \n", + " # Density plot for accuracy\n", + " plt.subplot(2, 2, 4)\n", + " plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40)\n", + " try:\n", + " plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40)\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load val_accuracy (density plot).')\n", + " plt.title('Density Plot for Accuracy')\n", + " plt.xlabel('Accuracy')\n", + " plt.grid(True)\n", + "\n", + " # Delta Loss\n", + " plt.figure(figsize=(14, 8))\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(delta_loss, label='delta_loss')\n", + " try:\n", + " plt.plot(delta_val_loss, label='delta_val_loss', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load delta_val_loss.')\n", + " plt.title('Delta Model Loss')\n", + " plt.ylabel('Delta Loss')\n", + " plt.ylim(top=1.5, bottom=-1.5) \n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + " # Delta Accuracy\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(delta_accuracy, label='delta_accuracy')\n", + " try:\n", + " plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange')\n", + " except (ValueError, NameError):\n", + " print('\\033[91mfailed to load delta_val_accuracy.')\n", + " plt.title('Delta Model Accuracy')\n", + " plt.ylabel('Delta Accuracy')\n", + " plt.xlabel(EPM)\n", + " plt.grid(True)\n", + "\n", + " # Calculate chunked data\n", + " chunked_loss = chunked_data(history['val_loss'], chunk_size)\n", + " chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size)\n", + "\n", + " # Clip the loss values to a maximum of max(history['val_loss'][10:])\n", + " max_loss = max(history['val_loss'][10:])\n", + " chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss)\n", + "\n", + " # Create 3D surface plots for each chunk\n", + " fig = plt.figure(figsize=(14, 8))\n", + " ax = fig.add_subplot(121, projection='3d')\n", + " X = np.arange(len(chunked_loss))\n", + " Y = np.arange(chunk_size)\n", + " X, Y = np.meshgrid(X, Y)\n", + " Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y\n", + " ax.plot_surface(X, Y, Z, cmap='viridis')\n", + " ax.set_title('3D Surface Plot of Chunked Loss')\n", + " ax.set_xlabel('Chunk Index')\n", + " ax.set_ylabel('Epoch')\n", + " ax.set_zlabel('Loss')\n", + "\n", + " ax = fig.add_subplot(122, projection='3d')\n", + " X = np.arange(len(chunked_accuracy))\n", + " Y = np.arange(chunk_size)\n", + " X, Y = np.meshgrid(X, Y)\n", + " Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y\n", + " ax.plot_surface(X, Y, Z, cmap='viridis')\n", + " ax.set_title('3D Surface Plot of Chunked Accuracy')\n", + " ax.set_xlabel('Chunk Index')\n", + " ax.set_ylabel('Epoch')\n", + " ax.set_zlabel('Accuracy')\n", + "\n", + " # Function to calculate the average of chunks\n", + " def chunked_average(values, chunk_size):\n", + " return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)]\n", + "\n", + " avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size)\n", + " avg_loss_chunks = chunked_average(history['val_loss'], chunk_size)\n", + "\n", + " # Find the chunk with the highest average accuracy\n", + " max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n", + " max_acc_value = avg_accuracy_chunks[max_acc_chunk_index]\n", + "\n", + " # Create a pile plot for accuracy\n", + " plt.figure(figsize=(10, 6))\n", + " plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label='Average Accuracy')\n", + " plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy')\n", + " plt.xlabel('Chunk')\n", + " plt.ylabel('Average Accuracy')\n", + " plt.title('Average Validation Accuracy per Chunk')\n", + " plt.legend()\n", + "\n", + " # Create a pile plot for loss\n", + " plt.figure(figsize=(10, 6))\n", + " plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss')\n", + " plt.xlabel('Chunk')\n", + " plt.ylabel('Average Loss')\n", + " plt.title('Average Validation Loss per Chunk')\n", + " plt.legend()\n", + "\n", + " # Function to calculate the average of each epoch across chunks, ignoring the first chunk\n", + " def average_across_chunks(values, chunk_size):\n", + " num_chunks = len(values) // chunk_size\n", + " avg_values = []\n", + " for epoch in range(chunk_size):\n", + " epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)]\n", + " avg_values.append(np.mean(epoch_values))\n", + " return avg_values\n", + "\n", + " # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk\n", + " avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size)\n", + " avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size)\n", + "\n", + " # Create a bar plot for average accuracy and loss of each epoch across chunks\n", + " plt.figure(figsize=(12, 6))\n", + "\n", + " # Create an index for each epoch\n", + " epoch_indices = np.arange(len(avg_accuracy_epochs))\n", + "\n", + " # Plot accuracy and loss as bars\n", + " plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6)\n", + " plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6)\n", + "\n", + " # Add labels and title\n", + " plt.xlabel('Epoch (within chunk)')\n", + " plt.ylabel('Average Value')\n", + " plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)')\n", + " plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers\n", + " plt.legend()\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "except (ValueError, NameError) as E:\n", + " print(f'\\033[91mFailed to load model history.\\nError: {E}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse model Predicting performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradcam heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### V2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_heatmap(model, img_array, conv_layer_name, pred_index):\n", + " \"\"\"\n", + " Helper function to compute the heatmap for a given convolutional layer.\n", + " \"\"\"\n", + " grad_model = tf.keras.models.Model(\n", + " [model.inputs], \n", + " [model.get_layer(conv_layer_name).output, model.output]\n", + " )\n", + "\n", + " with tf.GradientTape() as tape:\n", + " conv_layer_output, preds = grad_model(img_array)\n", + " class_channel = preds[:, pred_index]\n", + "\n", + " grads = tape.gradient(class_channel, conv_layer_output)\n", + " pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n", + "\n", + " conv_layer_output = conv_layer_output[0]\n", + " heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis]\n", + " heatmap = tf.squeeze(heatmap)\n", + " heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n", + " return heatmap\n", + "\n", + "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, second_last_conv_layer_name=None, pred_index=None, threshold=0, sensitivity_map=1.0):\n", + " \"\"\"\n", + " Function to compute the Grad-CAM heatmap for a specific class, given an input image.\n", + " \"\"\"\n", + " if pred_index is None:\n", + " preds = model.predict(img_array)\n", + " pred_index = tf.argmax(preds[0])\n", + "\n", + " # Compute heatmap for the last convolutional layer\n", + " heatmap = compute_heatmap(model, img_array, last_conv_layer_name, pred_index)\n", + " \n", + " # Apply threshold and adjust sensitivity\n", + " heatmap = np.where(heatmap > threshold, heatmap, 0)\n", + " heatmap = heatmap ** sensitivity_map\n", + "\n", + " if second_last_conv_layer_name is not None:\n", + " # Compute heatmap for the second last convolutional layer\n", + " heatmap_second = compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)\n", + " \n", + " # Apply threshold and adjust sensitivity\n", + " heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)\n", + " heatmap_second = heatmap_second ** sensitivity_map\n", + " \n", + " # Average the two heatmaps\n", + " heatmap = (heatmap + heatmap_second) / 2.0\n", + " \n", + " return heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Main test" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "notebookRunGroups": { + "groupValue": "" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 2s 2s/step\n", + "20/20 [==============================] - 2s 94ms/step\n", + "The accuracy of the model on validation data is 93.75%(93.75000%)\n", + "The accuracy of the model on test data is 97.12%(97.11538%)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Predicting: 1%| | 1/156 [00:00<00:49, 3.12dpb/s]Exception ignored in: \n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\framework\\c_api_util.py\", line 74, in __del__\n", + " self.deleter(obj)\n", + "KeyboardInterrupt: \n", + "Predicting: 17%|β–ˆβ–‹ | 27/156 [00:33<02:38, 1.23s/dpb]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[16], line 136\u001b[0m\n\u001b[0;32m 133\u001b[0m y_test_subset \u001b[38;5;241m=\u001b[39m y_test[indices]\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# Make predictions on the subset of test data\u001b[39;00m\n\u001b[1;32m--> 136\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test_subset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 137\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(test_predictions, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 138\u001b[0m y_test_original_subset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_test_subset, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\training.py:2253\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 2251\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[0;32m 2252\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m-> 2253\u001b[0m tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m 2255\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:954\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m 952\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m 953\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 954\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[0;32m 956\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 957\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m 2494\u001b[0m (graph_function,\n\u001b[0;32m 2495\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2497\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1860\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1861\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1863\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1865\u001b[0m args,\n\u001b[0;32m 1866\u001b[0m possible_gradient_type,\n\u001b[0;32m 1867\u001b[0m executing_eagerly)\n\u001b[0;32m 1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n", + "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix, accuracy_score\n", + "from scipy.stats import binom\n", + "from tqdm import tqdm\n", + "import efficientnet.tfkeras\n", + "import cv2\n", + "import gc\n", + "# Garbage Collection (memory)\n", + "gc.collect()\n", + "\n", + "Extra_EXT = '_T' # _T or _T_BL\n", + "prob_L = 0.9995\n", + "tick_spacing = 5\n", + "Train_data_test = False\n", + "if SAVE_TYPE == 'TF':\n", + " # Load the pre-trained model\n", + " model = load_model(f'PAI_model{Extra_EXT}')\n", + "else:\n", + " # Load the pre-trained model\n", + " model = load_model(f'PAI_model{Extra_EXT}.h5')\n", + "\n", + "# Ensure the model's input_shape matches your data\n", + "assert model.input_shape[1:] == (img_res[0], img_res[1], img_res[2]), 'Models input shape doesnt match data.'\n", + "\n", + "# Make predictions on validation data\n", + "val_predictions = model.predict(x_val)\n", + "val_predictions = np.argmax(val_predictions, axis=1)\n", + "\n", + "# Make predictions on Train data\n", + "if Train_data_test:\n", + " Train_predictions = model.predict(x_train)\n", + " Train_predictions = np.argmax(Train_predictions, axis=1)\n", + "\n", + "# Make predictions on test data\n", + "test_predictions = model.predict(x_test)\n", + "test_predictions = np.argmax(test_predictions, axis=1)\n", + "\n", + "# Convert y_val and y_test from one-hot encoder to their original form\n", + "y_val_original = np.argmax(y_val, axis=1)\n", + "y_test_original = np.argmax(y_test, axis=1)\n", + "if Train_data_test:\n", + " y_train_original = np.argmax(y_train, axis=1)\n", + "\n", + "# Calculate accuracy on validation data\n", + "val_accuracy = accuracy_score(y_val_original, val_predictions)\n", + "\n", + "# Calculate accuracy on Train data\n", + "if Train_data_test:\n", + " Train_accuracy = accuracy_score(y_val_original, Train_predictions)\n", + "\n", + "# Calculate accuracy on test data\n", + "test_accuracy = accuracy_score(y_test_original, test_predictions)\n", + "\n", + "# Print acc\n", + "if Train_data_test:\n", + " print(f'The accuracy of the model on Train data is {Train_accuracy:.2%}({Train_accuracy:.5%})')\n", + "print(f'The accuracy of the model on validation data is {val_accuracy:.2%}({val_accuracy:.5%})')\n", + "print(f'The accuracy of the model on test data is {test_accuracy:.2%}({test_accuracy:.5%})')\n", + "\n", + "# Visualize the predictions on validation data as a grid of squares\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i+1)\n", + " plt.imshow(x_val[i])\n", + " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "#Heatmap\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i+1)\n", + " img = x_val[i]\n", + " heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_conv', sensitivity_map = 2) \n", + " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", + " heatmap = np.uint8(255 * heatmap)\n", + " # Apply Adaptive Histogram Equalization\n", + " clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n", + " heatmap = clahe.apply(heatmap)\n", + " heatmap = cv2.applyColorMap(np.max(heatmap) - heatmap, cv2.COLORMAP_JET)\n", + " if RANGE_NOM:\n", + " superimposed_img = (heatmap / 255) * 0.4 + img \n", + " else:\n", + " superimposed_img = (heatmap / 255) * 0.4 + (img / 255)\n", + " #clip\n", + " superimposed_img = np.clip(superimposed_img, 0, 1) # ensure the values are in the range [0, 1]\n", + " plt.imshow(superimposed_img)\n", + " plt.title(f'True: {y_val_original[i]}\\nPredicted: {val_predictions[i]}')\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Define the list of labels\n", + "labels = ['NORMAL', 'PNEUMONIA']\n", + "\n", + "# Create a confusion matrix for validation data\n", + "val_cm = confusion_matrix(y_val_original, val_predictions)\n", + "\n", + "# Create a confusion matrix for test data\n", + "test_cm = confusion_matrix(y_test_original, test_predictions)\n", + "\n", + "# Plot the confusion matrix as a heatmap for validation data\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(val_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", + "plt.title('Confusion Matrix - Validation Data')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('True')\n", + "plt.show()\n", + "\n", + "# Plot the confusion matrix as a heatmap for test data\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(test_cm, annot=True, cmap='Blues', fmt='d', xticklabels=labels, yticklabels=labels)\n", + "plt.title('Confusion Matrix - Test Data')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('True')\n", + "plt.show()\n", + "\n", + "# Define the range of test data sizes to use\n", + "data_sizes = range(1, len(x_test), 4) \n", + "# Calculate the probability of a wrong prediction based on test accuracy\n", + "prob_wrong = 1 - test_accuracy\n", + "\n", + "# Create a list to store the number of incorrect predictions for each test data size\n", + "incorrect_predictions = []\n", + "\n", + "# Generate predictions and track incorrect predictions for each data size\n", + "for size in tqdm(data_sizes, desc='Predicting', unit='dpb'):\n", + " # Garbage Collection (memory)\n", + " gc.collect()\n", + " # Randomly select a subset of test data\n", + " indices = np.random.choice(len(x_test), size, replace=False)\n", + " x_test_subset = x_test[indices]\n", + " y_test_subset = y_test[indices]\n", + "\n", + " # Make predictions on the subset of test data\n", + " test_predictions = model.predict(x_test_subset, batch_size=1, verbose=0, max_queue_size=120, workers=1, use_multiprocessing=False)\n", + " test_predictions = np.argmax(test_predictions, axis=1)\n", + " y_test_original_subset = np.argmax(y_test_subset, axis=1)\n", + "\n", + " # Calculate the number of incorrect predictions\n", + " incorrect_preds = np.sum(test_predictions != y_test_original_subset)\n", + " incorrect_predictions.append(incorrect_preds)\n", + " \n", + "# Plot the number of incorrect predictions vs. the number of data points\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_sizes, incorrect_predictions)\n", + "plt.xlabel('Number of Data Points')\n", + "plt.ylabel('Number of Incorrect Predictions')\n", + "# Add gridlines for the x and y axes\n", + "plt.grid(True)\n", + "\n", + "# Change the tick spacing for the x and y axes\n", + "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 50))\n", + "plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n", + "\n", + "plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n", + "plt.show()\n", + "\n", + "# Define the range of test data sizes to use\n", + "data_sizes = range(1, len(x_test), 1) \n", + "\n", + "# Calculate the probability of a wrong prediction based on test accuracy\n", + "prob_wrong = 1 - test_accuracy\n", + "\n", + "# Create a list to store the probability of getting at least one wrong answer for each test data size\n", + "probabilities = []\n", + "\n", + "# Calculate the probability of getting at least one wrong answer for each data size\n", + "for size in data_sizes:\n", + " # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n", + " cdf = binom.cdf(0, size, prob_wrong)\n", + " # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n", + " prob = 1 - cdf\n", + " probabilities.append(prob)\n", + "\n", + "# Find the index of the first data point that has a probability greater than prob_L%\n", + "index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n", + "\n", + "# Limit the x-axis to the first data point that has a probability greater than prob_L%\n", + "data_sizes = data_sizes[:index+1]\n", + "probabilities = probabilities[:index+1]\n", + "\n", + "# Plot the probability vs. the number of data points\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(data_sizes, probabilities)\n", + "plt.xlabel('Number of Data Points')\n", + "plt.ylabel('Probability')\n", + "\n", + "# Add gridlines for the x and y axes\n", + "plt.grid(True)\n", + "\n", + "# Change the tick spacing for the x and y axes\n", + "plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, tick_spacing + 2))\n", + "plt.yticks(np.arange(0, max(probabilities)+0.1, tick_spacing / 100))\n", + "\n", + "plt.ylim(top=1.01)\n", + "\n", + "plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/conf/conf.py b/conf/conf.py index 84e927c..8df1c7b 100644 --- a/conf/conf.py +++ b/conf/conf.py @@ -1,5 +1,5 @@ -# Copyright (c) 2023 Aydin Hamedi -# -# This software is released under the MIT License. -# https://opensource.org/licenses/MIT - +# Copyright (c) 2023 Aydin Hamedi +# +# This software is released under the MIT License. +# https://opensource.org/licenses/MIT + diff --git a/doc/Archive/README_OLD.md b/doc/Archive/README_OLD.md index a8c9bdf..737256d 100644 --- a/doc/Archive/README_OLD.md +++ b/doc/Archive/README_OLD.md @@ -1,85 +1,85 @@ -# Pneumonia Prediction AI - - -This project is an AI-based solution designed to predict pneumonia from X-ray images. The AI model processes the images at a resolution of 280x280 using a modified EfficientNetB7 with a custom classifier layer. It uses data augmentation to generate 28,000 samples for training and has achieved an accuracy of 95.51%. - -The project is divided into two parts: - -1. **AI Training**: This part is responsible for training the AI model. -2. **CLI**: A colorful command-line interface (CLI) for using the trained AI model. - -## Table of Contents - -- [Pneumonia Prediction AI](#pneumonia-prediction-ai) - - [Table of Contents](#table-of-contents) - - [Releases](#releases) - - [Usage](#usage) - - [About the AI](#about-the-ai) - - [Computing Environment](#computing-environment) - - [Main Training File](#main-training-file) - - [Cloning the Repository](#cloning-the-repository) - - [Using the CLI Template](#using-the-cli-template) - - [Contributing](#contributing) - - [License](#license) - -## Releases - -There are two releases for this project: - -1. **Source Release**: This release includes the source code for both the AI training and the CLI. -2. **CLI and Model Release**: This release includes only the CLI and the trained model for usage. - -## Usage - -You can run the CLI by executing the `CLI.cmd` file. - -## About the AI - -The AI is designed to predict pneumonia from X-ray images. It processes the images at a resolution of 280x280. The model is a modified EfficientNetB7 with a custom classifier layer. - -The model is implemented using Keras and TensorFlow, two of the most popular libraries for deep learning. Keras provides a high-level, user-friendly API for developing and training machine learning models. - -To enhance the training, the model uses data augmentation to generate 28,000 samples for training. This technique helps improve the model's performance by providing a larger and more varied dataset for training. - -The model has achieved an accuracy of 95.51% at predicting pneumonia from X-ray images. - - -## Computing Environment - -The AI model was trained on a Windows machine with the following specifications: - -- Processor: Intel Core i7-12700KF -- RAM: 64GB -- Graphics Card: NVIDIA GeForce RTX 3090 - -## Main Training File - -The main file for training the AI model is `Model_TT.ipynb`. This Jupyter notebook contains all the code for training the model and should be used as the starting point for understanding the training process. - -## Cloning the Repository - -To clone the repository and run the project on your local machine, follow these steps: - -1. Open your terminal. -2. Change the current working directory to the location where you want the cloned directory. -3. Type `git clone`, and then paste the URL of this repository. It will look something like this: - -```bash -git clone https://github.com/YOUR-USERNAME/YOUR-REPOSITORY -``` - -4. Press Enter to create your local clone. - -## Using the CLI Template - -If you want to use the CLI for your own project, we provide a template version of the CLI. You can find it in the `cli-template` directory in this repository. - -To use the CLI template, you need to replace the AI model with your own model and adjust the input and output processing to match your project's requirements. - -## Contributing - -We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for more details. - -## License - -This project is licensed under the terms of the [MIT License](LICENSE). +# Pneumonia Prediction AI + + +This project is an AI-based solution designed to predict pneumonia from X-ray images. The AI model processes the images at a resolution of 280x280 using a modified EfficientNetB7 with a custom classifier layer. It uses data augmentation to generate 28,000 samples for training and has achieved an accuracy of 95.51%. + +The project is divided into two parts: + +1. **AI Training**: This part is responsible for training the AI model. +2. **CLI**: A colorful command-line interface (CLI) for using the trained AI model. + +## Table of Contents + +- [Pneumonia Prediction AI](#pneumonia-prediction-ai) + - [Table of Contents](#table-of-contents) + - [Releases](#releases) + - [Usage](#usage) + - [About the AI](#about-the-ai) + - [Computing Environment](#computing-environment) + - [Main Training File](#main-training-file) + - [Cloning the Repository](#cloning-the-repository) + - [Using the CLI Template](#using-the-cli-template) + - [Contributing](#contributing) + - [License](#license) + +## Releases + +There are two releases for this project: + +1. **Source Release**: This release includes the source code for both the AI training and the CLI. +2. **CLI and Model Release**: This release includes only the CLI and the trained model for usage. + +## Usage + +You can run the CLI by executing the `CLI.cmd` file. + +## About the AI + +The AI is designed to predict pneumonia from X-ray images. It processes the images at a resolution of 280x280. The model is a modified EfficientNetB7 with a custom classifier layer. + +The model is implemented using Keras and TensorFlow, two of the most popular libraries for deep learning. Keras provides a high-level, user-friendly API for developing and training machine learning models. + +To enhance the training, the model uses data augmentation to generate 28,000 samples for training. This technique helps improve the model's performance by providing a larger and more varied dataset for training. + +The model has achieved an accuracy of 95.51% at predicting pneumonia from X-ray images. + + +## Computing Environment + +The AI model was trained on a Windows machine with the following specifications: + +- Processor: Intel Core i7-12700KF +- RAM: 64GB +- Graphics Card: NVIDIA GeForce RTX 3090 + +## Main Training File + +The main file for training the AI model is `Model_TT.ipynb`. This Jupyter notebook contains all the code for training the model and should be used as the starting point for understanding the training process. + +## Cloning the Repository + +To clone the repository and run the project on your local machine, follow these steps: + +1. Open your terminal. +2. Change the current working directory to the location where you want the cloned directory. +3. Type `git clone`, and then paste the URL of this repository. It will look something like this: + +```bash +git clone https://github.com/YOUR-USERNAME/YOUR-REPOSITORY +``` + +4. Press Enter to create your local clone. + +## Using the CLI Template + +If you want to use the CLI for your own project, we provide a template version of the CLI. You can find it in the `cli-template` directory in this repository. + +To use the CLI template, you need to replace the AI model with your own model and adjust the input and output processing to match your project's requirements. + +## Contributing + +We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for more details. + +## License + +This project is licensed under the terms of the [MIT License](LICENSE). diff --git a/env/Test_ENV.py b/env/Test_ENV.py index 141c58c..cb707c1 100644 --- a/env/Test_ENV.py +++ b/env/Test_ENV.py @@ -1,143 +1,143 @@ -#!/usr/bin/python3 -import threading -import time -import itertools -import PySimpleGUI as sg - -""" - DESIGN PATTERN - Multithreaded GUI - One method for running multiple threads in a PySimpleGUI environment. - The PySimpleGUI code, and thus the underlying GUI framework, runs as the primary, main thread - Other parts of the software are implemented as threads - - While users never know the implementation details within PySimpleGUI, the mechanism is that a queue.Queue - is used to communicate data between a thread and a PySimpleGUI window. - The PySimpleGUI code is structured just like a typical PySimpleGUI program. A layout defined, - a Window is created, and an event loop is executed. - - Copyright 2020 PySimpleGUI.org - -""" - - -# ######## ## ## ######## ######## ### ######## -# ## ## ## ## ## ## ## ## ## ## -# ## ## ## ## ## ## ## ## ## ## -# ## ######### ######## ###### ## ## ## ## -# ## ## ## ## ## ## ######### ## ## -# ## ## ## ## ## ## ## ## ## ## -# ## ## ## ## ## ######## ## ## ######## - -def worker_thread1(thread_name, run_freq, window): - """ - A worker thread that communicates with the GUI - These threads can call functions that block without affecting the GUI (a good thing) - Note that this function is the code started as each thread. All threads are identical in this way - :param thread_name: Text name used for displaying info - :param run_freq: How often the thread should run in milliseconds - :param window: window this thread will be conversing with - :type window: sg.Window - :return: - """ - print('Starting thread 1 - {} that runs every {} ms'.format(thread_name, run_freq)) - for i in itertools.count(): # loop forever, keeping count in i as it loops - time.sleep(run_freq/1000) # sleep for a while - # put a message into queue for GUI - window.write_event_value(thread_name, f'count = {i}') - - -def worker_thread2(thread_name, run_freq, window): - """ - A worker thread that communicates with the GUI - These threads can call functions that block without affecting the GUI (a good thing) - Note that this function is the code started as each thread. All threads are identical in this way - :param thread_name: Text name used for displaying info - :param run_freq: How often the thread should run in milliseconds - :param window: window this thread will be conversing with - :type window: sg.Window - :return: - """ - print('Starting thread 2 - {} that runs every {} ms'.format(thread_name, run_freq)) - for i in itertools.count(): # loop forever, keeping count in i as it loops - time.sleep(run_freq/1000) # sleep for a while - # put a message into queue for GUI - window.write_event_value(thread_name, f'count = {i}') - - -def worker_thread3(thread_name, run_freq, window): - """ - A worker thread that communicates with the GUI - These threads can call functions that block without affecting the GUI (a good thing) - Note that this function is the code started as each thread. All threads are identical in this way - :param thread_name: Text name used for displaying info - :param run_freq: How often the thread should run in milliseconds - :param window: window this thread will be conversing with - :type window: sg.Window - :return: - """ - print('Starting thread 3 - {} that runs every {} ms'.format(thread_name, run_freq)) - for i in itertools.count(): # loop forever, keeping count in i as it loops - time.sleep(run_freq/1000) # sleep for a while - # put a message into queue for GUI - window.write_event_value(thread_name, f'count = {i}') - - - -# ###### ## ## #### -# ## ## ## ## ## -# ## ## ## ## -# ## #### ## ## ## -# ## ## ## ## ## -# ## ## ## ## ## -# ###### ####### #### - - -def the_gui(): - """ - Starts and executes the GUI - Reads data from a Queue and displays the data to the window - Returns when the user exits / closes the window - (that means it does NOT return until the user exits the window) - :param gui_queue: Queue the GUI should read from - :return: - """ - layout = [[sg.Text('Multithreaded Window Example')], - [sg.Text('', size=(15, 1), key='-OUTPUT-')], - [sg.Multiline(size=(40, 26), key='-ML-', autoscroll=True)], - [sg.Button('Exit')], ] - - window = sg.Window('Multithreaded Window', layout, finalize=True) - - # -- Create a Queue to communicate with GUI -- - # queue used to communicate between the gui and the threads - # -- Start worker threads, each taking a different amount of time - threading.Thread(target=worker_thread1, args=('Thread 1', 500, window,), daemon=True).start() - threading.Thread(target=worker_thread2, args=('Thread 2', 200, window,), daemon=True).start() - threading.Thread(target=worker_thread3, args=('Thread 3', 1000, window,), daemon=True).start() - # -- Start the GUI passing in the Queue -- - - sg.cprint_set_output_destination(window, '-ML-') - - colors = {'Thread 1':('white', 'red'), 'Thread 2':('white', 'purple'), 'Thread 3':('white', 'blue')} - # --------------------- EVENT LOOP --------------------- - while True: - # wait for up to 100 ms for a GUI event - event, values = window.read() - if event in (sg.WIN_CLOSED, 'Exit'): - break - # --------------- Loop through all messages coming in from threads --------------- - sg.cprint(event, values[event], c=colors[event]) - # if user exits the window, then close the window and exit the GUI func - window.close() - - -## ## ### #### ## ## -### ### ## ## ## ### ## -#### #### ## ## ## #### ## -## ### ## ## ## ## ## ## ## -## ## ######### ## ## #### -## ## ## ## ## ## ### -## ## ## ## #### ## ## - -if __name__ == '__main__': +#!/usr/bin/python3 +import threading +import time +import itertools +import PySimpleGUI as sg + +""" + DESIGN PATTERN - Multithreaded GUI + One method for running multiple threads in a PySimpleGUI environment. + The PySimpleGUI code, and thus the underlying GUI framework, runs as the primary, main thread + Other parts of the software are implemented as threads + + While users never know the implementation details within PySimpleGUI, the mechanism is that a queue.Queue + is used to communicate data between a thread and a PySimpleGUI window. + The PySimpleGUI code is structured just like a typical PySimpleGUI program. A layout defined, + a Window is created, and an event loop is executed. + + Copyright 2020 PySimpleGUI.org + +""" + + +# ######## ## ## ######## ######## ### ######## +# ## ## ## ## ## ## ## ## ## ## +# ## ## ## ## ## ## ## ## ## ## +# ## ######### ######## ###### ## ## ## ## +# ## ## ## ## ## ## ######### ## ## +# ## ## ## ## ## ## ## ## ## ## +# ## ## ## ## ## ######## ## ## ######## + +def worker_thread1(thread_name, run_freq, window): + """ + A worker thread that communicates with the GUI + These threads can call functions that block without affecting the GUI (a good thing) + Note that this function is the code started as each thread. All threads are identical in this way + :param thread_name: Text name used for displaying info + :param run_freq: How often the thread should run in milliseconds + :param window: window this thread will be conversing with + :type window: sg.Window + :return: + """ + print('Starting thread 1 - {} that runs every {} ms'.format(thread_name, run_freq)) + for i in itertools.count(): # loop forever, keeping count in i as it loops + time.sleep(run_freq/1000) # sleep for a while + # put a message into queue for GUI + window.write_event_value(thread_name, f'count = {i}') + + +def worker_thread2(thread_name, run_freq, window): + """ + A worker thread that communicates with the GUI + These threads can call functions that block without affecting the GUI (a good thing) + Note that this function is the code started as each thread. All threads are identical in this way + :param thread_name: Text name used for displaying info + :param run_freq: How often the thread should run in milliseconds + :param window: window this thread will be conversing with + :type window: sg.Window + :return: + """ + print('Starting thread 2 - {} that runs every {} ms'.format(thread_name, run_freq)) + for i in itertools.count(): # loop forever, keeping count in i as it loops + time.sleep(run_freq/1000) # sleep for a while + # put a message into queue for GUI + window.write_event_value(thread_name, f'count = {i}') + + +def worker_thread3(thread_name, run_freq, window): + """ + A worker thread that communicates with the GUI + These threads can call functions that block without affecting the GUI (a good thing) + Note that this function is the code started as each thread. All threads are identical in this way + :param thread_name: Text name used for displaying info + :param run_freq: How often the thread should run in milliseconds + :param window: window this thread will be conversing with + :type window: sg.Window + :return: + """ + print('Starting thread 3 - {} that runs every {} ms'.format(thread_name, run_freq)) + for i in itertools.count(): # loop forever, keeping count in i as it loops + time.sleep(run_freq/1000) # sleep for a while + # put a message into queue for GUI + window.write_event_value(thread_name, f'count = {i}') + + + +# ###### ## ## #### +# ## ## ## ## ## +# ## ## ## ## +# ## #### ## ## ## +# ## ## ## ## ## +# ## ## ## ## ## +# ###### ####### #### + + +def the_gui(): + """ + Starts and executes the GUI + Reads data from a Queue and displays the data to the window + Returns when the user exits / closes the window + (that means it does NOT return until the user exits the window) + :param gui_queue: Queue the GUI should read from + :return: + """ + layout = [[sg.Text('Multithreaded Window Example')], + [sg.Text('', size=(15, 1), key='-OUTPUT-')], + [sg.Multiline(size=(40, 26), key='-ML-', autoscroll=True)], + [sg.Button('Exit')], ] + + window = sg.Window('Multithreaded Window', layout, finalize=True) + + # -- Create a Queue to communicate with GUI -- + # queue used to communicate between the gui and the threads + # -- Start worker threads, each taking a different amount of time + threading.Thread(target=worker_thread1, args=('Thread 1', 500, window,), daemon=True).start() + threading.Thread(target=worker_thread2, args=('Thread 2', 200, window,), daemon=True).start() + threading.Thread(target=worker_thread3, args=('Thread 3', 1000, window,), daemon=True).start() + # -- Start the GUI passing in the Queue -- + + sg.cprint_set_output_destination(window, '-ML-') + + colors = {'Thread 1':('white', 'red'), 'Thread 2':('white', 'purple'), 'Thread 3':('white', 'blue')} + # --------------------- EVENT LOOP --------------------- + while True: + # wait for up to 100 ms for a GUI event + event, values = window.read() + if event in (sg.WIN_CLOSED, 'Exit'): + break + # --------------- Loop through all messages coming in from threads --------------- + sg.cprint(event, values[event], c=colors[event]) + # if user exits the window, then close the window and exit the GUI func + window.close() + + +## ## ### #### ## ## +### ### ## ## ## ### ## +#### #### ## ## ## #### ## +## ### ## ## ## ## ## ## ## +## ## ######### ## ## #### +## ## ## ## ## ## ### +## ## ## ## #### ## ## + +if __name__ == '__main__': the_gui() \ No newline at end of file diff --git a/env/Test_ENV2.py b/env/Test_ENV2.py index b9ea956..b74ff47 100644 --- a/env/Test_ENV2.py +++ b/env/Test_ENV2.py @@ -1,10 +1,10 @@ -# Utils -from Utils.one_cycle import OneCycleLr -from Utils.lr_find import LrFinder -from Utils.print_color_V2_NEW import print_Color_V2 -from Utils.print_color_V1_OLD import print_Color -from Utils.Other import * -# PySimpleGUI -import PySimpleGUI as sg - +# Utils +from Utils.one_cycle import OneCycleLr +from Utils.lr_find import LrFinder +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from Utils.Other import * +# PySimpleGUI +import PySimpleGUI as sg + sg.theme_previewer() \ No newline at end of file diff --git a/env/Utils/Grad_cam.py b/env/Utils/Grad_cam.py new file mode 100644 index 0000000..c63729a --- /dev/null +++ b/env/Utils/Grad_cam.py @@ -0,0 +1,63 @@ +import os +import glob +import numpy as np +import tensorflow as tf +# Other +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +tf.get_logger().setLevel('ERROR') +physical_devices = tf.config.list_physical_devices('GPU') +for gpu_instance in physical_devices: + tf.config.experimental.set_memory_growth(gpu_instance, True) + +# Main +def _compute_heatmap(model, + img_array, + conv_layer_name, + pred_index): + """ + Helper function to compute the heatmap for a given convolutional layer. + """ + grad_model = tf.keras.models.Model( + [model.inputs], + [model.get_layer(conv_layer_name).output, model.output] + ) + + with tf.GradientTape() as tape: + conv_layer_output, preds = grad_model(img_array) + class_channel = preds[:, pred_index] + + grads = tape.gradient(class_channel, conv_layer_output) + pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) + + conv_layer_output = conv_layer_output[0] + heatmap = conv_layer_output @ pooled_grads[..., tf.newaxis] + heatmap = tf.squeeze(heatmap) + heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap) + return heatmap + +def make_gradcam_heatmap(img_array, + model, + last_conv_layer_name, + second_last_conv_layer_name=None, + pred_index=None, + sensitivity_map=1.0): + """ + Function to compute the Grad-CAM heatmap for a specific class, given an input image. + """ + if pred_index is None: + preds = model.predict(img_array) + pred_index = tf.argmax(preds[0]) + + # Compute heatmap for the last convolutional layer + heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index) + heatmap = heatmap ** sensitivity_map + + if second_last_conv_layer_name is not None: + # Compute heatmap for the second last convolutional layer + heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index) + heatmap_second = heatmap_second ** sensitivity_map + + # Average the two heatmaps + heatmap = (heatmap + heatmap_second) / 2.0 + + return heatmap \ No newline at end of file diff --git a/env/Utils/Other.py b/env/Utils/Other.py index 71562e0..b882eb7 100644 --- a/env/Utils/Other.py +++ b/env/Utils/Other.py @@ -1,19 +1,92 @@ -import pickle -import gzip - -def save_list(history, filename, compress=True): - if compress: - with gzip.open(filename, 'wb') as f: - pickle.dump(history, f) - else: - with open(filename, 'wb') as f: - pickle.dump(history, f) - -def load_list(filename, compressed=True): - if compressed: - with gzip.open(filename, 'rb') as f: - return pickle.load(f) - else: - with open(filename, 'rb') as f: - return pickle.load(f) - +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score +from Utils.print_color_V2_NEW import print_Color_V2 +from Utils.print_color_V1_OLD import print_Color +from tabulate import tabulate +import numpy as np +import pickle +import gzip + +def save_list(history, filename, compress=True): + """Saves a list to a file. + + Args: + history: The list to save. + filename: The file to save the list to. + compress: Whether to gzip compress the file. Default is True. + + """ + if compress: + with gzip.open(filename, 'wb') as f: + pickle.dump(history, f) + else: + with open(filename, 'wb') as f: + pickle.dump(history, f) + + +def load_list(filename, compressed=True): + """Loads a list from a file. + + Args: + filename: The file to load from. + compressed: Whether the file is gzip compressed. Default is True. + + Returns: + The loaded list from the file. + """ + if compressed: + with gzip.open(filename, 'rb') as f: + return pickle.load(f) + else: + with open(filename, 'rb') as f: + return pickle.load(f) + + +def P_warning(msg): + """Prints a warning message to the console. + + Args: + msg (str): The warning message to print. + """ + print_Color_V2(f'Warning: {msg}') + +def evaluate_model_full(y_test, model_pred, model=None, x_test=None): + """Evaluates a machine learning model on a test set. + + Args: + x_test: Test set features. + y_test: Test set labels. + model_pred: Model predictions. + model: The model object. + + Returns: + None. Prints a table with accuracy, precision, recall and + F1 score. + """ + # Get the model predictions + if model_pred is None: + y_pred = model.predict(x_test) + else: + y_pred = model_pred + + # Convert one-hot encoded predictions and labels to label encoded form + y_pred_bin = np.argmax(y_pred, axis=1) + y_test_bin = np.argmax(y_test, axis=1) + + # Calculate normal metrics + accuracy = accuracy_score(y_test_bin, y_pred_bin) + + # Calculate weighted metrics + weighted_precision = precision_score( + y_test_bin, y_pred_bin, average='weighted') + weighted_f1 = f1_score(y_test_bin, y_pred_bin, average='weighted') + weighted_recall = recall_score(y_test_bin, y_pred_bin, average='weighted') + + # Prepare data for the table + metrics = [["Accuracy", round(accuracy * 100, 6)], + ["Precision", round(weighted_precision * 100, 6)], + ["F1 Score", round(weighted_f1 * 100, 6)], + ["Recall", round(weighted_recall * 100, 6)]] + + # Print the table + print(tabulate(metrics, headers=["Metric", "Value"], tablefmt="pretty")) + diff --git a/env/Utils/README.md b/env/Utils/README.md index bb6b9f5..16ffebb 100644 --- a/env/Utils/README.md +++ b/env/Utils/README.md @@ -1,13 +1,15 @@ -# Utils: - -## one_cycle_lr and lr_find (by 'benihime91') -- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) - - ### doc link: [1_README.md](docs\1_README.md) - -## Python-color-print-V2 and Python-color-print (by Me) -- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) - - ### doc link: [2_README.md](docs\2_README.md) -- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) - - ### doc link: [3_README.md](docs\3_README.md) - -## Other (by Me) +# Utils: + +## one_cycle_lr and lr_find (by 'benihime91') +- ### github repo used: [one_cycle_lr-tensorflow](https://github.com/benihime91/one_cycle_lr-tensorflow/tree/master) + - ### doc link: [1_README.md](docs\1_README.md) + +## Python-color-print-V2 and Python-color-print (by Me) +- ### github repo used(Python-color-print-V2): [Python-color-print-V2](https://github.com/Aydinhamedi/Python-color-print-V2) + - ### doc link: [2_README.md](docs\2_README.md) +- ### github repo used(Python-color-print): [Python-color-print](https://github.com/Aydinhamedi/Python-color-print) + - ### doc link: [3_README.md](docs\3_README.md) + +## Grad_cam (by GPT-4 😁) + +## Other.py (by Me) diff --git a/env/Utils/print_color_V1_OLD.py b/env/Utils/print_color_V1_OLD.py index 7ea9514..15cd24a 100644 --- a/env/Utils/print_color_V1_OLD.py +++ b/env/Utils/print_color_V1_OLD.py @@ -1,5 +1,5 @@ #the print_Color func -def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False): +def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: bool = False, return_str: bool = False): """ Prints colored text to the console using advanced terminal colors. @@ -8,7 +8,7 @@ def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: colors (list): A list of colors for the text. In non-advanced mode, only the first color in the list is used. In advanced mode, each color corresponds to a part of the input string separated by '~*'. print_END (str): The string appended after the final output. Default is '\\n'. advanced_mode (bool): If True, enables advanced mode that allows multiple colors in one string. Default is False. - + return_str (bool): If True, returns the colored string instead of printing it. Default is False. Examples: ~~~python print_Color('Hello, World!', ['green']) @@ -60,21 +60,30 @@ def print_Color(Input: str, colors: list, print_END: str = '\n', advanced_mode: 'underline': '\x1b[4m', 'blink': '\x1b[5m' } - + return_temp = '' if not advanced_mode: if colors[0] in color_code: + if return_str: + return color_code[colors[0]] + Input + '\x1b[0m' print(color_code[colors[0]] + Input + '\x1b[0m', end=print_END) else: print("[print_Color] ERROR: Invalid color input!!!") else: substrings = Input.split('~*') if len(substrings) != len(colors) + 1: - print("[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") + print( + "[print_Color] ERROR: Number of colors and number of '~*' don't match!!!") else: for sub_str, color in zip(substrings, ['normal'] + colors): if color in color_code: - print(color_code[color] + sub_str + '\x1b[0m', end='') + if return_str: + return_temp += color_code[color] + sub_str + '\x1b[0m' + else: + print(color_code[color] + sub_str + '\x1b[0m', end='') else: - print(f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") + print( + f"\n[print_Color] ERROR: Invalid color!!! The input color: '{color}' input list index: {colors.index(color)}") print('', end=print_END) + if return_str: + return return_temp #the func end \ No newline at end of file diff --git a/history/CSV_C.py b/history/CSV_C.py index b8c351b..30f756b 100644 --- a/history/CSV_C.py +++ b/history/CSV_C.py @@ -1,16 +1,16 @@ -import pickle -import gzip -import pandas as pd - -def load_list(filename, compressed=True): - if compressed: - with gzip.open(filename, 'rb') as f: - return pickle.load(f) - else: - with open(filename, 'rb') as f: - return pickle.load(f) - -Data = load_list('history\\model_history.pkl.gz', compressed=True) - -df = pd.DataFrame(Data) +import pickle +import gzip +import pandas as pd + +def load_list(filename, compressed=True): + if compressed: + with gzip.open(filename, 'rb') as f: + return pickle.load(f) + else: + with open(filename, 'rb') as f: + return pickle.load(f) + +Data = load_list('history\\model_history.pkl.gz', compressed=True) + +df = pd.DataFrame(Data) df.to_csv(r'history\\model_history_CSV.csv') \ No newline at end of file diff --git a/history/model_history_CSV.csv b/history/model_history_CSV.csv index cff9380..70c1754 100644 --- a/history/model_history_CSV.csv +++ b/history/model_history_CSV.csv @@ -1,469 +1,469 @@ -,loss,accuracy,val_loss,val_accuracy -0,21.55794334411621,0.60400390625,18.430618286132812,0.625 -1,14.369561195373535,0.748046875,10.786662101745605,0.6794871687889099 -2,8.290704727172852,0.814453125,6.37089204788208,0.7820512652397156 -3,5.093355655670166,0.84521484375,4.146455764770508,0.8349359035491943 -4,3.485626697540283,0.892578125,3.113571882247925,0.8926281929016113 -5,2.8950793743133545,0.8994140625,2.901104688644409,0.8990384340286255 -6,2.8040542602539062,0.85009765625,2.4500768184661865,0.8717948794364929 -7,2.1212971210479736,0.8505859375,1.8456534147262573,0.7772436141967773 -8,1.4827358722686768,0.8623046875,1.1592118740081787,0.9118589758872986 -9,1.0447114706039429,0.89208984375,0.8903440833091736,0.9022436141967773 -10,0.8193393349647522,0.90576171875,0.7527596354484558,0.9038461446762085 -11,0.6877335906028748,0.92138671875,0.7338330745697021,0.9086538553237915 -12,1.2390332221984863,0.849609375,1.0995858907699585,0.8878205418586731 -13,0.9968419075012207,0.8671875,0.8620863556861877,0.9086538553237915 -14,0.7445922493934631,0.892578125,0.6972772479057312,0.9070512652397156 -15,0.5628113150596619,0.908203125,0.500565767288208,0.9150640964508057 -16,0.4489874541759491,0.92822265625,0.60695880651474,0.8830128312110901 -17,0.4044981002807617,0.92822265625,0.5216004848480225,0.9022436141967773 -18,0.5554506182670593,0.88134765625,0.46677955985069275,0.9198718070983887 -19,0.4910257160663605,0.87841796875,0.44720810651779175,0.8974359035491943 -20,0.413088858127594,0.90185546875,0.3975745439529419,0.9038461446762085 -21,0.3290833532810211,0.92041015625,0.3632175028324127,0.9134615659713745 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delta_accuracy = np.diff(history['accuracy']) - - try: - delta_val_loss = np.diff(history['val_loss']) - delta_val_accuracy = np.diff(history['val_accuracy']) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') - - plt.figure(figsize=(16, 10)) - # Loss - plt.subplot(2, 2, 1) - plt.plot(history['loss'], label='loss') - try: - plt.plot(history['val_loss'], label='val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_loss.') - plt.title('Model Loss') - plt.ylabel('Loss') - plt.xlabel(EPM) - plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - # Density plot for loss - plt.subplot(2, 2, 2) - plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) - try: - plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) - except (ValueError, NameError): - print('\033[91mfailed to load val_loss (density plot).') - plt.title('Density Plot for Loss') - plt.xlabel('Loss') - plt.xlim(right=max(history['val_loss'][10:]), left=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 - plt.grid(True) - - - # Accuracy - plt.subplot(2, 2, 3) - plt.plot(history['accuracy'], label='accuracy') - try: - plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy.') - plt.title('Model Accuracy') - plt.ylabel('Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Density plot for accuracy - plt.subplot(2, 2, 4) - plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) - try: - plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) - except (ValueError, NameError): - print('\033[91mfailed to load val_accuracy (density plot).') - plt.title('Density Plot for Accuracy') - plt.xlabel('Accuracy') - plt.grid(True) - - # Delta Loss - plt.figure(figsize=(14, 8)) - plt.subplot(2, 2, 1) - plt.plot(delta_loss, label='delta_loss') - try: - plt.plot(delta_val_loss, label='delta_val_loss', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_loss.') - plt.title('Delta Model Loss') - plt.ylabel('Delta Loss') - plt.ylim(top=1.5, bottom=-1.5) - plt.xlabel(EPM) - plt.grid(True) - # Delta Accuracy - plt.subplot(2, 2, 2) - plt.plot(delta_accuracy, label='delta_accuracy') - try: - plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') - except (ValueError, NameError): - print('\033[91mfailed to load delta_val_accuracy.') - plt.title('Delta Model Accuracy') - plt.ylabel('Delta Accuracy') - plt.xlabel(EPM) - plt.grid(True) - - # Calculate chunked data - chunked_loss = chunked_data(history['val_loss'], chunk_size) - chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) - - # Clip the loss values to a maximum of max(history['val_loss'][10:]) - max_loss = max(history['val_loss'][10:]) - chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) - - # Create 3D surface plots for each chunk - fig = plt.figure(figsize=(14, 8)) - ax = fig.add_subplot(121, projection='3d') - X = np.arange(len(chunked_loss)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Loss') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Loss') - - ax = fig.add_subplot(122, projection='3d') - X = np.arange(len(chunked_accuracy)) - Y = np.arange(chunk_size) - X, Y = np.meshgrid(X, Y) - Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y - ax.plot_surface(X, Y, Z, cmap='viridis') - ax.set_title('3D Surface Plot of Chunked Accuracy') - ax.set_xlabel('Chunk Index') - ax.set_ylabel('Epoch') - ax.set_zlabel('Accuracy') - - # Function to calculate the average of chunks - def chunked_average(values, chunk_size): - return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] - - avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) - avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) - - # Find the chunk with the highest average accuracy - max_acc_chunk_index = np.argmax(avg_accuracy_chunks) - max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] - - # Create a pile plot for accuracy - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, color='blue', label='Average Accuracy') - plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') - plt.xlabel('Chunk') - plt.ylabel('Average Accuracy') - plt.title('Average Validation Accuracy per Chunk') - plt.legend() - - # Create a pile plot for loss - plt.figure(figsize=(10, 6)) - plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') - plt.xlabel('Chunk') - plt.ylabel('Average Loss') - plt.title('Average Validation Loss per Chunk') - plt.legend() - - # Function to calculate the average of each epoch across chunks, ignoring the first chunk - def average_across_chunks(values, chunk_size): - num_chunks = len(values) // chunk_size - avg_values = [] - for epoch in range(chunk_size): - epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] - avg_values.append(np.mean(epoch_values)) - return avg_values - - # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk - avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) - avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) - - # Create a bar plot for average accuracy and loss of each epoch across chunks - plt.figure(figsize=(12, 6)) - - # Create an index for each epoch - epoch_indices = np.arange(len(avg_accuracy_epochs)) - - # Plot accuracy and loss as bars - plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) - plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) - - # Add labels and title - plt.xlabel('Epoch (within chunk)') - plt.ylabel('Average Value') - plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') - plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers - plt.legend() - - plt.tight_layout() - plt.show() - -except (ValueError, NameError) as E: +from turtle import left +from Utils.Other import * +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns +import numpy as np + +# load history +history = load_list('history\\model_history.pkl.gz', compressed=True) + +# Chunk size for 3D plot +chunk_size = 6 # Change this to your desired chunk size + + +def chunked_data(data, chunk_size): + return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)] + + +try: + EPM = 'Epoch(Subset)' + + # Calculate deltas + delta_loss = np.diff(history['loss']) + delta_accuracy = np.diff(history['accuracy']) + + try: + delta_val_loss = np.diff(history['val_loss']) + delta_val_accuracy = np.diff(history['val_accuracy']) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss or val_accuracy for delta calculation.') + + plt.figure(figsize=(16, 10)) + # Loss + plt.subplot(2, 2, 1) + plt.plot(history['loss'], label='loss') + try: + plt.plot(history['val_loss'], label='val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_loss.') + plt.title('Model Loss') + plt.ylabel('Loss') + plt.xlabel(EPM) + plt.ylim(top=max(history['val_loss'][10:]), bottom=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + # Density plot for loss + plt.subplot(2, 2, 2) + plt.hist(history['loss'], label='loss density', color='blue', alpha=0.5, bins=100) + try: + plt.hist(history['val_loss'], label='val_loss density', color='orange', alpha=0.5, bins=100) + except (ValueError, NameError): + print('\033[91mfailed to load val_loss (density plot).') + plt.title('Density Plot for Loss') + plt.xlabel('Loss') + plt.xlim(right=max(history['val_loss'][10:]), left=0) # (max(history['val_loss'][8:]) + min(history['val_loss'])) / 2 + plt.grid(True) + + + # Accuracy + plt.subplot(2, 2, 3) + plt.plot(history['accuracy'], label='accuracy') + try: + plt.plot(history['val_accuracy'], label='val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy.') + plt.title('Model Accuracy') + plt.ylabel('Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Density plot for accuracy + plt.subplot(2, 2, 4) + plt.hist(history['accuracy'], label='accuracy density', color='blue', alpha=0.5, bins=40) + try: + plt.hist(history['val_accuracy'], label='val_accuracy density', color='orange', alpha=0.5, bins=40) + except (ValueError, NameError): + print('\033[91mfailed to load val_accuracy (density plot).') + plt.title('Density Plot for Accuracy') + plt.xlabel('Accuracy') + plt.grid(True) + + # Delta Loss + plt.figure(figsize=(14, 8)) + plt.subplot(2, 2, 1) + plt.plot(delta_loss, label='delta_loss') + try: + plt.plot(delta_val_loss, label='delta_val_loss', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_loss.') + plt.title('Delta Model Loss') + plt.ylabel('Delta Loss') + plt.ylim(top=1.5, bottom=-1.5) + plt.xlabel(EPM) + plt.grid(True) + # Delta Accuracy + plt.subplot(2, 2, 2) + plt.plot(delta_accuracy, label='delta_accuracy') + try: + plt.plot(delta_val_accuracy, label='delta_val_accuracy', color='orange') + except (ValueError, NameError): + print('\033[91mfailed to load delta_val_accuracy.') + plt.title('Delta Model Accuracy') + plt.ylabel('Delta Accuracy') + plt.xlabel(EPM) + plt.grid(True) + + # Calculate chunked data + chunked_loss = chunked_data(history['val_loss'], chunk_size) + chunked_accuracy = chunked_data(history['val_accuracy'], chunk_size) + + # Clip the loss values to a maximum of max(history['val_loss'][10:]) + max_loss = max(history['val_loss'][10:]) + chunked_loss = np.clip(chunked_loss, a_min=None, a_max=max_loss) + + # Create 3D surface plots for each chunk + fig = plt.figure(figsize=(14, 8)) + ax = fig.add_subplot(121, projection='3d') + X = np.arange(len(chunked_loss)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_loss).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Loss') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Loss') + + ax = fig.add_subplot(122, projection='3d') + X = np.arange(len(chunked_accuracy)) + Y = np.arange(chunk_size) + X, Y = np.meshgrid(X, Y) + Z = np.array(chunked_accuracy).T # Transpose the array to match the shape of X and Y + ax.plot_surface(X, Y, Z, cmap='viridis') + ax.set_title('3D Surface Plot of Chunked Accuracy') + ax.set_xlabel('Chunk Index') + ax.set_ylabel('Epoch') + ax.set_zlabel('Accuracy') + + # Function to calculate the average of chunks + def chunked_average(values, chunk_size): + return [np.mean(values[i:i + chunk_size]) for i in range(0, len(values), chunk_size)] + + avg_accuracy_chunks = chunked_average(history['val_accuracy'], chunk_size) + avg_loss_chunks = chunked_average(history['val_loss'], chunk_size) + + # Find the chunk with the highest average accuracy + max_acc_chunk_index = np.argmax(avg_accuracy_chunks) + max_acc_value = avg_accuracy_chunks[max_acc_chunk_index] + + # Create a pile plot for accuracy + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, color='blue', label='Average Accuracy') + plt.bar(max_acc_chunk_index, max_acc_value, color='red', label='Highest Average Accuracy') + plt.xlabel('Chunk') + plt.ylabel('Average Accuracy') + plt.title('Average Validation Accuracy per Chunk') + plt.legend() + + # Create a pile plot for loss + plt.figure(figsize=(10, 6)) + plt.bar(range(len(avg_loss_chunks)), avg_loss_chunks, color='green', label='Average Loss') + plt.xlabel('Chunk') + plt.ylabel('Average Loss') + plt.title('Average Validation Loss per Chunk') + plt.legend() + + # Function to calculate the average of each epoch across chunks, ignoring the first chunk + def average_across_chunks(values, chunk_size): + num_chunks = len(values) // chunk_size + avg_values = [] + for epoch in range(chunk_size): + epoch_values = [values[chunk * chunk_size + epoch] for chunk in range(1, num_chunks)] + avg_values.append(np.mean(epoch_values)) + return avg_values + + # Calculate the average accuracy and loss for each epoch across chunks, ignoring the first chunk + avg_accuracy_epochs = average_across_chunks(history['val_accuracy'], chunk_size) + avg_loss_epochs = average_across_chunks(history['val_loss'], chunk_size) + + # Create a bar plot for average accuracy and loss of each epoch across chunks + plt.figure(figsize=(12, 6)) + + # Create an index for each epoch + epoch_indices = np.arange(len(avg_accuracy_epochs)) + + # Plot accuracy and loss as bars + plt.bar(epoch_indices - 0.2, avg_accuracy_epochs, width=0.4, label='Average Accuracy', color='blue', alpha=0.6) + plt.bar(epoch_indices + 0.2, avg_loss_epochs, width=0.4, label='Average Loss', color='orange', alpha=0.6) + + # Add labels and title + plt.xlabel('Epoch (within chunk)') + plt.ylabel('Average Value') + plt.title('Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)') + plt.xticks(epoch_indices, [f'Epoch {i+1}' for i in epoch_indices]) # Set x-tick labels to epoch numbers + plt.legend() + + plt.tight_layout() + plt.show() + +except (ValueError, NameError) as E: print(f'\033[91mFailed to load model history.\nError: {E}') \ No newline at end of file diff --git a/logs/README.md b/logs/README.md index 83d7aa9..4c414c6 100644 --- a/logs/README.md +++ b/logs/README.md @@ -1,7 +1,7 @@ -# TensorBoard logs - -### You can open the TensorBoard logs by running: -```bash -tensorboard --logdir logs/fit --reload_interval 30 --reload_multifile True -``` - +# TensorBoard logs + +### You can open the TensorBoard logs by running: +```bash +tensorboard --logdir logs/fit --reload_interval 30 --reload_multifile True +``` + diff --git a/requirements.txt b/requirements.txt index 5d03310..66ba448 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,30 +1,30 @@ -absl-py==1.4.0 -adabelief-tf==0.2.1 -efficientnet==1.1.1 -gpu-control==1.0.0 -hyperas==0.4.1 -imbalanced-learn==0.11.0 -keras==2.10.0 -keras-adabound==0.6.0 -keras-efficientnet-v2==1.2.2 -keras-gradient-noise==0.11 -loguru==0.5.3 -matplotlib==3.7.2 -model-profiler==1.1.8 -numpy==1.25.1 -opencv-python==4.8.0.74 -pandas==2.0.3 -Pillow -psutil==5.9.5 -py-cpuinfo==9.0.0 -pydicom==2.4.3 -PySimpleGUI==4.60.5 -requests==2.31.0 -scikit-learn==1.3.0 -scipy==1.11.1 -seaborn==0.12.2 -tabulate==0.9.0 -tensorflow==2.10.1 -tensorflow-addons==0.22.0 -tensorflow-model-optimization==0.7.5 -tqdm==4.66.1 +absl-py==1.4.0 +adabelief-tf==0.2.1 +efficientnet==1.1.1 +gpu-control==1.0.0 +hyperas==0.4.1 +imbalanced-learn==0.11.0 +keras==2.10.0 +keras-adabound==0.6.0 +keras-efficientnet-v2==1.2.2 +keras-gradient-noise==0.11 +loguru==0.5.3 +matplotlib==3.7.2 +model-profiler==1.1.8 +numpy==1.25.1 +opencv-python==4.8.0.74 +pandas==2.0.3 +Pillow +psutil==5.9.5 +py-cpuinfo==9.0.0 +pydicom==2.4.3 +PySimpleGUI==4.60.5 +requests==2.31.0 +scikit-learn==1.3.0 +scipy==1.11.1 +seaborn==0.12.2 +tabulate==0.9.0 +tensorflow==2.10.1 +tensorflow-addons==0.22.0 +tensorflow-model-optimization==0.7.5 +tqdm==4.66.1 diff --git a/templates/DPL.txt b/templates/DPL.txt index 96f0078..e179ac5 100644 --- a/templates/DPL.txt +++ b/templates/DPL.txt @@ -1,163 +1,163 @@ -def save_images_to_dir(images, labels, dir_path): - # create the directory if it doesn't exist - if not os.path.exists(dir_path): - os.makedirs(dir_path) - # iterate over the images and labels - for i, (image, label) in enumerate(zip(images, labels)): - # get the class label - class_label = np.argmax(label) - # create the file path - file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') - # save the image to the file path - plt.imsave(file_path, image.squeeze(), cmap='gray') -# Create an ImageDataGenerator for the training set -train_datagen = ImageDataGenerator( - rescale=1./255, - horizontal_flip=False, - zoom_range = 0.1, - width_shift_range=0.1, - brightness_range=(0.95, 1.05), - height_shift_range=0.1 - ) -# Create an iterator for the training set -train_generator = train_datagen.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') - -# Create an ImageDataGenerator for the validation set -val_datagen = ImageDataGenerator( - rescale=1./255, - horizontal_flip=False, - zoom_range = 0.1, - width_shift_range=0.1, - brightness_range=(0.95, 1.05), - height_shift_range=0.1) - -# Create an iterator for the validation set -val_generator = val_datagen.flow_from_directory( - validation_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), - class_mode='binary') - -# Create an ImageDataGenerator for the test set -test_datagen = ImageDataGenerator( - rescale=1./255, - horizontal_flip=False, - zoom_range = 0.1, - width_shift_range=0.1, - brightness_range=(0.95, 1.05), - height_shift_range=0.1) - -# Create an iterator for the test set -test_generator = test_datagen.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), - class_mode='binary') - -# Load all images and labels into memory -x_train, y_train = next(iter(train_generator)) -x_val, y_val = next(iter(val_generator)) -x_test, y_test = next(iter(test_generator)) - -#GEN train data -for i in range(2): - augmented_train_generator_IDG = ImageDataGenerator( - rescale=1./255, - horizontal_flip=True, - rotation_range=180, - zoom_range = 0.4, - width_shift_range=0.4, - brightness_range=(0.3, 1.7), - height_shift_range=0.4 - ) - # Create an iterator for the training set - augmented_train_generator = augmented_train_generator_IDG.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') - x_train_augmented, y_train_augmented = augmented_train_generator.next() - - # Concatenate the original data with the augmented data - x_train = np.concatenate([x_train, x_train_augmented]) - y_train = np.concatenate([y_train, y_train_augmented]) -#GEN test data -for i in range(4): - augmented_test_generator_IDG = ImageDataGenerator( - rescale=1./255, - horizontal_flip=True, - rotation_range=180, - zoom_range = 0.3, - width_shift_range=0.3, - brightness_range=(0.6, 1.6), - height_shift_range=0.3 - ) - # Create an iterator for the training set - augmented_test_generator = augmented_test_generator_IDG.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') - x_test_augmented, y_test_augmented = augmented_test_generator.next() - - # Concatenate the original data with the augmented data - x_test = np.concatenate([x_test, x_test_augmented]) - y_test = np.concatenate([y_test, y_test_augmented]) -#to_categorical -y_train = to_categorical(y_train) -y_val = to_categorical(y_val) -y_test = to_categorical(y_test) - -#BUG augmented_datagen not working fix it -# # Create another ImageDataGenerator with the desired options -# augmented_datagen = ImageDataGenerator( -# horizontal_flip=False, -# zoom_range=0.2, -# rotation_range=20, -# brightness_range=(0.9, 1.1), -# width_shift_range=0.2, -# height_shift_range=0.2) - -# # Create an iterator for the training set -# augmented_train_generator = augmented_datagen.flow(x_train, y_train, batch_size=len(x_train)) - -# # Get the augmented data -# x_train_augmented, y_train_augmented = augmented_train_generator.next() - -# # Concatenate the original data with the augmented data -# x_train = np.concatenate([x_train, x_train_augmented]) -# y_train = np.concatenate([y_train, y_train_augmented]) -#BUG_End -# save_images_to_dir(x_train_augmented, y_train_augmented, 'test_TS_B') - -x_test = np.concatenate([x_test, x_val]) -y_test = np.concatenate([y_test, y_val]) - -# Shuffle the training data -combined = list(zip(x_train, y_train)) -shuffle(combined) -x_train, y_train = zip(*combined) - -# Shuffle the validation data -combined = list(zip(x_val, y_val)) -shuffle(combined) -x_val, y_val = zip(*combined) - -# Shuffle the test data -combined = list(zip(x_test, y_test)) -shuffle(combined) -x_test, y_test = zip(*combined) - -# Convert back to numpy arrays -x_train = np.array(x_train) -y_train = np.array(y_train) -x_val = np.array(x_val) -y_val = np.array(y_val) -x_test = np.array(x_test) -y_test = np.array(y_test) - +def save_images_to_dir(images, labels, dir_path): + # create the directory if it doesn't exist + if not os.path.exists(dir_path): + os.makedirs(dir_path) + # iterate over the images and labels + for i, (image, label) in enumerate(zip(images, labels)): + # get the class label + class_label = np.argmax(label) + # create the file path + file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') + # save the image to the file path + plt.imsave(file_path, image.squeeze(), cmap='gray') +# Create an ImageDataGenerator for the training set +train_datagen = ImageDataGenerator( + rescale=1./255, + horizontal_flip=False, + zoom_range = 0.1, + width_shift_range=0.1, + brightness_range=(0.95, 1.05), + height_shift_range=0.1 + ) +# Create an iterator for the training set +train_generator = train_datagen.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') + +# Create an ImageDataGenerator for the validation set +val_datagen = ImageDataGenerator( + rescale=1./255, + horizontal_flip=False, + zoom_range = 0.1, + width_shift_range=0.1, + brightness_range=(0.95, 1.05), + height_shift_range=0.1) + +# Create an iterator for the validation set +val_generator = val_datagen.flow_from_directory( + validation_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), + class_mode='binary') + +# Create an ImageDataGenerator for the test set +test_datagen = ImageDataGenerator( + rescale=1./255, + horizontal_flip=False, + zoom_range = 0.1, + width_shift_range=0.1, + brightness_range=(0.95, 1.05), + height_shift_range=0.1) + +# Create an iterator for the test set +test_generator = test_datagen.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), + class_mode='binary') + +# Load all images and labels into memory +x_train, y_train = next(iter(train_generator)) +x_val, y_val = next(iter(val_generator)) +x_test, y_test = next(iter(test_generator)) + +#GEN train data +for i in range(2): + augmented_train_generator_IDG = ImageDataGenerator( + rescale=1./255, + horizontal_flip=True, + rotation_range=180, + zoom_range = 0.4, + width_shift_range=0.4, + brightness_range=(0.3, 1.7), + height_shift_range=0.4 + ) + # Create an iterator for the training set + augmented_train_generator = augmented_train_generator_IDG.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') + x_train_augmented, y_train_augmented = augmented_train_generator.next() + + # Concatenate the original data with the augmented data + x_train = np.concatenate([x_train, x_train_augmented]) + y_train = np.concatenate([y_train, y_train_augmented]) +#GEN test data +for i in range(4): + augmented_test_generator_IDG = ImageDataGenerator( + rescale=1./255, + horizontal_flip=True, + rotation_range=180, + zoom_range = 0.3, + width_shift_range=0.3, + brightness_range=(0.6, 1.6), + height_shift_range=0.3 + ) + # Create an iterator for the training set + augmented_test_generator = augmented_test_generator_IDG.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') + x_test_augmented, y_test_augmented = augmented_test_generator.next() + + # Concatenate the original data with the augmented data + x_test = np.concatenate([x_test, x_test_augmented]) + y_test = np.concatenate([y_test, y_test_augmented]) +#to_categorical +y_train = to_categorical(y_train) +y_val = to_categorical(y_val) +y_test = to_categorical(y_test) + +#BUG augmented_datagen not working fix it +# # Create another ImageDataGenerator with the desired options +# augmented_datagen = ImageDataGenerator( +# horizontal_flip=False, +# zoom_range=0.2, +# rotation_range=20, +# brightness_range=(0.9, 1.1), +# width_shift_range=0.2, +# height_shift_range=0.2) + +# # Create an iterator for the training set +# augmented_train_generator = augmented_datagen.flow(x_train, y_train, batch_size=len(x_train)) + +# # Get the augmented data +# x_train_augmented, y_train_augmented = augmented_train_generator.next() + +# # Concatenate the original data with the augmented data +# x_train = np.concatenate([x_train, x_train_augmented]) +# y_train = np.concatenate([y_train, y_train_augmented]) +#BUG_End +# save_images_to_dir(x_train_augmented, y_train_augmented, 'test_TS_B') + +x_test = np.concatenate([x_test, x_val]) +y_test = np.concatenate([y_test, y_val]) + +# Shuffle the training data +combined = list(zip(x_train, y_train)) +shuffle(combined) +x_train, y_train = zip(*combined) + +# Shuffle the validation data +combined = list(zip(x_val, y_val)) +shuffle(combined) +x_val, y_val = zip(*combined) + +# Shuffle the test data +combined = list(zip(x_test, y_test)) +shuffle(combined) +x_test, y_test = zip(*combined) + +# Convert back to numpy arrays +x_train = np.array(x_train) +y_train = np.array(y_train) +x_val = np.array(x_val) +y_val = np.array(y_val) +x_test = np.array(x_test) +y_test = np.array(y_test) + #save_images_to_dir(x_train, y_train, 'test_TS') \ No newline at end of file diff --git a/templates/DPN.txt b/templates/DPN.txt index 83e5779..fbbd0c8 100644 --- a/templates/DPN.txt +++ b/templates/DPN.txt @@ -1,59 +1,59 @@ -def generate_data(directory, generator_options, augmentation_options, img_res, repeats, num_classes=2): - datagen = ImageDataGenerator(rescale=1./255, **generator_options) - generator = datagen.flow_from_directory( - directory, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(directory)]), - class_mode='categorical' if num_classes > 2 else 'binary', - color_mode='rgb' - ) - x_data, y_data = next(iter(generator)) - y_data = to_categorical(y_data, num_classes=num_classes) - - if augmentation_options is not None: - for _ in range(repeats): - augmented_datagen = ImageDataGenerator(rescale=1./255, **augmentation_options) - augmented_generator = augmented_datagen.flow_from_directory( - directory, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(directory)]), - class_mode='categorical' if num_classes > 2 else 'binary', - color_mode='rgb' - ) - x_augmented, y_augmented = augmented_generator.next() - y_augmented = to_categorical(y_augmented, num_classes=num_classes) - x_data = np.concatenate([x_data, x_augmented]) - y_data = np.concatenate([y_data, y_augmented]) - - return x_data, y_data - -train_options = {"horizontal_flip": False, - "zoom_range": 0.2, - "width_shift_range": 0.2, - "brightness_range": (0.95, 1.05), - "height_shift_range": 0.2} - -val_generator_options = {"horizontal_flip": False, - "zoom_range": 0.1, - "width_shift_range": 0.1, - "brightness_range": (0.95, 1.05), - "height_shift_range": 0.1} - -test_generator_options = {"horizontal_flip": False, - "zoom_range": 0.1, - "width_shift_range": 0.1, - "brightness_range": (0.95, 1.05), - "height_shift_range": 0.1} - -augmentation_options = {"horizontal_flip": True, - "rotation_range": 180, - "zoom_range": 0.4, - "width_shift_range": 0.4, - "brightness_range": (0.3, 1.7), - "height_shift_range": 0.4} - -x_val, y_val = generate_data(validation_dir, val_generator_options, None, img_res, 0) - -x_test, y_test = generate_data(test_dir, test_generator_options, None, img_res, 0) - -x_train, y_train = generate_data(train_dir, train_options, augmentation_options, img_res, 2) +def generate_data(directory, generator_options, augmentation_options, img_res, repeats, num_classes=2): + datagen = ImageDataGenerator(rescale=1./255, **generator_options) + generator = datagen.flow_from_directory( + directory, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(directory)]), + class_mode='categorical' if num_classes > 2 else 'binary', + color_mode='rgb' + ) + x_data, y_data = next(iter(generator)) + y_data = to_categorical(y_data, num_classes=num_classes) + + if augmentation_options is not None: + for _ in range(repeats): + augmented_datagen = ImageDataGenerator(rescale=1./255, **augmentation_options) + augmented_generator = augmented_datagen.flow_from_directory( + directory, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(directory)]), + class_mode='categorical' if num_classes > 2 else 'binary', + color_mode='rgb' + ) + x_augmented, y_augmented = augmented_generator.next() + y_augmented = to_categorical(y_augmented, num_classes=num_classes) + x_data = np.concatenate([x_data, x_augmented]) + y_data = np.concatenate([y_data, y_augmented]) + + return x_data, y_data + +train_options = {"horizontal_flip": False, + "zoom_range": 0.2, + "width_shift_range": 0.2, + "brightness_range": (0.95, 1.05), + "height_shift_range": 0.2} + +val_generator_options = {"horizontal_flip": False, + "zoom_range": 0.1, + "width_shift_range": 0.1, + "brightness_range": (0.95, 1.05), + "height_shift_range": 0.1} + +test_generator_options = {"horizontal_flip": False, + "zoom_range": 0.1, + "width_shift_range": 0.1, + "brightness_range": (0.95, 1.05), + "height_shift_range": 0.1} + +augmentation_options = {"horizontal_flip": True, + "rotation_range": 180, + "zoom_range": 0.4, + "width_shift_range": 0.4, + "brightness_range": (0.3, 1.7), + "height_shift_range": 0.4} + +x_val, y_val = generate_data(validation_dir, val_generator_options, None, img_res, 0) + +x_test, y_test = generate_data(test_dir, test_generator_options, None, img_res, 0) + +x_train, y_train = generate_data(train_dir, train_options, augmentation_options, img_res, 2) diff --git a/templates/DPU_NEW.txt b/templates/DPU_NEW.txt index fb51b2d..5e0a269 100644 --- a/templates/DPU_NEW.txt +++ b/templates/DPU_NEW.txt @@ -1,413 +1,413 @@ -#scale_data -def scale_data_NP(data): - if scale_data_NP_M: - data = data.astype('float32') - data = (data - 127.5) / 127.5 - return data - else: - return data / 255 -#add_image_grain -def add_image_grain(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -#adjust_brightness -# V1 -def adjust_brightness(images, target_average): - # Calculate the average pixel value of all the images - overall_average = np.mean(images) - - # Iterate over each image in the array - for i in range(len(images)): - # Calculate the average pixel value of the current image - image_average = np.mean(images[i]) - - # Compare the image average with the overall average - if image_average > overall_average + 10: - # Increase brightness by adding a constant value - images[i] = np.clip(images[i] - random.randint(6, 25), 0, 255) - elif image_average < overall_average - 10: - # Decrease brightness by subtracting a constant value - images[i] = np.clip(images[i] + random.randint(6, 25), 0, 255) - - return images -# V2 (Very slow NOT Recommended) -# def adjust_brightness(images, target_average): -# # Calculate the average pixel value of all the images -# overall_average = np.mean(images) - -# # Initialize a variable to keep track of the number of deleted images -# deleted_images = 0 - -# # Create a progress bar -# pbar = tqdm(total=len(images), desc='Processing images') - -# # Iterate over each image in the array -# for i in range(len(images)): -# # Adjust the index to account for deleted images -# adjusted_index = i - deleted_images - -# # Calculate the average pixel value of the current image -# image_average = np.mean(images[adjusted_index]) - -# # Compare the image average with the overall average -# if image_average > overall_average + 50 or image_average < overall_average - 60: -# # If the image brightness is 45 units higher than the overall average, delete the image -# images = np.delete(images, adjusted_index, axis=0) -# # Increment the count of deleted images -# deleted_images += 1 -# elif image_average > overall_average + 10: -# # Increase brightness by adding a random value between 6 and 25 -# images[adjusted_index] = np.clip(images[adjusted_index] - random.randint(6, 25), 0, 255) -# elif image_average < overall_average - 10: -# # Decrease brightness by subtracting a random value between 6 and 25 -# images[adjusted_index] = np.clip(images[adjusted_index] + random.randint(6, 25), 0, 255) - -# # Update the progress bar -# pbar.update(1) - -# # Close the progress bar -# pbar.close() - -# print(f'deleted_images: {deleted_images}') -# return images -#apply_clahe_rgb_array -def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): - # Create a CLAHE object - clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) - - # Iterate over each image in the array - for i in range(len(images)): - # Split the image into color channels - b, g, r = cv2.split(images[i]) - - # Convert the channels to the appropriate format - b = cv2.convertScaleAbs(b) - g = cv2.convertScaleAbs(g) - r = cv2.convertScaleAbs(r) - - # Apply adaptive histogram equalization to each channel - equalized_b = clahe.apply(b) - equalized_g = clahe.apply(g) - equalized_r = clahe.apply(r) - - # Merge the equalized channels back into an image - equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) - - # Replace the original image with the equalized image in the array - images[i] = equalized_image - - return images -#noise_func -def noise_func(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(-0.05, 0.05) - intensityL1 = random.uniform(-0.04, 0.04) - else: - intensityL2 = random.uniform(-0.07, 0.07) - intensityL1 = random.uniform(-0.05, 0.05) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.035) # Random intensity between 0 and 0.026 - new_image = add_image_grain(new_image, intensity=intensity) - return new_image -#shuffle_data -def shuffle_data(x, y): - indices = np.arange(x.shape[0]) - np.random.shuffle(indices) - x = x[indices] - y = y[indices] - return x, y -#save_images_to_dir -def save_images_to_dir(images, labels, dir_path): - # create the directory if it doesn't exist - if not os.path.exists(dir_path): - os.makedirs(dir_path) - # iterate over the images and labels - for i, (image, label) in enumerate(zip(images, labels)): - # get the class label - class_label = np.argmax(label) - # create the file path - file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') - # save the image to the file path - plt.imsave(file_path, image.squeeze()) - # compress the directory - shutil.make_archive(dir_path, 'gztar', dir_path) - # remove the original directory - shutil.rmtree(dir_path) -# Create an ImageDataGenerator for the training set -if OP_HDC: - print_Color('Using OP_HDC IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.86, 1.13), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=interpolation_order_IFG, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -else: - print_Color('Using Def IDG...', ['yellow']) - train_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.26, - shear_range=0.25, - width_shift_range=0.25, - brightness_range=(0.8, 1.15), - height_shift_range=0.25, - channel_shift_range=100, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False, - fill_mode='nearest', # constant - preprocessing_function=noise_func - ) -train_datagen_SM = ImageDataGenerator( - horizontal_flip=False, - vertical_flip=False, - rotation_range=20, - zoom_range=0.07, - shear_range=0.07, - width_shift_range=0.07, - brightness_range=(0.99, 1.01), - height_shift_range=0.07, - channel_shift_range=0, - featurewise_center=False, - interpolation_order=interpolation_order_IFG, - featurewise_std_normalization=False -) -# Create an iterator for the training set -train_generator_SM = train_datagen_SM.flow_from_directory( - train_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), - class_mode='binary') -# Create an ImageDataGenerator for the validation set (OP) -if Make_EV_DATA: - val_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the validation set - val_generator = val_datagen.flow_from_directory( - validation_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), - class_mode='binary', - color_mode='rgb') - - # Create an ImageDataGenerator for the test set - test_datagen = ImageDataGenerator( - horizontal_flip=False, - zoom_range = 0.01, - width_shift_range=0.01, - interpolation_order=interpolation_order_IFG, - height_shift_range=0.01) - - # Create an iterator for the test set - test_generator = test_datagen.flow_from_directory( - test_dir, - target_size=(img_res[0], img_res[1]), - batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), - class_mode='binary', - color_mode='rgb') -# Load all images and labels into memory -print_Color('Loading all images and labels into memory...', ['yellow']) -x_train, y_train = next(iter(train_generator_SM)) -if Make_EV_DATA: - x_val, y_val = next(iter(val_generator)) - x_test, y_test = next(iter(test_generator)) -# fit parameters from data -# train_datagen.fit(x_train) -#to_categorical (TEMP) -if categorical_IMP: - print_Color('Making categorical data...', ['yellow']) - y_train = to_categorical(y_train, num_classes=2) - if Make_EV_DATA: - y_val = to_categorical(y_val, num_classes=2) - y_test = to_categorical(y_test, num_classes=2) -# Use_SMOTE -if Use_SMOTE: - print_Color('SMOTE...', ['yellow']) - # Convert y_train from one-hot encoding to label encoding - y_train_label_encoded = np.argmax(y_train, axis=1) - - # Print the original label distribution - unique, counts = np.unique(y_train_label_encoded, return_counts=True) - print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) - - # Use SMOTE to oversample the minority class - smote = SMOTE(random_state=42) - x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) - - # Print the resampled label distribution - unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) - print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) - - # Reshape x_train_res back to the original x_train shape - x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) - - # Convert y_train_res from label encoding back to one-hot encoding - y_train_res = to_categorical(y_train_res_label_encoded) - - # Calculate the ratio of two labels after resampling - pneumonia_count = np.sum(y_train_res[:, 1]) - total_count = y_train_res.shape[0] - label_ratio_res = pneumonia_count / total_count - label_ratio_percentage_res = label_ratio_res * 100 - - # Replace the original data with the resampled data - x_train = x_train_res - y_train = y_train_res - - # Delete the resampled data to free up memory - del x_train_res, y_train_res_label_encoded, y_train_res -# Generating augmented data -print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', - ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], - advanced_mode=True) -if ADBD > 0: - for i in range(ADBD): - # ADB_clip_limit Scheduler>>> - if i == 0: - ADB_clip_limit = 1.2 - else: - #V1>>> - CL_SLM = 2.4 - ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) - # Try it in win graphing calculator copy and paste: - # β”Œ-------------┬--┬---------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ - # β””-------------β”΄--β”΄---------------β”˜ - #V2>>> - # CL_SLM_2 = 1.4 - # CL_SLM_Start_2 = 2 - # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) - # Try it in win graphing calculator copy and paste: - # β”Œ-----------------┬--┬-------------------┐ - # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ - # β””-----------------β”΄--β”΄-------------------β”˜ - print(f'> Generating ADB[{i+1}/{ADBD}]...') - # prepare an iterators to scale images - train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) - - # get augmented data - x_train_augmented, y_train_augmented = train_iterator.next() - print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') - print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') - x_train_augmented = np.clip(x_train_augmented, 0, 255) - #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) - x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss - print(f'> └───Adding the Generated ADB...') - # append augmented data to original data - x_train = np.concatenate([x_train, x_train_augmented]) - y_train = np.concatenate([y_train, y_train_augmented]) - #free up memory - del y_train_augmented - del x_train_augmented -# normalizing -print_Color('Normalizing image data...', ['yellow']) -if adjust_brightness_Mode: - x_train = adjust_brightness(x_train, np.mean(x_train)) -x_train = np.clip(x_train, 0, 255) -if RANGE_NOM: - x_train = scale_data_NP(x_train) -y_train = np.array(y_train) -if Make_EV_DATA: - x_test = np.clip(x_test, 0, 255) - x_val = np.clip(x_val, 0, 255) - if RANGE_NOM: - x_val = scale_data_NP(x_val) - y_val = np.array(y_val) - if RANGE_NOM: - x_test = scale_data_NP(x_test) - y_test = np.array(y_test) -# Check the data type of image data -print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) -# Check the range of image data -print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) -# Calculate the ratio of two labels -if categorical_IMP: - label_sums = np.sum(y_train, axis=0) - label_ratio = label_sums / (np.sum(y_train) + 1e-10) - label_ratio_percentage = label_ratio * 100 - print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', - ['normal', 'red', 'magenta', 'green'], advanced_mode=True) -print_Color('Setting LNTS...', ['yellow']) -# Get the total number of samples in the arrays -num_samples = x_train.shape[0] -print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -if LNTS != 0: - print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) - print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) - # Generate random indices to select LNTS samples - indices = np.random.choice(num_samples, size=LNTS, replace=False) - # Select the samples using the generated indices - x_selected = x_train[indices] - y_selected = y_train[indices] - x_train = x_selected - y_train = y_selected - #free up memory - del x_selected - del y_selected - del indices - #Debug - num_samples = x_train.shape[0] - print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) -# Shuffle the training data -print_Color('shuffling data...', ['yellow']) -x_train, y_train = shuffle_data(x_train, y_train) -#save_images_to_dir -if Save_TS: - print_Color('Saving TS...', ['yellow']) - SITD = np.random.choice(num_samples, size=400, replace=False) - S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') - print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) - if RANGE_NOM: - if scale_data_NP_M: - save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) - else: - save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) +#scale_data +def scale_data_NP(data): + if scale_data_NP_M: + data = data.astype('float32') + data = (data - 127.5) / 127.5 + return data + else: + return data / 255 +#add_image_grain +def add_image_grain(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +#adjust_brightness +# V1 +def adjust_brightness(images, target_average): + # Calculate the average pixel value of all the images + overall_average = np.mean(images) + + # Iterate over each image in the array + for i in range(len(images)): + # Calculate the average pixel value of the current image + image_average = np.mean(images[i]) + + # Compare the image average with the overall average + if image_average > overall_average + 10: + # Increase brightness by adding a constant value + images[i] = np.clip(images[i] - random.randint(6, 25), 0, 255) + elif image_average < overall_average - 10: + # Decrease brightness by subtracting a constant value + images[i] = np.clip(images[i] + random.randint(6, 25), 0, 255) + + return images +# V2 (Very slow NOT Recommended) +# def adjust_brightness(images, target_average): +# # Calculate the average pixel value of all the images +# overall_average = np.mean(images) + +# # Initialize a variable to keep track of the number of deleted images +# deleted_images = 0 + +# # Create a progress bar +# pbar = tqdm(total=len(images), desc='Processing images') + +# # Iterate over each image in the array +# for i in range(len(images)): +# # Adjust the index to account for deleted images +# adjusted_index = i - deleted_images + +# # Calculate the average pixel value of the current image +# image_average = np.mean(images[adjusted_index]) + +# # Compare the image average with the overall average +# if image_average > overall_average + 50 or image_average < overall_average - 60: +# # If the image brightness is 45 units higher than the overall average, delete the image +# images = np.delete(images, adjusted_index, axis=0) +# # Increment the count of deleted images +# deleted_images += 1 +# elif image_average > overall_average + 10: +# # Increase brightness by adding a random value between 6 and 25 +# images[adjusted_index] = np.clip(images[adjusted_index] - random.randint(6, 25), 0, 255) +# elif image_average < overall_average - 10: +# # Decrease brightness by subtracting a random value between 6 and 25 +# images[adjusted_index] = np.clip(images[adjusted_index] + random.randint(6, 25), 0, 255) + +# # Update the progress bar +# pbar.update(1) + +# # Close the progress bar +# pbar.close() + +# print(f'deleted_images: {deleted_images}') +# return images +#apply_clahe_rgb_array +def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): + # Create a CLAHE object + clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size) + + # Iterate over each image in the array + for i in range(len(images)): + # Split the image into color channels + b, g, r = cv2.split(images[i]) + + # Convert the channels to the appropriate format + b = cv2.convertScaleAbs(b) + g = cv2.convertScaleAbs(g) + r = cv2.convertScaleAbs(r) + + # Apply adaptive histogram equalization to each channel + equalized_b = clahe.apply(b) + equalized_g = clahe.apply(g) + equalized_r = clahe.apply(r) + + # Merge the equalized channels back into an image + equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r)) + + # Replace the original image with the equalized image in the array + images[i] = equalized_image + + return images +#noise_func +def noise_func(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(-0.05, 0.05) + intensityL1 = random.uniform(-0.04, 0.04) + else: + intensityL2 = random.uniform(-0.07, 0.07) + intensityL1 = random.uniform(-0.05, 0.05) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.035) # Random intensity between 0 and 0.026 + new_image = add_image_grain(new_image, intensity=intensity) + return new_image +#shuffle_data +def shuffle_data(x, y): + indices = np.arange(x.shape[0]) + np.random.shuffle(indices) + x = x[indices] + y = y[indices] + return x, y +#save_images_to_dir +def save_images_to_dir(images, labels, dir_path): + # create the directory if it doesn't exist + if not os.path.exists(dir_path): + os.makedirs(dir_path) + # iterate over the images and labels + for i, (image, label) in enumerate(zip(images, labels)): + # get the class label + class_label = np.argmax(label) + # create the file path + file_path = os.path.join(dir_path, f'image_{i}_class_{class_label}.png') + # save the image to the file path + plt.imsave(file_path, image.squeeze()) + # compress the directory + shutil.make_archive(dir_path, 'gztar', dir_path) + # remove the original directory + shutil.rmtree(dir_path) +# Create an ImageDataGenerator for the training set +if OP_HDC: + print_Color('Using OP_HDC IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.86, 1.13), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=interpolation_order_IFG, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +else: + print_Color('Using Def IDG...', ['yellow']) + train_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.26, + shear_range=0.25, + width_shift_range=0.25, + brightness_range=(0.8, 1.15), + height_shift_range=0.25, + channel_shift_range=100, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False, + fill_mode='nearest', # constant + preprocessing_function=noise_func + ) +train_datagen_SM = ImageDataGenerator( + horizontal_flip=False, + vertical_flip=False, + rotation_range=20, + zoom_range=0.07, + shear_range=0.07, + width_shift_range=0.07, + brightness_range=(0.99, 1.01), + height_shift_range=0.07, + channel_shift_range=0, + featurewise_center=False, + interpolation_order=interpolation_order_IFG, + featurewise_std_normalization=False +) +# Create an iterator for the training set +train_generator_SM = train_datagen_SM.flow_from_directory( + train_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(train_dir)]), + class_mode='binary') +# Create an ImageDataGenerator for the validation set (OP) +if Make_EV_DATA: + val_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the validation set + val_generator = val_datagen.flow_from_directory( + validation_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(validation_dir)]), + class_mode='binary', + color_mode='rgb') + + # Create an ImageDataGenerator for the test set + test_datagen = ImageDataGenerator( + horizontal_flip=False, + zoom_range = 0.01, + width_shift_range=0.01, + interpolation_order=interpolation_order_IFG, + height_shift_range=0.01) + + # Create an iterator for the test set + test_generator = test_datagen.flow_from_directory( + test_dir, + target_size=(img_res[0], img_res[1]), + batch_size=sum([len(files) for r, d, files in os.walk(test_dir)]), + class_mode='binary', + color_mode='rgb') +# Load all images and labels into memory +print_Color('Loading all images and labels into memory...', ['yellow']) +x_train, y_train = next(iter(train_generator_SM)) +if Make_EV_DATA: + x_val, y_val = next(iter(val_generator)) + x_test, y_test = next(iter(test_generator)) +# fit parameters from data +# train_datagen.fit(x_train) +#to_categorical (TEMP) +if categorical_IMP: + print_Color('Making categorical data...', ['yellow']) + y_train = to_categorical(y_train, num_classes=2) + if Make_EV_DATA: + y_val = to_categorical(y_val, num_classes=2) + y_test = to_categorical(y_test, num_classes=2) +# Use_SMOTE +if Use_SMOTE: + print_Color('SMOTE...', ['yellow']) + # Convert y_train from one-hot encoding to label encoding + y_train_label_encoded = np.argmax(y_train, axis=1) + + # Print the original label distribution + unique, counts = np.unique(y_train_label_encoded, return_counts=True) + print_Color(f'~*- Original label distribution: ~*{dict(zip(unique, counts))}', ['normal', 'blue'], advanced_mode=True) + + # Use SMOTE to oversample the minority class + smote = SMOTE(random_state=42) + x_train_res, y_train_res_label_encoded = smote.fit_resample(x_train.reshape(x_train.shape[0], -1), y_train_label_encoded) + + # Print the resampled label distribution + unique_res, counts_res = np.unique(y_train_res_label_encoded, return_counts=True) + print_Color(f'~*- Resampled label distribution: ~*{dict(zip(unique_res, counts_res))}', ['normal', 'blue'], advanced_mode=True) + + # Reshape x_train_res back to the original x_train shape + x_train_res = x_train_res.reshape(-1, x_train.shape[1], x_train.shape[2], x_train.shape[3]) + + # Convert y_train_res from label encoding back to one-hot encoding + y_train_res = to_categorical(y_train_res_label_encoded) + + # Calculate the ratio of two labels after resampling + pneumonia_count = np.sum(y_train_res[:, 1]) + total_count = y_train_res.shape[0] + label_ratio_res = pneumonia_count / total_count + label_ratio_percentage_res = label_ratio_res * 100 + + # Replace the original data with the resampled data + x_train = x_train_res + y_train = y_train_res + + # Delete the resampled data to free up memory + del x_train_res, y_train_res_label_encoded, y_train_res +# Generating augmented data +print_Color(f'~*Generating augmented data ~*[~*ADBD: ~*{str(ADBD)}~*]~*...', + ['yellow', 'cyan', 'green', 'red', 'cyan', 'yellow'], + advanced_mode=True) +if ADBD > 0: + for i in range(ADBD): + # ADB_clip_limit Scheduler>>> + if i == 0: + ADB_clip_limit = 1.2 + else: + #V1>>> + CL_SLM = 2.4 + ADB_clip_limit = max(2 / (i + 1)**CL_SLM, 0.05) + # Try it in win graphing calculator copy and paste: + # β”Œ-------------┬--┬---------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^𝑧 β”œOR─ 𝑦=2/(π‘₯+1)^2.4 β”‚ + # β””-------------β”΄--β”΄---------------β”˜ + #V2>>> + # CL_SLM_2 = 1.4 + # CL_SLM_Start_2 = 2 + # ADB_clip_limit = CL_SLM_Start_2/(i+1)**(i+CL_SLM_2) + # Try it in win graphing calculator copy and paste: + # β”Œ-----------------┬--┬-------------------┐ + # β”‚ 𝑦=2/(π‘₯+1)^(π‘₯+𝑉) β”œOR─ 𝑦=2/(π‘₯+1)^(π‘₯+1.4) β”‚ + # β””-----------------β”΄--β”΄-------------------β”˜ + print(f'> Generating ADB[{i+1}/{ADBD}]...') + # prepare an iterators to scale images + train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train)) + + # get augmented data + x_train_augmented, y_train_augmented = train_iterator.next() + print(f'> β”œβ”€β”€β”€Applying adaptive histogram equalization...') + print(f'> β”œβ”€β”€β”€Adaptive histogram equalization clip limit = {round(ADB_clip_limit, 2)}') + x_train_augmented = np.clip(x_train_augmented, 0, 255) + #print_Color(f'~*> |---Grayscale range: ~*Min = {np.min(x_train_augmented)}~* | ~*Max = {np.max(x_train_augmented)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) + x_train_augmented = apply_clahe_rgb_array(x_train_augmented, clip_limit=ADB_clip_limit) # compensating the image info loss + print(f'> └───Adding the Generated ADB...') + # append augmented data to original data + x_train = np.concatenate([x_train, x_train_augmented]) + y_train = np.concatenate([y_train, y_train_augmented]) + #free up memory + del y_train_augmented + del x_train_augmented +# normalizing +print_Color('Normalizing image data...', ['yellow']) +if adjust_brightness_Mode: + x_train = adjust_brightness(x_train, np.mean(x_train)) +x_train = np.clip(x_train, 0, 255) +if RANGE_NOM: + x_train = scale_data_NP(x_train) +y_train = np.array(y_train) +if Make_EV_DATA: + x_test = np.clip(x_test, 0, 255) + x_val = np.clip(x_val, 0, 255) + if RANGE_NOM: + x_val = scale_data_NP(x_val) + y_val = np.array(y_val) + if RANGE_NOM: + x_test = scale_data_NP(x_test) + y_test = np.array(y_test) +# Check the data type of image data +print_Color(f'~*Data type: ~*{x_train.dtype}', ['normal', 'green'], advanced_mode=True) +# Check the range of image data +print_Color(f'~*RGB Range: ~*Min = {np.min(x_train)}~* | ~*Max = {np.max(x_train)}', ['normal', 'blue', 'normal', 'red'], advanced_mode=True) +# Calculate the ratio of two labels +if categorical_IMP: + label_sums = np.sum(y_train, axis=0) + label_ratio = label_sums / (np.sum(y_train) + 1e-10) + label_ratio_percentage = label_ratio * 100 + print_Color(f'~*Label ratio: ~*{100 - label_ratio_percentage[0]:.2f}% PNEUMONIA ~*| ~*{label_ratio_percentage[0]:.2f}% NORMAL', + ['normal', 'red', 'magenta', 'green'], advanced_mode=True) +print_Color('Setting LNTS...', ['yellow']) +# Get the total number of samples in the arrays +num_samples = x_train.shape[0] +print_Color(f'~*Original num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +if LNTS != 0: + print_Color(f'~*Applying LNTS of: ~*{LNTS}', ['normal', 'green'], advanced_mode=True) + print_Color(f'~*SNC: ~*{num_samples - LNTS}', ['normal', 'green'], advanced_mode=True) + # Generate random indices to select LNTS samples + indices = np.random.choice(num_samples, size=LNTS, replace=False) + # Select the samples using the generated indices + x_selected = x_train[indices] + y_selected = y_train[indices] + x_train = x_selected + y_train = y_selected + #free up memory + del x_selected + del y_selected + del indices + #Debug + num_samples = x_train.shape[0] + print_Color(f'~*New num_samples: ~*{num_samples}', ['normal', 'green'], advanced_mode=True) +# Shuffle the training data +print_Color('shuffling data...', ['yellow']) +x_train, y_train = shuffle_data(x_train, y_train) +#save_images_to_dir +if Save_TS: + print_Color('Saving TS...', ['yellow']) + SITD = np.random.choice(num_samples, size=400, replace=False) + S_dir = 'Samples/TSR400_' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') + print_Color(f'~*Sample dir: ~*{S_dir}', ['normal', 'green'], advanced_mode=True) + if RANGE_NOM: + if scale_data_NP_M: + save_images_to_dir((x_train[SITD] + 1) / 2.0, y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD], y_train[SITD], S_dir) + else: + save_images_to_dir(x_train[SITD] / 255, y_train[SITD], S_dir) print_Color('Done.', ['green']) \ No newline at end of file diff --git a/templates/MSN.txt b/templates/MSN.txt index 2452e2e..a5d54d9 100644 --- a/templates/MSN.txt +++ b/templates/MSN.txt @@ -1,24 +1,24 @@ -from keras.models import Model -from keras.layers import Dense, Flatten -from keras.regularizers import l2 -from keras.applications.vgg16 import VGG16 - -# Make sure to add the input_shape parameter in the VGG16 call. -vgg = VGG16(include_top=False, input_shape=[img_res[0], img_res[1], img_res[2]]) - -# retrieve output from last layer in the vgg model -flat1 = Flatten()(vgg.outputs[0]) -fc1 = Dense(2024, activation='relu', name='fc1', kernel_regularizer=l2(0.1))(flat1) -norm1 = BatchNormalization()(fc1) -fc2 = Dense(1024, activation='relu', name='fc2', kernel_regularizer=l2(0.1))(norm1) -norm2 = BatchNormalization()(fc2) -class1 = Dense(2, activation='softmax', name='predictions')(norm2) - -# define new model -model = Model(inputs=vgg.inputs, outputs=class1) - -# compile model -opt = SGD(learning_rate=0.01, momentum=0.9) -model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy', 'binary_accuracy']) -model.predict() +from keras.models import Model +from keras.layers import Dense, Flatten +from keras.regularizers import l2 +from keras.applications.vgg16 import VGG16 + +# Make sure to add the input_shape parameter in the VGG16 call. +vgg = VGG16(include_top=False, input_shape=[img_res[0], img_res[1], img_res[2]]) + +# retrieve output from last layer in the vgg model +flat1 = Flatten()(vgg.outputs[0]) +fc1 = Dense(2024, activation='relu', name='fc1', kernel_regularizer=l2(0.1))(flat1) +norm1 = BatchNormalization()(fc1) +fc2 = Dense(1024, activation='relu', name='fc2', kernel_regularizer=l2(0.1))(norm1) +norm2 = BatchNormalization()(fc2) +class1 = Dense(2, activation='softmax', name='predictions')(norm2) + +# define new model +model = Model(inputs=vgg.inputs, outputs=class1) + +# compile model +opt = SGD(learning_rate=0.01, momentum=0.9) +model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy', 'binary_accuracy']) +model.predict() model.summary() \ No newline at end of file diff --git a/templates/TRAIN_BC.txt b/templates/TRAIN_BC.txt index 92e5cfc..785999b 100644 --- a/templates/TRAIN_BC.txt +++ b/templates/TRAIN_BC.txt @@ -1,189 +1,189 @@ -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -#CONF -WTD_augmentation = True -Conf_batch_size = 4 -Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) -#TensorBoard conf -TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) -# Learning rate configuration -Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr -OneCycleLr_MAXLR = 0.0174 -# First time -if Learning_rate_conf == 1: - learning_rate_start = 8e-04 - learning_rate_max = 5e-03 - learning_rate_min = 5e-05 - learning_rate_rampup_epochs = 5 - learning_rate_sustain_epochs = 1 - learning_rate_exp_decay = .3 - #TEMP - # learning_rate_start = 8e-04 - # learning_rate_max = 1e-02 - # learning_rate_min = 8e-04 - # learning_rate_rampup_epochs = 5 - # learning_rate_sustain_epochs = 3 - # learning_rate_exp_decay = .45 -# 2th time -if Learning_rate_conf == 2: - if Learning_rate_conf_SET2C == 1: - learning_rate_start = 4.10e-06 - learning_rate_max = 4.10e-06 - learning_rate_min = 4.10e-06 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 2: - learning_rate_start = 4e-07 - learning_rate_max = 4e-07 - learning_rate_min = 4e-07 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 - - elif Learning_rate_conf_SET2C == 3: - learning_rate_start = 5e-04 - learning_rate_max = 5e-04 - learning_rate_min = 5e-04 - learning_rate_rampup_epochs = 0 - learning_rate_sustain_epochs = 0 - learning_rate_exp_decay = .1 -# Function to build learning rate schedule -if Learning_rate_conf in [1,2]: - def build_learning_rate_fn(lr_start=learning_rate_start, - lr_max=learning_rate_max, - lr_min=learning_rate_min, - lr_rampup_epochs=learning_rate_rampup_epochs, - lr_sustain_epochs=learning_rate_sustain_epochs, - lr_exp_decay=learning_rate_exp_decay): - lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync - def learning_rate_fn(epoch): - if epoch < lr_rampup_epochs: - lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start - elif epoch < lr_rampup_epochs + lr_sustain_epochs: - lr = lr_max - else: - lr = (lr_max - lr_min) *\ - lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min - return lr - return learning_rate_fn -#WTD_augmentation -if WTD_augmentation: - print_Color('Using WTD_augmentation...', ['yellow']) - def TF_add_image_grain(image, intensity = 0.01): - # Generate random noise array in the range [0, 1] - noise = tf.random.uniform(shape=tf.shape(image), minval=0, maxval=1, dtype=tf.float32) - - # Scale the noise array - scaled_noise = noise * intensity - - # Add the noise to the image - noisy_image = tf.math.add(image, scaled_noise) - - # Clip - if RANGE_NOM: - noisy_image = tf.clip_by_value(noisy_image, -1.0, 1.0) - else: - noisy_image = tf.clip_by_value(noisy_image, 0.0, 255.0) - - return noisy_image - # Function to augment images - def augment_images(image, label): - image = tf.image.random_flip_left_right(image) - image = tf.image.random_flip_up_down(image) - image = tf.image.random_contrast(image, 0.2, 1.8) - image = tf.image.random_brightness(image, max_delta=0.3) - # Random intensity between 0 and 0.04 - intensity = random.uniform(0, 0.04) - image = TF_add_image_grain(image, intensity=intensity) - # Add random rotation - # image = tf.image.rot90(image, k=random.randint(0, 3)) - return image, label - - # Create TensorFlow dataset - AUTO = tf.data.experimental.AUTOTUNE - train_dataset = ( - tf.data.Dataset.from_tensor_slices((x_train, y_train)) - .map(augment_images, num_parallel_calls=AUTO) - .repeat() - .shuffle(2048) - .batch(Conf_batch_size) - .prefetch(AUTO) - ) - -# Calculate steps per epoch -steps_per_epoch_train = len(x_train) // Conf_batch_size - -# Set up callbacks -class EpochEndMON(tf.keras.callbacks.Callback): - def on_epoch_end(self, epoch, logs=None): - optimizer = self.model.optimizer - if hasattr(optimizer, 'lr'): - lr = tf.keras.backend.get_value(optimizer.lr) - print(f'\nLearning rate for epoch {epoch+1} is {lr}') - if hasattr(optimizer, 'momentum'): - momentum = tf.keras.backend.get_value(optimizer.momentum) - print(f'Momentum for epoch {epoch+1} is {momentum}') - if logs: - val_loss = logs.get('val_loss') - val_acc = logs.get('val_accuracy') - print(f'Validation loss for epoch {epoch+1} is {val_loss}') - print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') - - print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') - -# Instantiate the callback -EpochEndMON_callback = EpochEndMON() -if Learning_rate_conf in [1,2]: - learning_rate_fn = build_learning_rate_fn() - learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) -else: - learning_rate_schedule = OneCycleLr(max_lr=OneCycleLr_MAXLR, steps_per_epoch=steps_per_epoch_train, epochs=20) -if SAVE_TYPE == 'TF': - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -else: - checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) - checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) -early_stopping = EarlyStopping(monitor='val_accuracy', patience=8, verbose=1, restore_best_weights=True) -log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') -TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' -tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq) - -# Train the model -print('Log dir:', log_dir) -#MInfo -print("Input Shape:", model.input_shape) -print("Output Shape:", model.output_shape) -print("Loss Function:", model.loss) -print('Training the model...\n') -if WTD_augmentation: - history = model.fit(train_dataset, - epochs=256, - steps_per_epoch=steps_per_epoch_train, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) -else: - history = model.fit(x_train, - y_train, - epochs=256, - batch_size=Conf_batch_size, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[early_stopping, - tensorboard_callback, - learning_rate_schedule, - checkpoint_BVAC, - checkpoint_BVL, - EpochEndMON_callback]) +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +#CONF +WTD_augmentation = True +Conf_batch_size = 4 +Learning_rate_conf = 3 # 1 and 2 for custom learning_rate_fn and 3 for OneCycleLr (Better for full training) +#TensorBoard conf +TensorBoard_UF = 1 # 1 for Slow 2 for fast (very slow tarining) +# Learning rate configuration +Learning_rate_conf_SET2C = 3 # 1 for SGD and 2 for Adam and... for lower lr 3 for very high lr +OneCycleLr_MAXLR = 0.0174 +# First time +if Learning_rate_conf == 1: + learning_rate_start = 8e-04 + learning_rate_max = 5e-03 + learning_rate_min = 5e-05 + learning_rate_rampup_epochs = 5 + learning_rate_sustain_epochs = 1 + learning_rate_exp_decay = .3 + #TEMP + # learning_rate_start = 8e-04 + # learning_rate_max = 1e-02 + # learning_rate_min = 8e-04 + # learning_rate_rampup_epochs = 5 + # learning_rate_sustain_epochs = 3 + # learning_rate_exp_decay = .45 +# 2th time +if Learning_rate_conf == 2: + if Learning_rate_conf_SET2C == 1: + learning_rate_start = 4.10e-06 + learning_rate_max = 4.10e-06 + learning_rate_min = 4.10e-06 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 2: + learning_rate_start = 4e-07 + learning_rate_max = 4e-07 + learning_rate_min = 4e-07 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 + + elif Learning_rate_conf_SET2C == 3: + learning_rate_start = 5e-04 + learning_rate_max = 5e-04 + learning_rate_min = 5e-04 + learning_rate_rampup_epochs = 0 + learning_rate_sustain_epochs = 0 + learning_rate_exp_decay = .1 +# Function to build learning rate schedule +if Learning_rate_conf in [1,2]: + def build_learning_rate_fn(lr_start=learning_rate_start, + lr_max=learning_rate_max, + lr_min=learning_rate_min, + lr_rampup_epochs=learning_rate_rampup_epochs, + lr_sustain_epochs=learning_rate_sustain_epochs, + lr_exp_decay=learning_rate_exp_decay): + lr_max = lr_max * tf.distribute.get_strategy().num_replicas_in_sync + def learning_rate_fn(epoch): + if epoch < lr_rampup_epochs: + lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start + elif epoch < lr_rampup_epochs + lr_sustain_epochs: + lr = lr_max + else: + lr = (lr_max - lr_min) *\ + lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min + return lr + return learning_rate_fn +#WTD_augmentation +if WTD_augmentation: + print_Color('Using WTD_augmentation...', ['yellow']) + def TF_add_image_grain(image, intensity = 0.01): + # Generate random noise array in the range [0, 1] + noise = tf.random.uniform(shape=tf.shape(image), minval=0, maxval=1, dtype=tf.float32) + + # Scale the noise array + scaled_noise = noise * intensity + + # Add the noise to the image + noisy_image = tf.math.add(image, scaled_noise) + + # Clip + if RANGE_NOM: + noisy_image = tf.clip_by_value(noisy_image, -1.0, 1.0) + else: + noisy_image = tf.clip_by_value(noisy_image, 0.0, 255.0) + + return noisy_image + # Function to augment images + def augment_images(image, label): + image = tf.image.random_flip_left_right(image) + image = tf.image.random_flip_up_down(image) + image = tf.image.random_contrast(image, 0.2, 1.8) + image = tf.image.random_brightness(image, max_delta=0.3) + # Random intensity between 0 and 0.04 + intensity = random.uniform(0, 0.04) + image = TF_add_image_grain(image, intensity=intensity) + # Add random rotation + # image = tf.image.rot90(image, k=random.randint(0, 3)) + return image, label + + # Create TensorFlow dataset + AUTO = tf.data.experimental.AUTOTUNE + train_dataset = ( + tf.data.Dataset.from_tensor_slices((x_train, y_train)) + .map(augment_images, num_parallel_calls=AUTO) + .repeat() + .shuffle(2048) + .batch(Conf_batch_size) + .prefetch(AUTO) + ) + +# Calculate steps per epoch +steps_per_epoch_train = len(x_train) // Conf_batch_size + +# Set up callbacks +class EpochEndMON(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs=None): + optimizer = self.model.optimizer + if hasattr(optimizer, 'lr'): + lr = tf.keras.backend.get_value(optimizer.lr) + print(f'\nLearning rate for epoch {epoch+1} is {lr}') + if hasattr(optimizer, 'momentum'): + momentum = tf.keras.backend.get_value(optimizer.momentum) + print(f'Momentum for epoch {epoch+1} is {momentum}') + if logs: + val_loss = logs.get('val_loss') + val_acc = logs.get('val_accuracy') + print(f'Validation loss for epoch {epoch+1} is {val_loss}') + print(f'Validation accuracy for epoch {epoch+1} is {val_acc}') + + print_Color_V2(f'`red` `green`PBE↓', start_char='`', end_char='`') + +# Instantiate the callback +EpochEndMON_callback = EpochEndMON() +if Learning_rate_conf in [1,2]: + learning_rate_fn = build_learning_rate_fn() + learning_rate_schedule = LearningRateScheduler(learning_rate_fn, verbose=1) +else: + learning_rate_schedule = OneCycleLr(max_lr=OneCycleLr_MAXLR, steps_per_epoch=steps_per_epoch_train, epochs=20) +if SAVE_TYPE == 'TF': + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +else: + checkpoint_BVAC = ModelCheckpoint('models\\Temp\\bestVAC_model.h5', monitor='val_accuracy', mode='max', save_best_only=True, verbose=1) + checkpoint_BVL = ModelCheckpoint('models\\Temp\\bestVL_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) +early_stopping = EarlyStopping(monitor='val_accuracy', patience=8, verbose=1, restore_best_weights=True) +log_dir = 'logs/fit/' + datetime.datetime.now().strftime('y%Y_m%m_d%d-h%H_m%M_s%S') +TensorBoard_update_freq = 'batch' if TensorBoard_UF == 2 else 'epoch' +tensorboard_callback = TensorBoard(log_dir=log_dir, write_images=True, histogram_freq=1, update_freq=TensorBoard_update_freq) + +# Train the model +print('Log dir:', log_dir) +#MInfo +print("Input Shape:", model.input_shape) +print("Output Shape:", model.output_shape) +print("Loss Function:", model.loss) +print('Training the model...\n') +if WTD_augmentation: + history = model.fit(train_dataset, + epochs=256, + steps_per_epoch=steps_per_epoch_train, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) +else: + history = model.fit(x_train, + y_train, + epochs=256, + batch_size=Conf_batch_size, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[early_stopping, + tensorboard_callback, + learning_rate_schedule, + checkpoint_BVAC, + checkpoint_BVL, + EpochEndMON_callback]) print('Training done.\n') \ No newline at end of file diff --git a/templates/TRAIN_NEW_BC.txt b/templates/TRAIN_NEW_BC.txt index 0674dc9..48df89d 100644 --- a/templates/TRAIN_NEW_BC.txt +++ b/templates/TRAIN_NEW_BC.txt @@ -1,162 +1,162 @@ -import gc -# Garbage Collection (memory) -gc.collect() -tf.keras.backend.clear_session() -# CONF -max_epoch = 256 # 128 for small models 256 for full Fine tuning and big models -subset_epoch = 8 # change it if you are using a combined model | DEF=6 / COMM=8 | Too little can result the model not Learn the patterns and too much makes the model overfit on that subset and perform badly on the next subset -subset_epoch_FT = 5 -PL_epoch = 18 # 16 for small models and >=24 for big models -subset_size = 1024 -Conf_batch_size_REV2 = 8 -OneCycleLr_MAXLR = 0.01 -OneCycleLr_DEC_A = 0.0005 -OneCycleLr_MINLR = 0.0055 -Use_ES_ONSUBT = False -EarlyStopping_P = 5 -BEST_RSN = 'PAI_model_T' -#VAR -OneCycleLr_CUNLR = OneCycleLr_MAXLR -all_histories = [] -best_acc = 0 -#Funcs -def add_image_grain_TRLRev2(image, intensity = 0.01): - # Generate random noise array - noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) - - # Scale the noise array - scaled_noise = (noise * intensity).astype(np.float32) - # Add the noise to the image - noisy_image = cv2.add(image, scaled_noise) - - return noisy_image -def noise_func_TRLRev2(image): - noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) - new_image = np.copy(image) - - if noise_type == 'L3': - intensityL2 = random.uniform(0.001, 0.016) - intensityL1 = random.uniform(0.005, 0.020) - else: - intensityL2 = random.uniform(0.001, 0.027) - intensityL1 = random.uniform(0.001, 0.028) - - block_size_L1 = random.randint(16, 32) - block_size_L2 = random.randint(32, 64) - - if noise_type == 'L2' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L2): - for j in range(0, image.shape[1], block_size_L2): - block = image[i:i+block_size_L2, j:j+block_size_L2] - block = (np.random.rand() * intensityL2 + 1) * block - new_image[i:i+block_size_L2, j:j+block_size_L2] = block - image = new_image - - if noise_type == 'L1' or noise_type == 'L3': - for i in range(0, image.shape[0], block_size_L1): - for j in range(0, image.shape[1], block_size_L1): - block = image[i:i+block_size_L1, j:j+block_size_L1] - block = (np.random.rand() * intensityL1 + 1) * block - new_image[i:i+block_size_L1, j:j+block_size_L1] = block - - if add_img_grain: - intensity = random.uniform(0, 0.022) # Random intensity - new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) - return new_image -#CONST -train_SUB_datagen = ImageDataGenerator( - horizontal_flip=True, - vertical_flip=True, - rotation_range=179, - zoom_range=0.24, - shear_range=0.22, - width_shift_range=0.21, - brightness_range=(0.88, 1.12), - height_shift_range=0.21, - channel_shift_range=100, - featurewise_center=False, - featurewise_std_normalization=False, - interpolation_order=2, - fill_mode='nearest', - preprocessing_function=noise_func_TRLRev2 - ) -steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 -early_stopping = EarlyStopping(monitor='val_accuracy', patience=EarlyStopping_P, verbose=1, restore_best_weights=True, mode='max') -#MAIN -print('Training the model...') -for epoch in range(1, max_epoch): - # Start Epoch - STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' - C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT - start_FULL_time = time.time() - print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch}~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'magenta', 'green'], advanced_mode=True) - print_Color(f'~*Setting model subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - # DP - print_Color('Shuffling data...', ['yellow']) - x_train, y_train = shuffle_data(x_train, y_train) - print_Color(f'~*Taking a subset of ~*[{subset_size}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) - x_SUB_train = x_train[subset_indices] - y_SUB_train = y_train[subset_indices] - print_Color('Augmenting data...', ['yellow']) - train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, y_SUB_train, shuffle=False, batch_size=len(x_SUB_train)).next() - x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) / 255 - y_SUB_train = train_SUB_augmented_images[1] - # learning_rate_schedule_SUB - if epoch > PL_epoch and OneCycleLr_CUNLR > OneCycleLr_MINLR: - OneCycleLr_CUNLR -= OneCycleLr_DEC_A - - learning_rate_schedule_SUB = OneCycleLr(max_lr=OneCycleLr_CUNLR, steps_per_epoch=steps_per_epoch_train_SUB, epochs=C_subset_epoch) - print_Color(f'~*Setting model OneCycleLr::maxlr to ~*[{OneCycleLr_CUNLR:.6f}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) - # Train - print_Color('Training on subset...', ['green']) - start_SUBO_time = time.time() - SUB_history = model.fit(x_SUB_train, - y_SUB_train, - epochs=C_subset_epoch, - batch_size=Conf_batch_size_REV2, - validation_data=(x_test, y_test), - verbose='auto', - callbacks=[learning_rate_schedule_SUB, early_stopping] if Use_ES_ONSUBT else [learning_rate_schedule_SUB] - ) - end_SUBO_time = time.time() - print_Color('Subset training done.', ['green']) - all_histories.append(SUB_history.history) - # Evaluate the model on the test data - evaluation = model.evaluate(x_test, y_test, verbose=0) - - # Extract the loss and accuracy from the evaluation results - loss = evaluation[0] - acc = evaluation[1] - - # If the accuracy is higher than the best_acc - if acc > best_acc: - print("Improved model accuracy from {} to {}. Saving model.".format(best_acc, acc)) - - # Update the best_acc - best_acc = acc - - # Save the model - if SAVE_TYPE == 'TF': - print('Saving full model tf format...') - model.save(BEST_RSN, save_format='tf') - else: - model.save(f'{BEST_RSN}.h5') - else: - print("Model accuracy did not improve from {}. Not saving model.".format(best_acc)) - # Garbage Collection (memory) - gc.collect() - tf.keras.backend.clear_session() - # Epoch end - end_time = time.time() - epoch_time = end_time - start_FULL_time - print(f"Time taken for epoch(FULL) {epoch}: {epoch_time:.2f} sec") - epoch_SUB_time = end_SUBO_time - start_SUBO_time - print(f"Time taken for epoch(SUBo) {epoch}: {epoch_SUB_time:.2f} sec") - print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) -# End -history = {} -for key in all_histories[0].keys(): - # For each metric, concatenate the values from all histories - history[key] = np.concatenate([h[key] for h in all_histories]) +import gc +# Garbage Collection (memory) +gc.collect() +tf.keras.backend.clear_session() +# CONF +max_epoch = 256 # 128 for small models 256 for full Fine tuning and big models +subset_epoch = 8 # change it if you are using a combined model | DEF=6 / COMM=8 | Too little can result the model not Learn the patterns and too much makes the model overfit on that subset and perform badly on the next subset +subset_epoch_FT = 5 +PL_epoch = 18 # 16 for small models and >=24 for big models +subset_size = 1024 +Conf_batch_size_REV2 = 8 +OneCycleLr_MAXLR = 0.01 +OneCycleLr_DEC_A = 0.0005 +OneCycleLr_MINLR = 0.0055 +Use_ES_ONSUBT = False +EarlyStopping_P = 5 +BEST_RSN = 'PAI_model_T' +#VAR +OneCycleLr_CUNLR = OneCycleLr_MAXLR +all_histories = [] +best_acc = 0 +#Funcs +def add_image_grain_TRLRev2(image, intensity = 0.01): + # Generate random noise array + noise = np.random.randint(0, 255, size=image.shape, dtype=np.uint8) + + # Scale the noise array + scaled_noise = (noise * intensity).astype(np.float32) + # Add the noise to the image + noisy_image = cv2.add(image, scaled_noise) + + return noisy_image +def noise_func_TRLRev2(image): + noise_type = np.random.choice(['L1', 'L2', 'L3', 'none']) + new_image = np.copy(image) + + if noise_type == 'L3': + intensityL2 = random.uniform(0.001, 0.016) + intensityL1 = random.uniform(0.005, 0.020) + else: + intensityL2 = random.uniform(0.001, 0.027) + intensityL1 = random.uniform(0.001, 0.028) + + block_size_L1 = random.randint(16, 32) + block_size_L2 = random.randint(32, 64) + + if noise_type == 'L2' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L2): + for j in range(0, image.shape[1], block_size_L2): + block = image[i:i+block_size_L2, j:j+block_size_L2] + block = (np.random.rand() * intensityL2 + 1) * block + new_image[i:i+block_size_L2, j:j+block_size_L2] = block + image = new_image + + if noise_type == 'L1' or noise_type == 'L3': + for i in range(0, image.shape[0], block_size_L1): + for j in range(0, image.shape[1], block_size_L1): + block = image[i:i+block_size_L1, j:j+block_size_L1] + block = (np.random.rand() * intensityL1 + 1) * block + new_image[i:i+block_size_L1, j:j+block_size_L1] = block + + if add_img_grain: + intensity = random.uniform(0, 0.022) # Random intensity + new_image = add_image_grain_TRLRev2(new_image, intensity=intensity) + return new_image +#CONST +train_SUB_datagen = ImageDataGenerator( + horizontal_flip=True, + vertical_flip=True, + rotation_range=179, + zoom_range=0.24, + shear_range=0.22, + width_shift_range=0.21, + brightness_range=(0.88, 1.12), + height_shift_range=0.21, + channel_shift_range=100, + featurewise_center=False, + featurewise_std_normalization=False, + interpolation_order=2, + fill_mode='nearest', + preprocessing_function=noise_func_TRLRev2 + ) +steps_per_epoch_train_SUB = subset_size // Conf_batch_size_REV2 +early_stopping = EarlyStopping(monitor='val_accuracy', patience=EarlyStopping_P, verbose=1, restore_best_weights=True, mode='max') +#MAIN +print('Training the model...') +for epoch in range(1, max_epoch): + # Start Epoch + STG = 'Learning the patterns' if epoch < PL_epoch else 'Fine tuning' + C_subset_epoch = subset_epoch if epoch < PL_epoch else subset_epoch_FT + start_FULL_time = time.time() + print_Color(f'\n~*Epoch: ~*{epoch}~*/~*{max_epoch}~* | ~*[{STG}]', ['normal', 'cyan', 'normal', 'green', 'magenta', 'green'], advanced_mode=True) + print_Color(f'~*Setting model subset epoch.c to ~*[{C_subset_epoch}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + # DP + print_Color('Shuffling data...', ['yellow']) + x_train, y_train = shuffle_data(x_train, y_train) + print_Color(f'~*Taking a subset of ~*[{subset_size}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + subset_indices = np.random.choice(x_train.shape[0], subset_size, replace=False) + x_SUB_train = x_train[subset_indices] + y_SUB_train = y_train[subset_indices] + print_Color('Augmenting data...', ['yellow']) + train_SUB_augmented_images = train_SUB_datagen.flow(x_SUB_train * 255, y_SUB_train, shuffle=False, batch_size=len(x_SUB_train)).next() + x_SUB_train = np.clip(train_SUB_augmented_images[0], 0, 255) / 255 + y_SUB_train = train_SUB_augmented_images[1] + # learning_rate_schedule_SUB + if epoch > PL_epoch and OneCycleLr_CUNLR > OneCycleLr_MINLR: + OneCycleLr_CUNLR -= OneCycleLr_DEC_A + + learning_rate_schedule_SUB = OneCycleLr(max_lr=OneCycleLr_CUNLR, steps_per_epoch=steps_per_epoch_train_SUB, epochs=C_subset_epoch) + print_Color(f'~*Setting model OneCycleLr::maxlr to ~*[{OneCycleLr_CUNLR:.6f}]~*...', ['yellow', 'green', 'yellow'], advanced_mode=True) + # Train + print_Color('Training on subset...', ['green']) + start_SUBO_time = time.time() + SUB_history = model.fit(x_SUB_train, + y_SUB_train, + epochs=C_subset_epoch, + batch_size=Conf_batch_size_REV2, + validation_data=(x_test, y_test), + verbose='auto', + callbacks=[learning_rate_schedule_SUB, early_stopping] if Use_ES_ONSUBT else [learning_rate_schedule_SUB] + ) + end_SUBO_time = time.time() + print_Color('Subset training done.', ['green']) + all_histories.append(SUB_history.history) + # Evaluate the model on the test data + evaluation = model.evaluate(x_test, y_test, verbose=0) + + # Extract the loss and accuracy from the evaluation results + loss = evaluation[0] + acc = evaluation[1] + + # If the accuracy is higher than the best_acc + if acc > best_acc: + print("Improved model accuracy from {} to {}. Saving model.".format(best_acc, acc)) + + # Update the best_acc + best_acc = acc + + # Save the model + if SAVE_TYPE == 'TF': + print('Saving full model tf format...') + model.save(BEST_RSN, save_format='tf') + else: + model.save(f'{BEST_RSN}.h5') + else: + print("Model accuracy did not improve from {}. Not saving model.".format(best_acc)) + # Garbage Collection (memory) + gc.collect() + tf.keras.backend.clear_session() + # Epoch end + end_time = time.time() + epoch_time = end_time - start_FULL_time + print(f"Time taken for epoch(FULL) {epoch}: {epoch_time:.2f} sec") + epoch_SUB_time = end_SUBO_time - start_SUBO_time + print(f"Time taken for epoch(SUBo) {epoch}: {epoch_SUB_time:.2f} sec") + print_Color(f'<---------------------------------------|Epoch [{epoch}] END|--------------------------------------->', ['cyan']) +# End +history = {} +for key in all_histories[0].keys(): + # For each metric, concatenate the values from all histories + history[key] = np.concatenate([h[key] for h in all_histories]) print('Training done.\n') \ No newline at end of file